Enhancing Wearable Cardioverter Defibrillator Performance: Comparative Validation of a Revised Algorithm to Reduce False Positive Alarms

Enhancing Wearable Cardioverter Defibrillator Performance: Comparative Validation of a Revised Algorithm to Reduce False Positive Alarms | 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 Enhancing Wearable Cardioverter Defibrillator Performance: Comparative Validation of a Revised Algorithm to Reduce False Positive Alarms David Finch, Jaeho Kim, Maurie Wiswell, Karl Hibler, Pamela Breske This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9453768/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The ASSURE® wearable cardioverter defibrillator (WCD) is designed to protect patients at risk of sudden cardiac death while minimizing false positive shock alarms (FPAs), which can undermine confidence and reduce adherence. Although the original ASSURE Detection Algorithm (ADA) demonstrated robust performance, post-market experience identified opportunities to further reduce FPAs without compromising ventricular tachycardia/fibrillation (VT/VF) sensitivity. Objective: To develop and validate a revised ADA that reduces FPAs while preserving VT/VF sensitivity, using stored ECG episode data obtained from ASSURE WCD patients. Methods: Algorithm refinements focused on improved supraventricular tachycardia discrimination, enhanced noise management, and more accurate QRS identification. Development incorporated a high-fidelity virtual model for rapid iteration, and validation was performed using a physical WCD device model. Comparative validation relied on the measurement reliability of the WCD models to enable controlled evaluation of arrhythmia classification performance. Sensitivity, and per-episode and per-patient FPA rates as measures of specificity, were assessed using a real-world ECG dataset and corroborated with datasets used for original algorithm testing. Results: The revised algorithm detected 100% of shockable VT/VF rhythms (137 episodes) in the Field Episode test dataset. In > 8,000 non-VT/VF Field Episodes, the revised ADA reduced per-patient total FPAs by 51.9% (95% CI 43.8–60.0) and sustained FPAs by 69.4% (59.2–78.3). Testing with legacy datasets confirmed continued compliance with standard performance criteria. Conclusion Using a controlled comparative validation methodology, the revised ADA demonstrated substantial reductions in FPAs while maintaining high VT/VF sensitivity, supporting improved patient experience, adherence, and reduced exposure to potential inappropriate therapy. Biomedical Engineering wearable cardioverter defibrillator (WCD) detection algorithm false positive alarms signal quality comparative validation Figures Figure 1 Figure 2 Introduction The ASSURE® wearable cardioverter defibrillator (WCD) is designed to protect patients at risk of sudden cardiac death while optimizing comfort and minimizing false positive shock alarms (FPAs). Near continuous wear is essential for timely detection and treatment of life-threatening ventricular arrhythmias; however, prior studies of other FDA approved WCDs have identified adherence challenges often linked to discomfort and frequent FPAs [ 1 – 3 ]. Central to ASSURE system performance is the ASSURE Detection Algorithm (ADA), engineered to maintain high sensitivity in detecting ventricular arrhythmias with sufficient specificity to keep FPAs low. FPAs are clinically important because they reflect rhythm misclassification that can lead to inappropriate therapy; even when nonsustained or diverted, FPAs can provoke anxiety and diminish patient quality of life. In bench testing and an early human trial, ADA performance was robust [1 FPA per 1,333 patient days with no missed episodes] [ 4 ], supporting Food and Drug Administration Pre-Market Approval in July 2021. Post market surveillance further corroborated these findings. In the ASSURE WCD Clinical Evaluation – Post Approval Study (ACE-PAS, NCT05135403), FPAs occurred in only 6% of patients [ 5 ], comparing favorably to other WCD systems that reported FPAs in 46% [ 3 ] and 38% [ 2 ] of patients. Despite this superior performance, analysis of residual FPAs revealed opportunities to further increase specificity and minimize inappropriate shock risk without compromising VT/VF sensitivity. The intended clinical benefit is improved patient safety, experience, and adherence, while reinforcing provider confidence in prescribing the ASSURE system. This paper describes the development of a revised ADA and presents comparative validation results relative to the original algorithm. This methodology focuses on the measurement reliability of physical and virtual WCD models to evaluate arrhythmia classification performance using stored ECG episodes from ASSURE WCD patients that reflect the signal-quality variability inherent to wearable systems. The results establish a benchmark for expected real-world benefits. Methods ASSURE WCD – Design and Operational Summary A brief overview is provided here to contextualize subsequent sections; full details of product design, detection logic, and noise discrimination have been previously reported [ 4 ]. The ASSURE WCD system consists of two styles of patient-worn garments with integrated ECG electrodes and a monitor that houses the capacitors, electronics, removable battery, and software, including the ADA. A therapy cable connects the monitor to the garment and delivers therapy through anterior and posterior electrode pads positioned within dedicated garment pockets and secured via a snap-in connector. The ADA integrates per channel adaptive matched filtering to enhance R-wave identification; device determined R-wave width analysis and signal organization metrics for rhythm discrimination; and cross channel machine learning for heart rate determination. The R‑wave width is not a measurement of the total QRS width but rather is derived by the device using specific fiduciary points within a portion of the complex [ 4 ]. Confirmation of an arrhythmia requires persistence beyond initial detection for 5 seconds in the VF zone or 45 seconds in the VT zone. If confirmed, the device enters a reconfirmation phase at which time audible, visual, and tactile alarms are activated to alert the patient of impending therapy. Reconfirmation requires an additional 15 seconds of sustained arrhythmia, providing time for patient intervention via alert button press. Once reconfirmed, the device initiates a 5 second preshock warning, releases conductive gel, and delivers a synchronized 170 J shock unless interrupted by the alert button. If VT/VF persists, up to four additional 170 J shocks can be delivered per episode. Failure to confirm a sustained arrhythmia at any point, or patient activation of the alert button, aborts therapy and returns the device to a monitoring state. Datasets Three datasets were used and each organized into independent development and validation sets: Arrhythmia Rich, Ambulatory, and Field Episodes. The