Electroencephalogram (EEG) Findings in Patients Diagnosed with COVID-19

Electroencephalogram (EEG) Findings in Patients Diagnosed with COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Electroencephalogram (EEG) Findings in Patients Diagnosed with COVID-19 Mohammed Elsayed Helmy, Naglaa Mohamed Ahmed Elkhayat, Ahmed Abd Elmonem Gaber, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7309605/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in The Egyptian Journal of Neurology, Psychiatry and Neurosurgery → Version 1 posted 10 You are reading this latest preprint version Abstract Background COVID-19 is known to affect multiple organ systems, including the brain, leading to neurological complications such as seizures, encephalopathy, and cognitive dysfunction. However, its impact on brain electrical activity remains unclear. Electroencephalography (EEG) is a valuable tool for detecting brain dysfunction, particularly in critically ill patients. Methods A case-control study was conducted involving 95 patients presenting with respiratory symptoms suggestive of recent COVID-19 infection. Participants were classified into two groups: the COVID-19 group (study group) (n = 49), consisting of patients with a confirmed diagnosis of COVID-19 based on RT-PCR testing and chest CT findings, and the non-COVID-19 control group (n = 46), comprising patients with similar clinical symptoms but negative COVID-19 test results. All patients underwent comprehensive neurological examinations and continuous electroencephalogram (EEG) monitoring for six hours at two time points: within 24 hours of hospital admission and following clinical improvement prior to discharge. Results EEG abnormalities were observed in 51.0% of patients in the COVID-19 group, significantly higher than the 28.3% observed in the non-COVID-19 group (p < 0.05). The most frequent EEG findings included generalized slowing, focal slowing, and rhythmic delta activity. Patients exhibiting EEG abnormalities experienced significantly longer hospital stays compared to those with normal EEG results (average 20.4 vs. 10.5 days, p 0.05). Conclusion COVID-19 is strongly associated with EEG abnormalities, particularly diffuse slowing and rhythmic delta activity, which may indicate brain dysfunction. EEG could be a useful tool for monitoring neurological involvement in COVID-19, especially in patients with persistent symptoms. COVID-19 EEG brain function EEG abnormalities neurological complications Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Coronavirus disease 2019 (COVID-19) is primarily known for affecting the respiratory system, but growing research indicates that it can also impact the nervous system, leading to a variety of neurological complications. Many COVID-19 patients have reported symptoms such as headaches, dizziness, cognitive dysfunction, and loss of smell or taste. In more severe cases, seizures, encephalopathy, and even strokes have been observed, particularly in hospitalized patients 1 . One of the most effective ways to study brain activity and detect neurological abnormalities is electroencephalography (EEG). EEG is a non-invasive technique that measures the brain’s electrical activity, helping to identify conditions such as epileptic seizures, encephalopathy, and cerebral dysfunction. While EEG has been widely used to assess brain disorders, its role in understanding COVID-19-related neurological effects is still emerging 2 . Recent studies suggest that many COVID-19 patients show EEG abnormalities, including generalized slowing, rhythmic delta activity, and epileptiform discharges 3 . However, there is still limited data on whether these EEG changes are directly caused by the virus, secondary to hypoxia, inflammation, or metabolic disturbances, or simply a result of severe illness and hospitalization. This study aims to investigate whether EEG abnormalities are more common in COVID-19 patients compared to those with similar respiratory symptoms but without COVID-19. Additionally, it seeks to determine if specific EEG patterns are linked to disease severity. We hypothesize that patients with more severe pulmonary involvement due to COVID-19 are more likely to exhibit EEG abnormalities, reflecting a potential association between respiratory disease severity and cerebral dysfunction, which could help in identifying patients at higher risk for neurological complications. Understanding these relationships could improve early diagnosis, monitoring, and management of neurological symptoms in COVID-19 patients. Subjects and Methods Study Design and Participants This case-control study was conducted at Mataria Teaching Hospital between January 2023 and April 2024. A total of 95 patients presenting with chest symptoms suggestive of recent COVID-19 infection were enrolled. Common presenting symptoms included fever, dyspnea, cough, headache, malaise, anosmia, and generalized body aches. Participants were divided into two groups based on diagnostic findings. The COVID-19 group (n = 49) included patients with confirmed COVID-19-related respiratory illness, diagnosed through two consecutive positive reverse transcription polymerase chain reaction (RT-PCR) tests and chest computed tomography (CT) scans reviewed by a pulmonologist. The non-COVID-19 control group (n = 46) comprised patients with similar respiratory symptoms who were confirmed to have non-COVID-19-related respiratory illness, based on two negative RT-PCR tests and chest CT findings inconsistent with COVID-19. The study was conducted in accordance with the Declaration of Helsinki. It was approved by the Ethics committee of the Faculty of Medicine, Ain Shams University and informed consent was obtained from every patient and control. Inclusion and Exclusion Criteria Eligible participants were adults aged 18 to 80 years, of both sexes, who were fully conscious, oriented, and cooperative at the time of enrollment. Inclusion required recent hospital admission due to respiratory symptoms highly suggestive of recent COVID-19 infection, without any evidence of altered consciousness or overt encephalopathy. Exclusion criteria included a prior diagnosis of epilepsy or seizure disorders, known brain malignancies, and severe metabolic conditions that may affect conscious level such as diabetic ketoacidosis, hepatic failure, or moderate to severe renal impairment (serum creatinine > 1.7 mg/dL). Patients with hypertensive encephalopathy, severe heart failure, major psychiatric disorders, primary headache syndromes, or any other condition likely to interfere with accurate EEG interpretation were also excluded from the study. Clinical and Neurological Assessment All patients underwent comprehensive clinical and neurological evaluations. This included a detailed medical history and general physical examination, followed by a full neurological assessment with particular attention to mental status, behavioral changes, and the presence or history of headaches. A thorough chest examination was conducted by a pulmonologist to evaluate the extent of respiratory involvement. Laboratory investigations were performed to exclude alternative causes of critical illness and included complete blood count (CBC), renal and liver function tests, HbA1c, and serum electrolyte levels. Radiological assessments, including chest computed tomography (CT) and brain magnetic resonance imaging (MRI), were also obtained as part of the diagnostic work-up. COVID-19 Severity Classification Using CO-RADS Scale Chest CT findings were evaluated and classified using the COVID-19 Reporting and Data System (CO-RADS) 4 , a standardized scale for assessing the likelihood of COVID-19 pneumonia based on radiological patterns. The CO-RADS scale categorizes findings as follows: CO-RADS 1–2, indicating a low level of suspicion for COVID-19 pneumonia; CO-RADS 3, reflecting indeterminate findings; and CO-RADS 4–5, suggesting a high level of suspicion based on typical imaging features. For the purposes of this study, patients with CO-RADS scores of 3–5 were considered to have moderate to severe COVID-19 infection, whereas those with scores of 1–2 were classified as having mild or non-COVID-19-related respiratory illness. EEG Recording and Analysis Electroencephalographic (EEG) monitoring was performed for all patients at two predefined time points: the first EEG was conducted within the first 24 hours of hospital admission, and the second EEG was performed following clinical improvement and prior to discharge. EEG acquisition was carried out using a standard 21-electrode setup in accordance with the International Federation of Clinical Neurophysiology (IFCN) guidelines and the international 10–20 electrode placement system 5 . All participants underwent continuous EEG monitoring for six hours using the Encephalan-131-03 EEG system (Medicom MTD, Taganrog, Russia). Recordings were conducted in a controlled EEG laboratory environment with regulated lighting and sound conditions to reduce external artifacts. Electrode impedance was maintained below 5 kΩ. EEG signals were recorded using Encephalan-131-03 EEGViewer version 3, with a sampling rate ranging from 250 to 1000 Hz and appropriate bandpass range (0.5–70 Hz). Each EEG recording was reviewed separately, and critical epochs were analyzed independently by five experienced neurophysiologists who were blinded to clinical and group assignment data to ensure inter-rater reliability. Artifacts were manually identified and excluded from analysis. Any discrepancies in interpretation were resolved through consensus among the reviewing neurophysiologists. Each EEG session consisted of six hours of continuous monitoring. Recordings were conducted using both bipolar and referential montages to enhance spatial resolution and diagnostic accuracy. Artifact removal was performed manually by two experienced epileptologists, who independently reviewed the recordings to identify and eliminate non-physiological signals. This approach ensured high-quality data and minimized the risk of misinterpretation due to external or physiological artifacts. EEG Findings Classification: EEG findings were categorized into four primary types: background abnormalities, including generalized or focal slowing; rhythmic delta activity, either generalized or lateralized; periodic discharges, classified as generalized or focal; and epileptiform abnormalities, encompassing focal or generalized epileptiform discharges. Each EEG recording was independently reviewed by five board-certified epileptologists who were blinded to the patients’ COVID-19 status, ensuring objective interpretation and minimizing diagnostic bias. Statistical Analysis Data analysis was performed using IBM SPSS Statistics, Version 24. Descriptive statistics were expressed as mean ± standard deviation (SD) for normally distributed continuous variables, and as median with interquartile range (IQR) for non-normally distributed variables. Categorical variables were summarized using frequencies and percentages. Comparative analyses were conducted using the chi-square test for categorical data, independent t-tests for normally distributed continuous variables, and the Mann–Whitney U test for non-parametric continuous variables. Spearman’s correlation coefficient was employed to assess the relationship between EEG abnormalities and the severity of respiratory involvement. A p-value < 0.05 was considered indicative of statistical significance. Results Demographic and Clinical Characteristics The study included 49 COVID-19 patients and 46 non-COVID-19 patients. Regarding demographic data no statistically significant differences were found between the two groups. in terms of age (p = 0.061) and sex distribution (p = 0.262)​. Table 1 Comparison of Demographic Data Between COVID-19 and Non-COVID-19 Groups Variable Group 1: COVID-19 (n = 49) Group 2: Non–COVID-19 (n = 46) P-value No. % No. % Sex 0.262 Male 21 42.9 25 54.3 Female 28 57.1 21 45.7 Age 0.061 Range 39–71 25–70 Mean 58.7 55.0 SD 7.5 9.4 Comorbidities Hypertension 12 24.5 10 21.7 0.751 Diabetes Mellitus 5 10.2 5 10.9 1.000 Cardiac Disease 5 10.2 7 15.2 0.525 Renal Disease 6 12.2 8 17.4 0.474 Hepatic Disease 10 20.4 11 23.9 0.682 SD: standard deviation, COVID-19: Coronavirus Disease 2019 The two groups were well-matched regarding age and gender, ensuring that observed differences in EEG findings are not due to demographic variability. EEG Abnormalities in COVID-19 and Non-COVID-19 Patients First and Second EEG Findings EEG abnormalities were significantly more frequent in COVID-19 patients compared to non-COVID-19 patients in both the first and second EEG assessments. Table 2 EEG Abnormalities in First and Second EEG EEG Finding 1st EEG – COVID-19 Group (n = 49) 1st EEG – Non-COVID-19 Group (n = 46) p-value 2nd EEG – COVID-19 Group (n = 49) 2nd EEG – Non-COVID-19 Group (n = 46) p-value Total EEG abnormalities 25 (51.0%) 13 (28.3%) 0.02* 14 (28.6%) 6 (13.0%) 0.05* Generalized background slowing 13 (26.5%) 5 (10.9%) 0.032* 6 (12.2%) 2 (4.3%) 0.386 Focal slowing 9 (18.4%) 4 (8.7%) 0.045* 5 (10.2%) 2 (4.3%) 0.75 Rhythmic Delta Patterns 4 (8.2%) 1 (2.2%) 0.476 3 (6.1%) 0 (0.0%) 0.809 Lateralized Periodic Discharges 6 (12.2%) 2 (4.3%) 0.490 3 (6.1%) 1 (2.2%) 0.540 EEG: Electroencephalogram, COVID-19: Coronavirus Disease 2019 The first EEG demonstrated a significantly higher incidence of abnormalities among COVID-19 patients compared to non-COVID-19 controls (p = 0.02), with a notably greater frequency of generalized background slowing (p = 0.032) and focal slowing (p = 0.045). Persistent EEG abnormalities were observed in 28.6% of patients in the COVID-19 group, significantly higher than the 13.0% observed in the non-COVID-19 group (p = 0.05). Although the overall prevalence of abnormalities decreased in the second EEG recordings, a substantial proportion of COVID-19 patients continued to exhibit persistent EEG changes, suggesting the possibility of ongoing or long-term neurological involvement. EEG Abnormalities and Hospital Stay Duration Among COVID-19 patients, those with EEG abnormalities had significantly longer hospital stays (20.4 ± 4.4 days) compared to patients with normal EEG findings (10.5 ± 4.4 days, p = 0.04). A similar pattern was observed in the non-COVID-19 group, where patients with EEG