Arrhythmia Rich and Ambulatory datasets were used during development and testing of the original ADA and served as controls to ensure that the revised algorithm maintained VT/VF detection sensitivity and monitoring performance. The Arrhythmia Rich dataset was used to ensure detection compliance with American Heart Association (AHA) performance requirements [ 6 ] and is comprised of artifact-free ECG segments of shockable and nonshockable rhythms collected from electrophysiology labs and other clinical environments. The Ambulatory dataset consisted of continuous ECG recordings (equivalent to 295 patient‑days of wear) gathered during development using WCD prototypes without a shock function. The Field Episode datasets comprised real-world data collected from consecutive patients fitted across the United States within the ASSURE WCD Registry. Episodes from November 2021 through July 2023 were used for development; episodes collected from August 2023 through April 2024 were used for validation testing. The episodes in the Field Episode test dataset were adjudicated by experts in arrhythmia analysis, and declared as VT/VF (VF, monomorphic VT, or polymorphic VT) or non-VT/VF prior to comparative analysis. Each Field Episode dataset included more than 8,000 non-VT/VF episodes from more than 650 patients. False Positive Alarms The majority of episodes in the non-VT/VF portion of the Field Episode dataset represent false detections that were stored by the WCD, but most were short in duration and did not progress to an FPA. Those that resulted in an FPA were categorized as nonsustained (progressed to reconfirmation but spontaneously terminated prior to shock delivery) or sustained (progressed through the detection sequence to therapy delivery). In the development set, the predominant cause of FPAs was SVT accounting for 59% of non-sustained FPAs and 69% of sustained FPAs. Noise contributed to 32% of non-sustained FPAs and 23% of sustained FPAs. Other factors, such as physiologic oversensing, represented 9% and 8% of the non-sustained and sustained FPAs, respectively. WCD Device Models Two objective, repeatable models were developed to compare the performance of the original ADA and revised ADA: a physical WCD Device Model and a virtual WCD Device Model. Strict controls included manufacturer default programming (170bpm VT zone rate, 200bpm VF zone rate); system restart before playback of each ECG to avoid adaptive matched filtering carryover; and deactivation of the alert button so alarms and pending shocks proceeded without interruption, enabling assessment of the full detection sequence. The physical model consisted of WCD systems (without garments) connected to a custom patient simulator that provided ECG and defibrillation interfaces, managed ECG playback, and monitored device state. The virtual model used in development was a high-fidelity MATLAB [ 7 ] implementation replicating WCD behavior and hosting multiple algorithm versions, including the original ADA and prototype revisions. ECG recordings from development datasets were replayed into algorithm versions, and results were compared against the original ADA. This model served as the primary development environment, enabling rapid iteration and direct, paired comparisons. The physical WCD Device Model was used for validation. Algorithm Changes Prototype algorithm revisions were assessed against two competing priorities: improved specificity (FPA reduction) and preserved VT/VF sensitivity. The final candidate selected for validation balanced strong FPA reduction without compromising VT/VF sensitivity. To improve SVT specificity, the device determined R-wave width (not equivalent to total QRS width) threshold for classifying rhythms as SVT in the VT rate zone was increased from 80 ms to 90 ms, while the VF rate zone conservatively retained the 80 ms threshold. A heart rate stability metric was added in the VT rate zone to better distinguish VT from atrial fibrillation with rapid ventricular response. Noise discrimination was strengthened by simplifying the matched filter adaptation logic and refining QRS identification and cross-channel heart rate determination to reduce false detections from motion artifact or electromagnetic interference. Further improvements to QRS identification included updated processing and windowing within the matched filter for more reliable QRS capture, and preservation of noise-free, lower amplitude signals to support more accurate rate determination across diverse ECG conditions. Finally, the required durations for VT/VF detection during reconfirmation and post-shock redetection were extended by 5 seconds to allow more time for noise to subside or patients to divert. To aid interpretation, Fig. 1 illustrates the integrated system design and key functional elements of the revised ADA. Validation and Statistical Analysis Validation used the physical WCD Device Model to compare performance of the original ADA and revised ADA. The legacy Arrhythmia Rich test dataset was used to evaluate shockable rhythm sensitivity and against AHA performance thresholds [ 6 ] and statistically compared using one-sided, 90% lower confidence limits (exact binominal methods (Clopper-Pearson)). The non-VT/VF recordings from the Field Episode test dataset were used to measure specificity as per-episode and per-patient FPA rates (nonsustained, sustained, and total) for both ADA versions. Comparative reductions and two-sided 95% confidence intervals were estimated using Fisher’s exact test. Field Episode VT/VF recordings were used to measure sensitivity; numerical results were reported. All statistical analyses were conducted using IBM SPSS Statistics, version 29.0.2.0 (20). Results VT/VF Sensitivity Comparative performance for the original and revised ADA using the VT/VF portion of the Field Episode test dataset demonstrated identical VT/VF sensitivity for both algorithms; 100% of shockable rhythms (137 episodes) were detected by the revised ADA (Table 1 ). Evaluation using the Arrhythmia Rich test dataset exceeded AHA performance goals [ 6 ] and demonstrated statistically equivalent VT/VF sensitivity for both algorithms. Table 1 Field Episode Test Dataset Detection Results (n = 48 patients with 137 VT/VF episodes) Rhythm Classification Episode Sample Size (n) Original ADA Detections (n) Revised ADA Detections (n) Shockable: VF 26 26 26 Shockable: Rapid VT 61 61 61 Intermediate: Other VT 1 50 50 50 Totals 137 137 137 1 Ventricular rhythm (Monomorphic/Polymorphic/Pleomorphic VT) adjudicated heart rate ≥ 170 bpm (nominal VT rate threshold) and ≤ 187 bpm (nominal VT rate threshold + 10%). Specificity Measured as False Positive Alarm Performance Comparative FPA results are summarized in Table 2 (per‑episode) and Table 3 (per‑patient) for the Field Episode test dataset. On a per‑episode basis, total FPAs with the revised ADA were reduced by 64.2% (95% CI 60.8–67.6); sustained FPAs were reduced by 79.9% (75.4–84.0). On a per-patient basis, the revised ADA reduced the number of patients with 1 or more FPAs by 51.9% (CI 43.8% − 60.0%); patients with 1 or more sustained FPAs were reduced by 69.4% (59.2% − 78.3%). Two representative six-second ECG recordings from the non-VT/VF Field Episode development dataset are presented in Fig. 2. They illustrate atrial fibrillation with rapid ventricular response and noise, respectively, which were misclassified by the original ADA causing FPAs. In the real‑world episodes, patients diverted therapy using the alert button. When these same recordings were replayed through the virtual WCD Device Model, the revised ADA correctly classified both rhythms as non‑VT/VF and did not generate episodes or FPAs. Table 2 Field Episode Test Dataset Per‑episode FPA Reduction Results (n = 8,155 non-VT/VF episodes) All FPAs # Episodes with FPAs, Original ADA # Episodes with FPAs, Revised ADA FPA Reduction (%) FPA Reduction (95% CI) 811 290 64.2% 60.8% – 67.6% Sustained FPAs 359 72 79.9% 75.4% – 84.0% Abbreviations : ADA, arrhythmia detection algorithm; FPA, false positive alarm. Table 3 Field Episode Test Dataset Per‑patient FPA Reduction Results (n = 688 patients with non-VT/VF episodes) All FPAs # Patients with ≥ 1 FPA, Original ADA # Patients with ≥ 1 FPA, Revised ADA FPA Reduction (%) FPA Reduction (95% CI) 156 75 51.9% 43.8% – 60.0% Sustained FPAs 98 30 69.4% 59.2% – 78.3% Abbreviations : ADA, arrhythmia detection algorithm; FPA, false positive alarm. Discussion The ASSURE system employs an integrated hardware and software strategy for sensing and artifact reduction, enabling high sensitivity to life‑threatening ventricular arrhythmias and a competitively low FPA rate. Post‑market experience, however, revealed limitations in distinguishing supraventricular tachyarrhythmias, most notably atrial fibrillation and SVT with rapid rates, from true VT due to overlap in QRS morphology and ventricular rate. Noise artifact and other factors, such as physiologic oversensing, also contributed to FPAs leaving patients exposed to FPAs and possible inappropriate therapies. Importantly, real-world WCD ECG episodes were incorporated into the development of the revised ADA, and were used for a direct, comparative validation of both sensitivity and FPAs as a measure of specificity. The revised ADA retains the core architecture of the original algorithm and fully preserves its ability to detect VT/VF, as demonstrated by the 100% detection of shockable rhythms in the Field Episode test dataset. Inclusion of these field episodes was critical for identifying the specific factors that limited the original algorithm’s specificity and guided targeted refinements. These revisions resulted in focused enhancements to SVT discrimination and more accurate QRS detection in the presence of noise artifact and challenging QRS morphologies. The Field Episode datasets mirror the full diversity of U.S. WCD patients across demographics, regions, rhythms, and signal characteristics. Consequently, the 51.9% relative reduction in per‑patient total FPAs is expected to be predictive of real‑world experience with the revised ADA. Although the per-patient sustained FPA rate is not equivalent to the inappropriate shock rate (given the potential for patients to divert), it represents the potential for inappropriate therapy. The 69.4% relative reduction in per‑patient sustained FPAs with the revised ADA indicates a substantial reduction in exposure to potential inappropriate shocks. Collectively, these findings indicate that the revised ADA delivers a clinically meaningful enhancement in specificity while maintaining the high sensitivity required for a lifesaving WCD system. Clinical Implications By reducing both total and sustained FPAs, the revised ADA is expected to lessen alarm fatigue and anxiety, improve sleep and daily wear adherence, and reduce exposure to potential inappropriate therapy. When the comparative validation results are applied to the existing ACE-PAS registry data, only an estimated 3% of patients are projected to experience any FPA with the revised ADA, and the opportunity for inappropriate therapies is estimated to be less than 0.4%. Limitations Differentiating SVT with wide QRS morphology and rapid ventricular response from true VT remains challenging. Although the revised ADA improves specificity for these rhythms, some overlap in morphology and rate characteristics is unavoidable. The testing environment differs from clinical use in that alert button use is disabled, so in the presence of a sustained FPA, test episodes proceeded to the committed shock state. Consequently, absolute sustained FPA counts in testing cannot be equated directly to inappropriate shock rates in the field; patient diverts may make the inappropriate shock rate lower. However, while the WCD alert button function provides an important safety mechanism, symptomatic or anxious patients may be less likely to intervene, underscoring the need to minimize both FPA frequency and reliance on patient diversion. Finally, the present evaluation is retrospective, based on prerecorded datasets rather than a prospective clinical study. Although the Field Episode dataset is extensive, well‑annotated, and representative of real‑world WCD use, prospective validation would provide a more definitive assessment of algorithm performance, patient experience, and clinical impact. Conclusion The revised ASSURE Detection Algorithm was evaluated using real-world ECG data replayed into software simulations of the ASSURE WCD and actual ASSURE WCD hardware. It maintained high sensitivity for detecting shockable ventricular arrhythmias while significantly increasing specificity as measured by the reduction in FPAs and opportunity for inappropriate therapy. Reduced FPA burden is expected to improve patient experience, decrease unnecessary clinical interventions, and increase trust and adherence. Declarations Acknowledgements The authors thank Melinda Wang, Laura Gustavson, and Kristin Eis for assistance with episode annotation, registry management, and consultation. Funding Kestra Medical Technologies, Inc. funded this algorithm project in its entirety including registry data collection, analysis, and interpretation; writing of the report; and the decision to submit the article for publication. Competing Interests DF, JK, MW, and PB are or have been employees of Kestra Medical Technologies, Inc. and KH is a paid consultant for same. Ethics Approval and Consent to Participate The registry protocol (ACE‑PAS, NCT05135403) and retrospective analyses were approved by the WCG IRB, protocol ID 20213046, with a waiver of informed consent for analysis of de‑identified data. The study complied with the Declaration of Helsinki. Data Availability The datasets generated and analyzed during the current study are proprietary to Kestra Medical Technologies and therefore are not publicly available. Limited de‑identified data may be shared by the corresponding author upon reasonable request and subject to data‑use agreements. References Zylla MM, Hillmann HAK, Proctor T, Kieser M, Scholz E, Zitron E, Katus HA, Thomas D (2018) Use of the wearable cardioverter-defibrillator (WCD) and WCD-based remote rhythm monitoring in a real-life patient cohort. Heart Vessels 33(11):1390–1402. 