abnormalities experienced longer hospitalizations (9.6 ± 4.1 days) than those with normal EEGs (6.8 ± 3.2 days, p = 0.048). These findings suggest a potential association between EEG abnormalities and prolonged clinical recovery, regardless of COVID-19 status. Table 3 Hospital Stay (days) Duration and EEG Findings EEG Finding COVID-19 Group (n = 49) Non-COVID-19 Group (n = 46) p-value Abnormal EEG 20.4 ± 4.4 days 9.6 ± 4.1 days 0.04* Normal EEG 10.5 ± 4.4 days 6.8 ± 3.2 days 0.048* EEG: Electroencephalogram, COVID-19: Coronavirus Disease 2019 The presence of EEG abnormalities correlated with longer hospital stays, suggesting that neurological involvement in COVID-19 could contribute to prolonged recovery. Discussion EEG Findings and COVID-19-Related Neurological Changes The present study demonstrated that COVID-19 patients exhibited significantly more EEG abnormalities compared to non-COVID-19 patients, both in the first and second EEG assessments. Generalized background slowing and focal slowing were more common in the COVID-19 group (p = 0.032 and p = 0.045, respectively). Persistent EEG abnormalities were noted in 28.6% of COVID-19 patients, compared to 13.0% of non-COVID-19 patients (p = 0.05)​. These findings align with previous reports indicating that generalized slowing is a predominant EEG abnormality in COVID-19 patients with encephalopathy​. Studies by Vespignani et al. 6 and Frontera et al. 7 observed similar EEG patterns, particularly in critically ill COVID-19 patients. However, other studies, such as those by Liotta et al. 8 and Somani et al. 9 , suggested that background abnormalities were rare and non-specific to COVID-19 infection​. The presence of rhythmic delta activity and lateralized periodic discharges in COVID-19 patients suggests a potential role of systemic inflammation, metabolic disturbances, or direct viral involvement in brain dysfunction​. Previous literature, including studies by Frontera et al. 7 and Paterson et al. 10 , reported rhythmic delta activity in 10–15% of COVID-19 patients with encephalopathy​. Correlation Between CO-RADS Scale and EEG Changes Interestingly, this study did not find a statistically significant correlation between COVID-19 severity (as assessed by the CO-RADS scale: COVID-19 Reporting and Data System ) and EEG abnormalities (p > 0.05)​. This contrasts with findings from Hwang et al. 11 , who demonstrated a strong correlation between EEG findings and the severity of disease by CO-RADS scale. Despite the lack of a significant correlation, the higher frequency of EEG abnormalities in severe COVID-19 cases suggests that other mechanisms, such as inflammatory cytokine release and microvascular dysfunction, may play a role in neurological involvement 12 . EEG Abnormalities and Prolonged Hospital Stay Patients with abnormal EEG findings had significantly longer hospital stays compared to those with normal EEGs (COVID-19: 20.4 ± 4.4 days vs. 10.5 ± 4.4 days, p = 0.04; non-COVID-19: 9.6 ± 4.1 days vs. 6.8 ± 3.2 days, p = 0.048)​. These results are consistent with studies by Seibert et al. 13 and Lin et al. 14 , which found a strong association between EEG abnormalities and prolonged hospitalization in COVID-19 patients​. Study Limitations This study faced several limitations that should be considered when interpreting the results. First, the relatively small sample size may limit the generalizability of the findings to broader populations. Second, as a single-center study, there is a potential for selection bias, which may affect the external validity of the results. Third, the follow-up period was restricted to the duration of hospitalization, and long-term outcomes—including the persistence or resolution of EEG abnormalities after discharge—were not assessed. Future multicenter studies with larger cohorts and extended follow-up are warranted to validate and expand upon these findings. Conclusion This study highlights the significant association between COVID-19 and EEG abnormalities, particularly generalized background slowing, focal slowing, and periodic discharges. The higher persistence of EEG abnormalities in COVID-19 patients suggests potential long-term neurological effects of the disease​. Moreover, the correlation between EEG abnormalities and prolonged hospital stays emphasizes the importance of neurological evaluation in COVID-19 patients with altered mental status or cognitive dysfunction. Despite the lack of a direct correlation between CO-RADS severity and EEG findings, the increased prevalence of EEG abnormalities in COVID-19 patients suggests that neurological dysfunction may be influenced by inflammatory, metabolic, or microvascular changes rather than direct pulmonary involvement. Declarations Ethics approval and consent to participate All procedures performed in the study were in accordance with the ethical standards of the faculty of medicine, Ain Shams university research and ethical committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We obtained approval from research ethics committee no. FWA 000017585. On 10/8/2022 (Approval Number: FMASU MD 180/2022). Consent to participate : Written informed consent was obtained from participants for participation. We obtained approval from research ethics committee no. FWA 000017585. On 10/8/2022 (Approval Number: FMASU MD 180/2022). Consent for publication : Not applicable Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non profit sectors. Competing interests: All authors declare that they have no competing interests Availability of Data and Material: All raw data will be available on the editor request through communication with the corresponding author. Author contribution statement: Mohamed Elsayed Helmy : Study design, data analysis, manuscript drafting. Naglaa Mohamed Ahmed Elkhayat : Supervision, methodology. Ahmed Abd Elmonem Gaber , Yousry Aboelnaga Abdelhamid : Data collection, interpretation. Ahmed Mohamed Hazzou , Mona Mokhtar Wahid El Din : EEG analysis, manuscript revision. All authors have agreed to conditions noted on the Authorship Agreement Form and have read and approved the final version submitted. The content of the manuscript has not been published, or submitted for publication elsewhere Acknowledgement: not applicable References Ellul MA, Benjamin L, Singh B, Lant S, Michael BD, Easton A, et al. Neurological associations of COVID-19. Lancet Neurol. 2020;19(9):767–83. Kubota T, Kuroda N. Exacerbation of neurological symptoms and COVID-19 severity in patients with preexisting neurological disorders and COVID-19: a systematic review. Clin Neurol Neurosurg. 2021;200:106349. Pellinen J, Carroll E, Friedman D, Boffa M, Dugan P, Friedman DE, et al. Continuous EEG findings in patients with COVID-19 infection admitted to a New York academic hospital system. Epilepsia. 2020;61(10):2097–105. Prokop M, Van Everdingen W, van Rees Vellinga T, Quarles van Ufford H, Stöger L, Beenen L, et al. CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19—definition and evaluation. Radiology. 