10.1007/s00380-018-1181-x Epub 2018 May 2. PMID: 29721674 Hummel J, Houmsse M, Tomassoni G et al (2024 Aug) A Patch Wearable Cardioverter-Defibrillator for Patients at Risk of Sudden Cardiac Arrest. 84(6):525–536. https://doi.org/10.1016/j.jacc.2024.04.063 Arkles J, Delaughter C, D'Souza B (2023) A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms. J Interv Card Electrophysiol 66(7):1723–1728. 10.1007/s10840-023-01497-w Epub 2023 Feb 15. PMID: 36790576 Poole JE, Gleva MJ, Birgersdotter-Green U, Branch KRH, Doshi RN, Salam T, Crawford TC, Willcox ME, Sridhar AM, Mikdadi G, Beinart SC, Cha YM, Russo AM, Rowbotham RK, Sullivan J, Gustavson LM, Kivilaid K (2022) A wearable cardioverter defibrillator with a low false alarm rate. J Cardiovasc Electrophysiol 33(5):831–842. 10.1111/jce.15417 Epub 2022 Feb 28. PMID: 35174572; PMCID: PMC9305432 Poole JE et al (2025) ASSURE WCD Clinical Evaluation Post–Approval Study (ACE–PAS): Results From a Large Real–World Wearable Cardioverter–Defibrillator Registry. Circulation. ;152:e526–e605. Late–Breaking Science Abstract, American Heart Association Scientific Sessions 2025 Kerber RE, Becker LB, Bourland JD, Cummins RO, Hallstrom AP, Michos MB, Nichol G, Ornato JP, Thies WH, White RD, Zuckerman BD (1997) Automatic external defibrillators for public access defibrillation: recommendations for specifying and reporting arrhythmia analysis algorithm performance, incorporating new waveforms, and enhancing safety. A statement for health professionals from the American Heart Association Task Force on Automatic External Defibrillation, Subcommittee on AED Safety and Efficacy. Circulation. ;95(6):1677-82. 10.1161/01.cir.95.6.1677 . PMID: 9118556 The MathWorks Inc (2025) MATLAB Version: 9.11.0.1809720 (R2021b) Update 1, Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com Additional Declarations The authors declare potential competing interests as follows: All authors are or have been employees of or paid consultants for Kestra Medical Technologies, Inc. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9453768","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626503403,"identity":"baf7412a-068b-4f01-95c0-430c46e52025","order_by":0,"name":"David Finch","email":"","orcid":"","institution":"Kestra Medical Technologies, Inc., Kirkland, WA, USA","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Finch","suffix":""},{"id":626503404,"identity":"6e83d0e8-ec26-4497-8558-73ad713ab42d","order_by":1,"name":"Jaeho Kim","email":"","orcid":"","institution":"Kestra Medical Technologies, Inc., Kirkland, WA, USA","correspondingAuthor":false,"prefix":"","firstName":"Jaeho","middleName":"","lastName":"Kim","suffix":""},{"id":626503405,"identity":"81035ebb-c5f8-47aa-af70-5a1cf2076b7f","order_by":2,"name":"Maurie Wiswell","email":"","orcid":"","institution":"Kestra Medical Technologies, Inc., Kirkland, WA, USA","correspondingAuthor":false,"prefix":"","firstName":"Maurie","middleName":"","lastName":"Wiswell","suffix":""},{"id":626503406,"identity":"72d86948-7a8a-4bb3-9f60-6e3b7903ad7b","order_by":3,"name":"Karl Hibler","email":"","orcid":"","institution":"Kestra Medical Technologies, Inc., Kirkland, WA, USA","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"","lastName":"Hibler","suffix":""},{"id":626503407,"identity":"05ca8079-d8f0-44e2-a522-1bf05af2509c","order_by":4,"name":"Pamela Breske","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-8380-8467","institution":"Kestra Medical Technologies, Inc., Kirkland, WA, USA","correspondingAuthor":true,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Breske","suffix":""}],"badges":[],"createdAt":"2026-04-18 03:34:22","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9453768/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9453768/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107515390,"identity":"11683908-87a6-41bc-be3c-6dc5e9940772","added_by":"auto","created_at":"2026-04-22 08:28:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1473513,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated system design and key functional elements of the revised detection algorithm\u003c/p\u003e\n\u003cp\u003eThree interdependent levels collectively reduce noise and support reliable rhythm detection.\u003c/p\u003e\n\u003cp\u003eNoise minimization forms the foundation with a combination of garment design, electrode configuration, shielded cabling, and patient alerts that promote continued signal integrity.\u003c/p\u003e\n\u003cp\u003eSignal processing builds on this foundation using 4-independent channels for ECG sensing, dynamic channel qualification, high and low pass filtering, adaptive matched filtering, and a machine‑learning based heart‑rate determination.\u003c/p\u003e\n\u003cp\u003eRhythm classification incorporates this conditioned signal information to accurately detect VT/VF using rate‑zone logic, R‑wave width analysis, heart‑rate stability metrics, and adaptive confirmation periods for slower rhythms or noisy conditions to preserve detection accuracy and reduce false positive alarms.\u003c/p\u003e\n\u003cp\u003eFigure 1 is adapted from a figure published in Poole et al., J Cardiovasc Electrophysiol, 2022 [4]\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADA, arrhythmia detection algorithm; AF, atrial fibrillation; DC, direct current; ECG, electrocardiogram; HR, heart rate; SVT, supraventricular tachycardia; VF, ventricular fibrillation; VT, ventricular tachycardia; WCD, wearable cardioverter defibrillator.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9453768/v1/243dd591baab6c6e50ea51b3.png"},{"id":107515394,"identity":"93032ba7-870f-45c0-8825-5103196656af","added_by":"auto","created_at":"2026-04-22 08:28:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3860805,"visible":true,"origin":"","legend":"\u003cp\u003eExample False Positive Alarm Episodes using the Original ADA and Correct Classification by the Revised ADA\u003c/p\u003e\n\u003cp\u003eTwo representative six‑second ECG recordings from the non‑VT/VF Field Episode development dataset illustrate atrial fibrillation with rapid ventricular response and noise, respectively, which were misclassified by the original ADA causing FPAs. In both episodes, the onset of the FPA is indicated by the “HEART ALERT” notification with additional information in the associated segment detection metrics. In the real‑world episodes, patients diverted therapy using the alert button. When these same episodes were replayed through the virtual WCD Device Model, the revised ADA correctly classified both episodes as non‑VT/VF and did not record episodes or generate FPAs.\u003c/p\u003e\n\u003cp\u003e2a. Episode excerpt illustrating FPA caused by atrial fibrillation with rapid ventricular response, with segment‑level detection markers (e.g., S44, S45) displayed above the ECG waveform and detection information on the right side.\u003c/p\u003e\n\u003cp\u003e2b. Episode excerpt demonstrating an FPA produced by noise artifact, where motion‑ or interference‑related distortion resulted in erroneous rhythm detection by the original ADA.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADA, arrhythmia detection algorithm; FPA, false positive alarm; VT/VF, ventricular tachycardia/ventricular fibrillation; WCD, wearable cardioverter defibrillator.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9453768/v1/4ff0b4be37877eeed8889d15.png"},{"id":107706141,"identity":"20a87812-6c8f-41d7-bdc6-13269e23b541","added_by":"auto","created_at":"2026-04-24 09:17:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5534006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9453768/v1/0217a535-3ac9-4ea1-8d18-2dd2f27da510.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: All authors are or have been employees of or paid consultants for Kestra Medical Technologies, Inc.","formattedTitle":"\u003cp\u003eEnhancing Wearable Cardioverter Defibrillator Performance: Comparative Validation of a Revised Algorithm to Reduce False Positive Alarms\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ASSURE\u0026reg; wearable cardioverter defibrillator (WCD) is designed to protect patients at risk of sudden cardiac death while optimizing comfort and minimizing false positive shock alarms (FPAs). Near continuous wear is essential for timely detection and treatment of life-threatening ventricular arrhythmias; however, prior studies of other FDA approved WCDs have identified adherence challenges often linked to discomfort and frequent FPAs [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCentral to ASSURE system performance is the ASSURE Detection Algorithm (ADA), engineered to maintain high sensitivity in detecting ventricular arrhythmias with sufficient specificity to keep FPAs low. FPAs are clinically important because they reflect rhythm misclassification that can lead to inappropriate therapy; even when nonsustained or diverted, FPAs can provoke anxiety and diminish patient quality of life. In bench testing and an early human trial, ADA performance was robust [1 FPA per 1,333 patient days with no missed episodes] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], supporting Food and Drug Administration Pre-Market Approval in July 2021. Post market surveillance further corroborated these findings. In the ASSURE WCD Clinical Evaluation \u0026ndash; Post Approval Study (ACE-PAS, NCT05135403), FPAs occurred in only 6% of patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], comparing favorably to other WCD systems that reported FPAs in 46% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and 38% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] of patients.\u003c/p\u003e \u003cp\u003eDespite this superior performance, analysis of residual FPAs revealed opportunities to further increase specificity and minimize inappropriate shock risk without compromising VT/VF sensitivity. The intended clinical benefit is improved patient safety, experience, and adherence, while reinforcing provider confidence in prescribing the ASSURE system.\u003c/p\u003e \u003cp\u003eThis paper describes the development of a revised ADA and presents comparative validation results relative to the original algorithm. This methodology focuses on the measurement reliability of physical and virtual WCD models to evaluate arrhythmia classification performance using stored ECG episodes from ASSURE WCD patients that reflect the signal-quality variability inherent to wearable systems. The results establish a benchmark for expected real-world benefits.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eASSURE WCD \u0026ndash; Design and Operational Summary\u003c/h2\u003e \u003cp\u003eA brief overview is provided here to contextualize subsequent sections; full details of product design, detection logic, and noise discrimination have been previously reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The ASSURE WCD system consists of two styles of patient-worn garments with integrated ECG electrodes and a monitor that houses the capacitors, electronics, removable battery, and software, including the ADA. A therapy cable connects the monitor to the garment and delivers therapy through anterior and posterior electrode pads positioned within dedicated garment pockets and secured via a snap-in connector. The ADA integrates per channel adaptive matched filtering to enhance R-wave identification; device determined R-wave width analysis and signal organization metrics for rhythm discrimination; and cross channel machine learning for heart rate determination. The R‑wave width is not a measurement of the total QRS width but rather is derived by the device using specific fiduciary points within a portion of the complex [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Confirmation of an arrhythmia requires persistence beyond initial detection for 5 seconds in the VF zone or 45 seconds in the VT zone. If confirmed, the device enters a reconfirmation phase at which time audible, visual, and tactile alarms are activated to alert the patient of impending therapy. Reconfirmation requires an additional 15 seconds of sustained arrhythmia, providing time for patient intervention via alert button press. Once reconfirmed, the device initiates a 5 second preshock warning, releases conductive gel, and delivers a synchronized 170 J shock unless interrupted by the alert button. If VT/VF persists, up to four additional 170 J shocks can be delivered per episode. Failure to confirm a sustained arrhythmia at any point, or patient activation of the alert button, aborts therapy and returns the device to a monitoring state.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDatasets\u003c/h3\u003e\n\u003cp\u003eThree datasets were used and each organized into independent development and validation sets: Arrhythmia Rich, Ambulatory, and Field Episodes. The Arrhythmia Rich and Ambulatory datasets were used during development and testing of the original ADA and served as controls to ensure that the revised algorithm maintained VT/VF detection sensitivity and monitoring performance. The Arrhythmia Rich dataset was used to ensure detection compliance with American Heart Association (AHA) performance requirements [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and is comprised of artifact-free ECG segments of shockable and nonshockable rhythms collected from electrophysiology labs and other clinical environments. The Ambulatory dataset consisted of continuous ECG recordings (equivalent to 295 patient‑days of wear) gathered during development using WCD prototypes without a shock function. The Field Episode datasets comprised real-world data collected from consecutive patients fitted across the United States within the ASSURE WCD Registry. Episodes from November 2021 through July 2023 were used for development; episodes collected from August 2023 through April 2024 were used for validation testing. The episodes in the Field Episode test dataset were adjudicated by experts in arrhythmia analysis, and declared as VT/VF (VF, monomorphic VT, or polymorphic VT) or non-VT/VF prior to comparative analysis. Each Field Episode dataset included more than 8,000 non-VT/VF episodes from more than 650 patients.