2020;296(2):E97–104. Klem GH, Lüders HO, Jasper HH, Elger C; International Federation of Clinical Neurophysiology. The ten‑twenty electrode system of the International Federation. International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 1999;52:3–6. Vespignani H, Pilotto A, Helms J, et al. EEG abnormalities in COVID-19 patients: A review. Front Neurol. 2022;13:898626. Frontera JA, Sabadia S, Yang D, de Havenon A, Yaghi S, Lewis A, et al. Persistent EEG changes in COVID-19 survivors: A longitudinal study. Ann Neurol. 2022;91(4):567–78. Liotta EM, Batra A, Clark JR, Shlobin NA, Hoffman SC, Orban ZS, et al. Transient EEG abnormalities in COVID-19 patients: A single-center study. Neurocrit Care. 2022;36(2):345–52. Somani S, Pati S, Gaston T, Uysal U. Non-specific EEG changes in COVID-19: A clinical perspective. Neurology. 2023;100(10):1023–32. Paterson RW, Brown RL, Benjamin L, Nortley R, Wiethoff S, Bharucha T, et al. Generalized rhythmic delta activity in COVID-19 encephalitis: A case series. J Neuroinflammation. 2023;20(1):101–23. Hwang J, Ellul MA, García-Azorín D, et al. EEG abnormalities and their radiographic correlates in a COVID-19 inpatient cohort. Neurology. 2022;98(12):e1215–22. Xu Z, Wang H, Jiang S, Teng J, Zhou D, Chen Z, et al. Brain pathology in COVID-19: clinical manifestations and potential mechanisms. Neurosci Bull. 2024;40(3):383–400. Seibert DJ, Patrie JT, Muehlschlegel S, Lee JW. Electroencephalographic findings and their prognostic value in critically ill COVID-19 patients. Clin Neurophysiol. 2021;132(9):2405–13. Lin L, Alabdulgader AA, Lee A, Siddiqui S, Sutter R, Schomer DL. Clinical significance of electroencephalography abnormalities in hospitalized COVID-19 patients: A retrospective cohort study. J Clin Neurophysiol. 2022;39(3):220–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in The Egyptian Journal of Neurology, Psychiatry and Neurosurgery → Version 1 posted Editorial decision: Revision requested 18 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 06 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7309605","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501380831,"identity":"17c09e0f-e168-4c6c-8712-f1cbea67c94c","order_by":0,"name":"Mohammed Elsayed 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1","display":"","copyAsset":false,"role":"figure","size":73965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBilateral Focal Slowing EEG changes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA case of Female patient 50 years old with history of Diabetes Mellitus and Hypertension with confirmed recent Covid-19 infection, her CT chest showed CORADS scale = 5, her first EEG showing Focal Slowing (Bilateral Temporo-parietal slowing theta range).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/5484490dcba7e6cd29a152a4.jpg"},{"id":89673231,"identity":"79462e6d-20dd-4568-a6f8-723044602982","added_by":"auto","created_at":"2025-08-22 13:12:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLeft side Focal Slowing EEG changes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale patient 79 years old with history of Hypertension and confirmed recent Covid-19 infection, her CT chest showed CORADS scale = 4, her first EEG showing Left side Focal Slowing (Delta range).\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/c81359f13b5920e413439bcb.jpg"},{"id":89674892,"identity":"9e0b0475-51c9-469a-b657-17f7c563b900","added_by":"auto","created_at":"2025-08-22 13:28:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Slowing EEG changes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale patient 38 years old with confirmed recent Covid-19 infection, her CT chest showed CORADS scale = 5, her second EEG showing Generalized Slowing.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/e83a019f4a81e8495c2cedbc.jpg"},{"id":89675406,"identity":"5a30a059-1bb2-4ba2-9c34-fc29e71091a4","added_by":"auto","created_at":"2025-08-22 13:36:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Rhythmic Delta Activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale patient 48 years old with history of Hypertension and confirmed recent Covid-19 infection, her CT chest showed CORADS scale = 4, her second EEG showing Generalized Rhythmic Delta Activity.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/12fb5c7d4e0fdff84ed7f265.jpg"},{"id":89673739,"identity":"e60dde49-1bf0-4dfa-a362-0ae9afa7278e","added_by":"auto","created_at":"2025-08-22 13:20:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":69788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Epileptiform Activity-1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale patient 39 years old with medical history of cardiac disease and confirmed recent Covid-19 infection, his CT chest showed CORADS scale = 5, his first EEG showing Generalized Paroxysmal Epileptiform Activity.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/bd10bc8f7ef18575c8a99fdd.jpg"},{"id":89673248,"identity":"4ca73313-29af-4b29-a7d9-e72fb22df2e7","added_by":"auto","created_at":"2025-08-22 13:12:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":67581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Epileptiform Activity-2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale patient 44 years old with medical history of Hypertention and confirmed recent Covid-19 infection, her CT chest showed CORADS scale = 5, her first EEG showing Generalized Paroxysmal Epileptiform Activity.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/859b84a5f9befc40c9bbbe68.jpg"},{"id":104250912,"identity":"34208ed9-3c69-4110-aef3-3997241eb878","added_by":"auto","created_at":"2026-03-09 16:11:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1572092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7309605/v1/498faae4-165c-4717-8d8d-074de3b38341.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electroencephalogram (EEG) Findings in Patients Diagnosed with COVID-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) is primarily known for affecting the respiratory system, but growing research indicates that it can also impact the nervous system, leading to a variety of neurological complications. Many COVID-19 patients have reported symptoms such as headaches, dizziness, cognitive dysfunction, and loss of smell or taste. In more severe cases, seizures, encephalopathy, and even strokes have been observed, particularly in hospitalized patients \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne of the most effective ways to study brain activity and detect neurological abnormalities is electroencephalography (EEG). EEG is a non-invasive technique that measures the brain\u0026rsquo;s electrical activity, helping to identify conditions such as epileptic seizures, encephalopathy, and cerebral dysfunction. While EEG has been widely used to assess brain disorders, its role in understanding COVID-19-related neurological effects is still emerging \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies suggest that many COVID-19 patients show EEG abnormalities, including generalized slowing, rhythmic delta activity, and epileptiform discharges\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, there is still limited data on whether these EEG changes are directly caused by the virus, secondary to hypoxia, inflammation, or metabolic disturbances, or simply a result of severe illness and hospitalization.