\u003c/p\u003e\n\u003ch3\u003eFalse Positive Alarms\u003c/h3\u003e\n\u003cp\u003eThe majority of episodes in the non-VT/VF portion of the Field Episode dataset represent false detections that were stored by the WCD, but most were short in duration and did not progress to an FPA. Those that resulted in an FPA were categorized as nonsustained (progressed to reconfirmation but spontaneously terminated prior to shock delivery) or sustained (progressed through the detection sequence to therapy delivery). In the development set, the predominant cause of FPAs was SVT accounting for 59% of non-sustained FPAs and 69% of sustained FPAs. Noise contributed to 32% of non-sustained FPAs and 23% of sustained FPAs. Other factors, such as physiologic oversensing, represented 9% and 8% of the non-sustained and sustained FPAs, respectively.\u003c/p\u003e\n\u003ch3\u003eWCD Device Models\u003c/h3\u003e\n\u003cp\u003eTwo objective, repeatable models were developed to compare the performance of the original ADA and revised ADA: a physical WCD Device Model and a virtual WCD Device Model. Strict controls included manufacturer default programming (170bpm VT zone rate, 200bpm VF zone rate); system restart before playback of each ECG to avoid adaptive matched filtering carryover; and deactivation of the alert button so alarms and pending shocks proceeded without interruption, enabling assessment of the full detection sequence. The physical model consisted of WCD systems (without garments) connected to a custom patient simulator that provided ECG and defibrillation interfaces, managed ECG playback, and monitored device state. The virtual model used in development was a high-fidelity MATLAB [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] implementation replicating WCD behavior and hosting multiple algorithm versions, including the original ADA and prototype revisions. ECG recordings from development datasets were replayed into algorithm versions, and results were compared against the original ADA. This model served as the primary development environment, enabling rapid iteration and direct, paired comparisons. The physical WCD Device Model was used for validation.\u003c/p\u003e\n\u003ch3\u003eAlgorithm Changes\u003c/h3\u003e\n\u003cp\u003ePrototype algorithm revisions were assessed against two competing priorities: improved specificity (FPA reduction) and preserved VT/VF sensitivity. The final candidate selected for validation balanced strong FPA reduction without compromising VT/VF sensitivity. To improve SVT specificity, the device determined R-wave width (not equivalent to total QRS width) threshold for classifying rhythms as SVT in the VT rate zone was increased from 80 ms to 90 ms, while the VF rate zone conservatively retained the 80 ms threshold. A heart rate stability metric was added in the VT rate zone to better distinguish VT from atrial fibrillation with rapid ventricular response. Noise discrimination was strengthened by simplifying the matched filter adaptation logic and refining QRS identification and cross-channel heart rate determination to reduce false detections from motion artifact or electromagnetic interference. Further improvements to QRS identification included updated processing and windowing within the matched filter for more reliable QRS capture, and preservation of noise-free, lower amplitude signals to support more accurate rate determination across diverse ECG conditions. Finally, the required durations for VT/VF detection during reconfirmation and post-shock redetection were extended by 5 seconds to allow more time for noise to subside or patients to divert. To aid interpretation, Fig.\u0026nbsp;1 illustrates the integrated system design and key functional elements of the revised ADA.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation and Statistical Analysis\u003c/h2\u003e \u003cp\u003eValidation used the physical WCD Device Model to compare performance of the original ADA and revised ADA. The legacy Arrhythmia Rich test dataset was used to evaluate shockable rhythm sensitivity and against AHA performance thresholds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and statistically compared using one-sided, 90% lower confidence limits (exact binominal methods (Clopper-Pearson)).\u003c/p\u003e \u003cp\u003eThe non-VT/VF recordings from the Field Episode test dataset were used to measure specificity as per-episode and per-patient FPA rates (nonsustained, sustained, and total) for both ADA versions. Comparative reductions and two-sided 95% confidence intervals were estimated using Fisher\u0026rsquo;s exact test. Field Episode VT/VF recordings were used to measure sensitivity; numerical results were reported. All statistical analyses were conducted using IBM SPSS Statistics, version 29.0.2.0 (20).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eVT/VF Sensitivity\u003c/h2\u003e \u003cp\u003eComparative performance for the original and revised ADA using the VT/VF portion of the Field Episode test dataset demonstrated identical VT/VF sensitivity for both algorithms; 100% of shockable rhythms (137 episodes) were detected by the revised ADA (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Evaluation using the Arrhythmia Rich test dataset exceeded AHA performance goals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and demonstrated statistically equivalent VT/VF sensitivity for both algorithms.\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\u003eField Episode Test Dataset Detection Results (n\u0026thinsp;=\u0026thinsp;48 patients with 137 VT/VF episodes)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhythm Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpisode Sample Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOriginal ADA Detections (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRevised ADA Detections (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShockable: VF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShockable: Rapid VT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate: \u003c/p\u003e \u003cp\u003eOther VT\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003e Ventricular rhythm (Monomorphic/Polymorphic/Pleomorphic VT) adjudicated heart rate\u0026thinsp;\u0026ge;\u0026thinsp;170 bpm (nominal VT rate threshold) and \u0026le;\u0026thinsp;187 bpm (nominal VT rate threshold\u0026thinsp;+\u0026thinsp;10%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpecificity Measured as False Positive Alarm Performance\u003c/h2\u003e \u003cp\u003eComparative FPA results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (per‑episode) and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (per‑patient) for the Field Episode test dataset. On a per‑episode basis, total FPAs with the revised ADA were reduced by 64.2% (95% CI 60.8\u0026ndash;67.6); sustained FPAs were reduced by 79.9% (75.4\u0026ndash;84.0). On a per-patient basis, the revised ADA reduced the number of patients with 1 or more FPAs by 51.9% (CI 43.8% \u0026minus;\u0026thinsp;60.0%); patients with 1 or more sustained FPAs were reduced by 69.4% (59.2% \u0026minus;\u0026thinsp;78.3%). Two representative six-second ECG recordings from the non-VT/VF Field Episode development dataset are presented in Fig.