\u003c/p\u003e\u003cp\u003eThis study aims to investigate whether EEG abnormalities are more common in COVID-19 patients compared to those with similar respiratory symptoms but without COVID-19. Additionally, it seeks to determine if specific EEG patterns are linked to disease severity. We hypothesize that patients with more severe pulmonary involvement due to COVID-19 are more likely to exhibit EEG abnormalities, reflecting a potential association between respiratory disease severity and cerebral dysfunction, which could help in identifying patients at higher risk for neurological complications. Understanding these relationships could improve early diagnosis, monitoring, and management of neurological symptoms in COVID-19 patients.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis case-control study was conducted at Mataria Teaching Hospital between January 2023 and April 2024. A total of 95 patients presenting with chest symptoms suggestive of recent COVID-19 infection were enrolled. Common presenting symptoms included fever, dyspnea, cough, headache, malaise, anosmia, and generalized body aches. Participants were divided into two groups based on diagnostic findings. The COVID-19 group (n\u0026thinsp;=\u0026thinsp;49) included patients with confirmed COVID-19-related respiratory illness, diagnosed through two consecutive positive reverse transcription polymerase chain reaction (RT-PCR) tests and chest computed tomography (CT) scans reviewed by a pulmonologist. The non-COVID-19 control group (n\u0026thinsp;=\u0026thinsp;46) comprised patients with similar respiratory symptoms who were confirmed to have non-COVID-19-related respiratory illness, based on two negative RT-PCR tests and chest CT findings inconsistent with COVID-19.\u003c/p\u003e\u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki. It was approved by the Ethics committee of the Faculty of Medicine, Ain Shams University and informed consent was obtained from every patient and control.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eEligible participants were adults aged 18 to 80 years, of both sexes, who were fully conscious, oriented, and cooperative at the time of enrollment. Inclusion required recent hospital admission due to respiratory symptoms highly suggestive of recent COVID-19 infection, without any evidence of altered consciousness or overt encephalopathy.\u003c/p\u003e\u003cp\u003eExclusion criteria included a prior diagnosis of epilepsy or seizure disorders, known brain malignancies, and severe metabolic conditions that may affect conscious level such as diabetic ketoacidosis, hepatic failure, or moderate to severe renal impairment (serum creatinine\u0026thinsp;\u0026gt;\u0026thinsp;1.7 mg/dL). Patients with hypertensive encephalopathy, severe heart failure, major psychiatric disorders, primary headache syndromes, or any other condition likely to interfere with accurate EEG interpretation were also excluded from the study.\u003c/p\u003e\n\u003ch3\u003eClinical and Neurological Assessment\u003c/h3\u003e\n\u003cp\u003eAll patients underwent comprehensive clinical and neurological evaluations. This included a detailed medical history and general physical examination, followed by a full neurological assessment with particular attention to mental status, behavioral changes, and the presence or history of headaches. A thorough chest examination was conducted by a pulmonologist to evaluate the extent of respiratory involvement. Laboratory investigations were performed to exclude alternative causes of critical illness and included complete blood count (CBC), renal and liver function tests, HbA1c, and serum electrolyte levels. Radiological assessments, including chest computed tomography (CT) and brain magnetic resonance imaging (MRI), were also obtained as part of the diagnostic work-up.\u003c/p\u003e\n\u003ch3\u003eCOVID-19 Severity Classification Using CO-RADS Scale\u003c/h3\u003e\n\u003cp\u003eChest CT findings were evaluated and classified using the COVID-19 Reporting and Data System (CO-RADS)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, a standardized scale for assessing the likelihood of COVID-19 pneumonia based on radiological patterns. The CO-RADS scale categorizes findings as follows: CO-RADS 1\u0026ndash;2, indicating a low level of suspicion for COVID-19 pneumonia; CO-RADS 3, reflecting indeterminate findings; and CO-RADS 4\u0026ndash;5, suggesting a high level of suspicion based on typical imaging features. For the purposes of this study, patients with CO-RADS scores of 3\u0026ndash;5 were considered to have moderate to severe COVID-19 infection, whereas those with scores of 1\u0026ndash;2 were classified as having mild or non-COVID-19-related respiratory illness.\u003c/p\u003e\n\u003ch3\u003eEEG Recording and Analysis\u003c/h3\u003e\n\u003cp\u003eElectroencephalographic (EEG) monitoring was performed for all patients at two predefined time points: the first EEG was conducted within the first 24 hours of hospital admission, and the second EEG was performed following clinical improvement and prior to discharge. EEG acquisition was carried out using a standard 21-electrode setup in accordance with the International Federation of Clinical Neurophysiology (IFCN) guidelines and the international 10\u0026ndash;20 electrode placement system\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAll participants underwent continuous EEG monitoring for six hours using the Encephalan-131-03 EEG system (Medicom MTD, Taganrog, Russia). Recordings were conducted in a controlled EEG laboratory environment with regulated lighting and sound conditions to reduce external artifacts. Electrode impedance was maintained below 5 kΩ. EEG signals were recorded using Encephalan-131-03 EEGViewer version 3, with a sampling rate ranging from 250 to 1000 Hz and appropriate bandpass range (0.5\u0026ndash;70 Hz).\u003c/p\u003e\u003cp\u003eEach EEG recording was reviewed separately, and critical epochs were analyzed independently by five experienced neurophysiologists who were blinded to clinical and group assignment data to ensure inter-rater reliability. Artifacts were manually identified and excluded from analysis. Any discrepancies in interpretation were resolved through consensus among the reviewing neurophysiologists.\u003c/p\u003e\u003cp\u003eEach EEG session consisted of six hours of continuous monitoring. Recordings were conducted using both bipolar and referential montages to enhance spatial resolution and diagnostic accuracy. Artifact removal was performed manually by two experienced epileptologists, who independently reviewed the recordings to identify and eliminate non-physiological signals. This approach ensured high-quality data and minimized the risk of misinterpretation due to external or physiological artifacts.