\u0026nbsp;2. They illustrate atrial fibrillation with rapid ventricular response and noise, respectively, which were misclassified by the original ADA causing FPAs. In the real‑world episodes, patients diverted therapy using the alert button. When these same recordings were replayed through the virtual WCD Device Model, the revised ADA correctly classified both rhythms as non‑VT/VF and did not generate episodes or FPAs.\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\u003eField Episode Test Dataset Per‑episode FPA Reduction Results (n\u0026thinsp;=\u0026thinsp;8,155 non-VT/VF episodes)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll FPAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# Episodes with FPAs, Original ADA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# Episodes with FPAs, Revised ADA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPA Reduction (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFPA Reduction (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.2%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.8% \u0026ndash; 67.6%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustained FPAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.4% \u0026ndash; 84.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: ADA, arrhythmia detection algorithm; FPA, false positive alarm.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eField Episode Test Dataset Per‑patient FPA Reduction Results (n\u0026thinsp;=\u0026thinsp;688 patients with non-VT/VF episodes)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll FPAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# Patients with \u0026ge;\u0026thinsp;1 FPA, Original ADA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# Patients with \u0026ge;\u0026thinsp;1 FPA, Revised ADA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFPA Reduction (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFPA Reduction (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.9%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.8% \u0026ndash; 60.0%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustained FPAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.2% \u0026ndash; 78.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: ADA, arrhythmia detection algorithm; FPA, false positive alarm.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe ASSURE system employs an integrated hardware and software strategy for sensing and artifact reduction, enabling high sensitivity to life‑threatening ventricular arrhythmias and a competitively low FPA rate. Post‑market experience, however, revealed limitations in distinguishing supraventricular tachyarrhythmias, most notably atrial fibrillation and SVT with rapid rates, from true VT due to overlap in QRS morphology and ventricular rate. Noise artifact and other factors, such as physiologic oversensing, also contributed to FPAs leaving patients exposed to FPAs and possible inappropriate therapies.\u003c/p\u003e \u003cp\u003eImportantly, real-world WCD ECG episodes were incorporated into the development of the revised ADA, and were used for a direct, comparative validation of both sensitivity and FPAs as a measure of specificity. The revised ADA retains the core architecture of the original algorithm and fully preserves its ability to detect VT/VF, as demonstrated by the 100% detection of shockable rhythms in the Field Episode test dataset. Inclusion of these field episodes was critical for identifying the specific factors that limited the original algorithm\u0026rsquo;s specificity and guided targeted refinements. These revisions resulted in focused enhancements to SVT discrimination and more accurate QRS detection in the presence of noise artifact and challenging QRS morphologies.\u003c/p\u003e \u003cp\u003eThe Field Episode datasets mirror the full diversity of U.S. WCD patients across demographics, regions, rhythms, and signal characteristics. Consequently, the 51.9% relative reduction in per‑patient total FPAs is expected to be predictive of real‑world experience with the revised ADA. Although the per-patient sustained FPA rate is not equivalent to the inappropriate shock rate (given the potential for patients to divert), it represents the potential for inappropriate therapy. The 69.4% relative reduction in per‑patient sustained FPAs with the revised ADA indicates a substantial reduction in exposure to potential inappropriate shocks. Collectively, these findings indicate that the revised ADA delivers a clinically meaningful enhancement in specificity while maintaining the high sensitivity required for a lifesaving WCD system.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eBy reducing both total and sustained FPAs, the revised ADA is expected to lessen alarm fatigue and anxiety, improve sleep and daily wear adherence, and reduce exposure to potential inappropriate therapy. When the comparative validation results are applied to the existing ACE-PAS registry data, only an estimated 3% of patients are projected to experience any FPA with the revised ADA, and the opportunity for inappropriate therapies is estimated to be less than 0.4%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDifferentiating SVT with wide QRS morphology and rapid ventricular response from true VT remains challenging. Although the revised ADA improves specificity for these rhythms, some overlap in morphology and rate characteristics is unavoidable. The testing environment differs from clinical use in that alert button use is disabled, so in the presence of a sustained FPA, test episodes proceeded to the committed shock state. Consequently, absolute sustained FPA counts in testing cannot be equated directly to inappropriate shock rates in the field; patient diverts may make the inappropriate shock rate lower. However, while the WCD alert button function provides an important safety mechanism, symptomatic or anxious patients may be less likely to intervene, underscoring the need to minimize both FPA frequency and reliance on patient diversion. Finally, the present evaluation is retrospective, based on prerecorded datasets rather than a prospective clinical study. Although the Field Episode dataset is extensive, well‑annotated, and representative of real‑world WCD use, prospective validation would provide a more definitive assessment of algorithm performance, patient experience, and clinical impact.