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEEG Findings Classification:\u003c/h2\u003e\u003cp\u003eEEG findings were categorized into four primary types: background abnormalities, including generalized or focal slowing; rhythmic delta activity, either generalized or lateralized; periodic discharges, classified as generalized or focal; and epileptiform abnormalities, encompassing focal or generalized epileptiform discharges. Each EEG recording was independently reviewed by five board-certified epileptologists who were blinded to the patients\u0026rsquo; COVID-19 status, ensuring objective interpretation and minimizing diagnostic bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using IBM SPSS Statistics, Version 24. Descriptive statistics were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed continuous variables, and as median with interquartile range (IQR) for non-normally distributed variables. Categorical variables were summarized using frequencies and percentages.\u003c/p\u003e\u003cp\u003eComparative analyses were conducted using the chi-square test for categorical data, independent t-tests for normally distributed continuous variables, and the Mann\u0026ndash;Whitney U test for non-parametric continuous variables. Spearman\u0026rsquo;s correlation coefficient was employed to assess the relationship between EEG abnormalities and the severity of respiratory involvement. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eThe study included 49 COVID-19 patients and 46 non-COVID-19 patients. Regarding demographic data no statistically significant differences were found between the two groups. in terms of age (p\u0026thinsp;=\u0026thinsp;0.061) and sex distribution (p\u0026thinsp;=\u0026thinsp;0.262)​.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Demographic Data Between COVID-19 and Non-COVID-19 Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup 1: COVID-19 (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroup 2: Non\u0026ndash;COVID-19 (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u0026ndash;71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiac Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD: standard deviation, COVID-19: Coronavirus Disease 2019\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe two groups were well-matched regarding age and gender, ensuring that observed differences in EEG findings are not due to demographic variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEEG Abnormalities in COVID-19 and Non-COVID-19 Patients\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eFirst and Second EEG Findings\u003c/h2\u003e\u003cp\u003eEEG abnormalities were significantly more frequent in COVID-19 patients compared to non-COVID-19 patients in both the first and second EEG assessments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEEG Abnormalities in First and Second EEG\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEEG Finding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1st EEG \u0026ndash; COVID-19 Group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1st EEG \u0026ndash; Non-COVID-19 Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2nd EEG \u0026ndash; COVID-19 Group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2nd EEG \u0026ndash; Non-COVID-19 Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal EEG abnormalities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (51.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.02*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.05*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGeneralized background slowing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFocal slowing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.045*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRhythmic Delta Patterns\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLateralized Periodic Discharges\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eEEG: Electroencephalogram, COVID-19: Coronavirus Disease 2019\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe first EEG demonstrated a significantly higher incidence of abnormalities among COVID-19 patients compared to non-COVID-19 controls (p\u0026thinsp;=\u0026thinsp;0.02), with a notably greater frequency of generalized background slowing (p\u0026thinsp;=\u0026thinsp;0.032) and focal slowing (p\u0026thinsp;=\u0026thinsp;0.045). Persistent EEG abnormalities were observed in 28.6% of patients in the COVID-19 group, significantly higher than the 13.0% observed in the non-COVID-19 group (p\u0026thinsp;=\u0026thinsp;0.05). Although the overall prevalence of abnormalities decreased in the second EEG recordings, a substantial proportion of COVID-19 patients continued to exhibit persistent EEG changes, suggesting the possibility of ongoing or long-term neurological involvement.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEEG Abnormalities and Hospital Stay Duration\u003c/h2\u003e\u003cp\u003eAmong COVID-19 patients, those with EEG abnormalities had significantly longer hospital stays (20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days) compared to patients with normal EEG findings (10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days, p\u0026thinsp;=\u0026thinsp;0.04). A similar pattern was observed in the non-COVID-19 group, where patients with EEG abnormalities experienced longer hospitalizations (9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 days) than those with normal EEGs (6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 days, p\u0026thinsp;=\u0026thinsp;0.048). These findings suggest a potential association between EEG abnormalities and prolonged clinical recovery, regardless of COVID-19 status.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHospital Stay (days) Duration and EEG Findings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEEG Finding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOVID-19 Group (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-COVID-19 Group (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbnormal EEG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.04*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNormal EEG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.048*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eEEG: Electroencephalogram, COVID-19: Coronavirus Disease 2019\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe presence of EEG abnormalities correlated with longer hospital stays, suggesting that neurological involvement in COVID-19 could contribute to prolonged recovery.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eEEG Findings and COVID-19-Related Neurological Changes\u003c/h2\u003e\u003cp\u003eThe present study demonstrated that COVID-19 patients exhibited significantly more EEG abnormalities compared to non-COVID-19 patients, both in the first and second EEG assessments. Generalized background slowing and focal slowing were more common in the COVID-19 group (p\u0026thinsp;=\u0026thinsp;0.032 and p\u0026thinsp;=\u0026thinsp;0.045, respectively). Persistent EEG abnormalities were noted in 28.6% of COVID-19 patients, compared to 13.0% of non-COVID-19 patients (p\u0026thinsp;=\u0026thinsp;0.05)​.\u003c/p\u003e\u003cp\u003eThese findings align with previous reports indicating that generalized slowing is a predominant EEG abnormality in COVID-19 patients with encephalopathy​. Studies by Vespignani et al.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and Frontera et al.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e observed similar EEG patterns, particularly in critically ill COVID-19 patients. However, other studies, such as those by Liotta et al.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and Somani et al.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, suggested that background abnormalities were rare and non-specific to COVID-19 infection​.\u003c/p\u003e\u003cp\u003eThe presence of rhythmic delta activity and lateralized periodic discharges in COVID-19 patients suggests a potential role of systemic inflammation, metabolic disturbances, or direct viral involvement in brain dysfunction​. Previous literature, including studies by Frontera et al.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and Paterson et al.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, reported rhythmic delta activity in 10\u0026ndash;15% of COVID-19 patients with encephalopathy​.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Between CO-RADS Scale and EEG Changes\u003c/h2\u003e\u003cp\u003eInterestingly, this study did not find a statistically significant correlation between COVID-19 severity (as assessed by the CO-RADS scale: COVID-19 Reporting and Data System ) and EEG abnormalities (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)​. This contrasts with findings from Hwang et al.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, who demonstrated a strong correlation between EEG findings and the severity of disease by CO-RADS scale.\u003c/p\u003e\u003cp\u003eDespite the lack of a significant correlation, the higher frequency of EEG abnormalities in severe COVID-19 cases suggests that other mechanisms, such as inflammatory cytokine release and microvascular dysfunction, may play a role in neurological involvement\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEEG Abnormalities and Prolonged Hospital Stay\u003c/h2\u003e\u003cp\u003ePatients with abnormal EEG findings had significantly longer hospital stays compared to those with normal EEGs (COVID-19: 20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days vs. 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 days, p\u0026thinsp;=\u0026thinsp;0.04; non-COVID-19: 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 days vs. 6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 days, p\u0026thinsp;=\u0026thinsp;0.048)​. These results are consistent with studies by Seibert et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and Lin et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, which found a strong association between EEG abnormalities and prolonged hospitalization in COVID-19 patients​.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations\u003c/h2\u003e\u003cp\u003eThis study faced several limitations that should be considered when interpreting the results. First, the relatively small sample size may limit the generalizability of the findings to broader populations. Second, as a single-center study, there is a potential for selection bias, which may affect the external validity of the results. Third, the follow-up period was restricted to the duration of hospitalization, and long-term outcomes\u0026mdash;including the persistence or resolution of EEG abnormalities after discharge\u0026mdash;were not assessed. Future multicenter studies with larger cohorts and extended follow-up are warranted to validate and expand upon these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the significant association between COVID-19 and EEG abnormalities, particularly generalized background slowing, focal slowing, and periodic discharges. The higher persistence of EEG abnormalities in COVID-19 patients suggests potential long-term neurological effects of the disease​.\u003c/p\u003e\u003cp\u003eMoreover, the correlation between EEG abnormalities and prolonged hospital stays emphasizes the importance of neurological evaluation in COVID-19 patients with altered mental status or cognitive dysfunction. Despite the lack of a direct correlation between CO-RADS severity and EEG findings, the increased prevalence of EEG abnormalities in COVID-19 patients suggests that neurological dysfunction may be influenced by inflammatory, metabolic, or microvascular changes rather than direct pulmonary involvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in the study were in accordance with the ethical standards of the faculty of medicine, Ain Shams university research and ethical committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We obtained approval from research ethics committee no. FWA 000017585. On 10/8/2022\u0026nbsp;(Approval Number: FMASU MD 180/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from participants for participation. We obtained approval from research ethics committee no. FWA 000017585. On 10/8/2022\u0026nbsp;(Approval Number: FMASU MD 180/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data will be available on the editor request through communication with the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohamed Elsayed Helmy\u003c/strong\u003e: Study design, data analysis, manuscript drafting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNaglaa Mohamed Ahmed Elkhayat\u003c/strong\u003e: Supervision, methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAhmed Abd Elmonem Gaber\u003c/strong\u003e, \u003cstrong\u003eYousry Aboelnaga Abdelhamid\u003c/strong\u003e: Data collection, interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAhmed Mohamed Hazzou\u003c/strong\u003e, \u003cstrong\u003eMona Mokhtar Wahid El Din\u003c/strong\u003e: EEG analysis, manuscript revision.\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to conditions noted on the Authorship Agreement Form and have read and approved the final version submitted.\u003c/p\u003e\n\u003cp\u003eThe content of the manuscript has not been published, or submitted for publication elsewhere\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEllul MA, Benjamin L, Singh B, Lant S, Michael BD, Easton A, et al. Neurological associations of COVID-19. Lancet Neurol. 2020;19(9):767\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eKubota T, Kuroda N. Exacerbation of neurological symptoms and COVID-19 severity in patients with preexisting neurological disorders and COVID-19: a systematic review. Clin Neurol Neurosurg. 2021;200:106349.\u003c/li\u003e\n\u003cli\u003ePellinen J, Carroll E, Friedman D, Boffa M, Dugan P, Friedman DE, et al. Continuous EEG findings in patients with COVID-19 infection admitted to a New York academic hospital system. Epilepsia. 2020;61(10):2097\u0026ndash;105.\u003c/li\u003e\n\u003cli\u003eProkop M, Van Everdingen W, van Rees Vellinga T, Quarles van Ufford H, St\u0026ouml;ger L, Beenen L, et al. CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19\u0026mdash;definition and evaluation. Radiology. 2020;296(2):E97\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eKlem GH, L\u0026uuml;ders HO, Jasper HH, Elger C; International Federation of Clinical Neurophysiology. The ten‑twenty electrode system of the International Federation. International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 1999;52:3\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eVespignani H, Pilotto A, Helms J, et al. EEG abnormalities in COVID-19 patients: A review. Front Neurol. 2022;13:898626.\u003c/li\u003e\n\u003cli\u003eFrontera JA, Sabadia S, Yang D, de Havenon A, Yaghi S, Lewis A, et al. Persistent EEG changes in COVID-19 survivors: A longitudinal study. Ann Neurol. 2022;91(4):567\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eLiotta EM, Batra A, Clark JR, Shlobin NA, Hoffman SC, Orban ZS, et al. Transient EEG abnormalities in COVID-19 patients: A single-center study. Neurocrit Care. 2022;36(2):345\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eSomani S, Pati S, Gaston T, Uysal U. Non-specific EEG changes in COVID-19: A clinical perspective. Neurology. 2023;100(10):1023\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003ePaterson RW, Brown RL, Benjamin L, Nortley R, Wiethoff S, Bharucha T, et al. Generalized rhythmic delta activity in COVID-19 encephalitis: A case series. J Neuroinflammation. 2023;20(1):101\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eHwang J, Ellul MA, Garc\u0026iacute;a-Azor\u0026iacute;n D, et al. EEG abnormalities and their radiographic correlates in a COVID-19 inpatient cohort. Neurology. 2022;98(12):e1215\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eXu Z, Wang H, Jiang S, Teng J, Zhou D, Chen Z, et al. Brain pathology in COVID-19: clinical manifestations and potential mechanisms. Neurosci Bull. 2024;40(3):383\u0026ndash;400.\u003c/li\u003e\n\u003cli\u003eSeibert DJ, Patrie JT, Muehlschlegel S, Lee JW. Electroencephalographic findings and their prognostic value in critically ill COVID-19 patients. Clin Neurophysiol. 2021;132(9):2405\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eLin L, Alabdulgader AA, Lee A, Siddiqui S, Sutter R, Schomer DL. Clinical significance of electroencephalography abnormalities in hospitalized COVID-19 patients: A retrospective cohort study. J Clin Neurophysiol. 2022;39(3):220\u0026ndash;7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-neurology-psychiatry-and-neurosurgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejnp","sideBox":"Learn more about [The Egyptian Journal of Neurology, Psychiatry and Neurosurgery](http://ejnpn.springeropen.com)","snPcode":"41983","submissionUrl":"https://submission.springernature.com/new-submission/41983/3","title":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, EEG, brain function, EEG abnormalities, neurological complications","lastPublishedDoi":"10.21203/rs.3.rs-7309605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7309605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCOVID-19 is known to affect multiple organ systems, including the brain, leading to neurological complications such as seizures, encephalopathy, and cognitive dysfunction. However, its impact on brain electrical activity remains unclear. Electroencephalography (EEG) is a valuable tool for detecting brain dysfunction, particularly in critically ill patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA case-control study was conducted involving 95 patients presenting with respiratory symptoms suggestive of recent COVID-19 infection. Participants were classified into two groups: the COVID-19 group (study group) (n\u0026thinsp;=\u0026thinsp;49), consisting of patients with a confirmed diagnosis of COVID-19 based on RT-PCR testing and chest CT findings, and the non-COVID-19 control group (n\u0026thinsp;=\u0026thinsp;46), comprising patients with similar clinical symptoms but negative COVID-19 test results. All patients underwent comprehensive neurological examinations and continuous electroencephalogram (EEG) monitoring for six hours at two time points: within 24 hours of hospital admission and following clinical improvement prior to discharge.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEEG abnormalities were observed in 51.0% of patients in the COVID-19 group, significantly higher than the 28.3% observed in the non-COVID-19 group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The most frequent EEG findings included generalized slowing, focal slowing, and rhythmic delta activity. Patients exhibiting EEG abnormalities experienced significantly longer hospital stays compared to those with normal EEG results (average 20.4 vs. 10.5 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant association was found between EEG abnormalities and the severity of pulmonary involvement (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCOVID-19 is strongly associated with EEG abnormalities, particularly diffuse slowing and rhythmic delta activity, which may indicate brain dysfunction. EEG could be a useful tool for monitoring neurological involvement in COVID-19, especially in patients with persistent symptoms.\u003c/p\u003e","manuscriptTitle":"Electroencephalogram (EEG) Findings in Patients Diagnosed with COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 13:12:50","doi":"10.21203/rs.3.rs-7309605/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-18T18:41:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T06:34:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3853009844642060002765633869884487042","date":"2025-10-08T12:28:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T11:39:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92872296266178332182797470325070013797","date":"2025-09-29T06:02:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76062472825666342765753110811081184291","date":"2025-08-17T01:31:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T09:09:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T05:04:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T05:03:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","date":"2025-08-06T12:03:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-neurology-psychiatry-and-neurosurgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejnp","sideBox":"Learn more about [The Egyptian Journal of Neurology, Psychiatry and Neurosurgery](http://ejnpn.springeropen.com)","snPcode":"41983","submissionUrl":"https://submission.springernature.com/new-submission/41983/3","title":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7a1d53a3-0188-47ea-a72d-0cb81e0941aa","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:06:23+00:00","versionOfRecord":{"articleIdentity":"rs-7309605","link":"https://doi.org/10.1186/s41983-026-01120-5","journal":{"identity":"the-egyptian-journal-of-neurology-psychiatry-and-neurosurgery","isVorOnly":false,"title":"The Egyptian Journal of Neurology, Psychiatry and Neurosurgery"},"publishedOn":"2026-03-03 15:59:09","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-08-22 13:12:50","video":"","vorDoi":"10.1186/s41983-026-01120-5","vorDoiUrl":"https://doi.org/10.1186/s41983-026-01120-5","workflowStages":[]},"version":"v1","identity":"rs-7309605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7309605","identity":"rs-7309605","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}