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe revised ASSURE Detection Algorithm was evaluated using real-world ECG data replayed into software simulations of the ASSURE WCD and actual ASSURE WCD hardware. It maintained high sensitivity for detecting shockable ventricular arrhythmias while significantly increasing specificity as measured by the reduction in FPAs and opportunity for inappropriate therapy. Reduced FPA burden is expected to improve patient experience, decrease unnecessary clinical interventions, and increase trust and adherence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank Melinda Wang, Laura Gustavson, and Kristin Eis for assistance with episode annotation, registry management, and consultation.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eKestra Medical Technologies, Inc. funded this algorithm project in its entirety including registry data collection, analysis, and interpretation; writing of the report; and the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eDF, JK, MW, and PB are or have been employees of Kestra Medical Technologies, Inc. and KH is a paid consultant for same.\u003c/p\u003e\n\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThe registry protocol (ACE‑PAS, NCT05135403) and retrospective analyses were approved by the WCG IRB, protocol ID 20213046, with a waiver of informed consent for analysis of de‑identified data. The study complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are proprietary to Kestra Medical Technologies and therefore are not publicly available. Limited de‑identified data may be shared by the corresponding author upon reasonable request and subject to data‑use agreements.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZylla MM, Hillmann HAK, Proctor T, Kieser M, Scholz E, Zitron E, Katus HA, Thomas D (2018) Use of the wearable cardioverter-defibrillator (WCD) and WCD-based remote rhythm monitoring in a real-life patient cohort. Heart Vessels 33(11):1390\u0026ndash;1402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00380-018-1181-x\u003c/span\u003e\u003cspan address=\"10.1007/s00380-018-1181-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2018 May 2. PMID: 29721674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHummel J, Houmsse M, Tomassoni G et al (2024 Aug) A Patch Wearable Cardioverter-Defibrillator for Patients at Risk of Sudden Cardiac Arrest. 84(6):525\u0026ndash;536. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2024.04.063\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2024.04.063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArkles J, Delaughter C, D'Souza B (2023) A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms. J Interv Card Electrophysiol 66(7):1723\u0026ndash;1728. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10840-023-01497-w\u003c/span\u003e\u003cspan address=\"10.1007/s10840-023-01497-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2023 Feb 15. PMID: 36790576\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoole JE, Gleva MJ, Birgersdotter-Green U, Branch KRH, Doshi RN, Salam T, Crawford TC, Willcox ME, Sridhar AM, Mikdadi G, Beinart SC, Cha YM, Russo AM, Rowbotham RK, Sullivan J, Gustavson LM, Kivilaid K (2022) A wearable cardioverter defibrillator with a low false alarm rate. J Cardiovasc Electrophysiol 33(5):831\u0026ndash;842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jce.15417\u003c/span\u003e\u003cspan address=\"10.1111/jce.15417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2022 Feb 28. PMID: 35174572; PMCID: PMC9305432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoole JE et al (2025) ASSURE WCD Clinical Evaluation Post\u0026ndash;Approval Study (ACE\u0026ndash;PAS): Results From a Large Real\u0026ndash;World Wearable Cardioverter\u0026ndash;Defibrillator Registry. Circulation. ;152:e526\u0026ndash;e605. Late\u0026ndash;Breaking Science Abstract, American Heart Association Scientific Sessions 2025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerber RE, Becker LB, Bourland JD, Cummins RO, Hallstrom AP, Michos MB, Nichol G, Ornato JP, Thies WH, White RD, Zuckerman BD (1997) Automatic external defibrillators for public access defibrillation: recommendations for specifying and reporting arrhythmia analysis algorithm performance, incorporating new waveforms, and enhancing safety. A statement for health professionals from the American Heart Association Task Force on Automatic External Defibrillation, Subcommittee on AED Safety and Efficacy. Circulation. ;95(6):1677-82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.cir.95.6.1677\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.95.6.1677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 9118556\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe MathWorks Inc (2025) MATLAB Version: 9.11.0.1809720 (R2021b) Update 1, Natick, Massachusetts: The MathWorks Inc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kestra Medical Technologies, Inc","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"wearable cardioverter defibrillator (WCD), detection algorithm, false positive alarms, signal quality, comparative validation","lastPublishedDoi":"10.21203/rs.3.rs-9453768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9453768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe ASSURE\u0026reg; wearable cardioverter defibrillator (WCD) is designed to protect patients at risk of sudden cardiac death while minimizing false positive shock alarms (FPAs), which can undermine confidence and reduce adherence. Although the original ASSURE Detection Algorithm (ADA) demonstrated robust performance, post-market experience identified opportunities to further reduce FPAs without compromising ventricular tachycardia/fibrillation (VT/VF) sensitivity.\u003c/p\u003e\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eTo develop and validate a revised ADA that reduces FPAs while preserving VT/VF sensitivity, using stored ECG episode data obtained from ASSURE WCD patients.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eAlgorithm refinements focused on improved supraventricular tachycardia discrimination, enhanced noise management, and more accurate QRS identification. Development incorporated a high-fidelity virtual model for rapid iteration, and validation was performed using a physical WCD device model. Comparative validation relied on the measurement reliability of the WCD models to enable controlled evaluation of arrhythmia classification performance. Sensitivity, and per-episode and per-patient FPA rates as measures of specificity, were assessed using a real-world ECG dataset and corroborated with datasets used for original algorithm testing.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe revised algorithm detected 100% of shockable VT/VF rhythms (137 episodes) in the Field Episode test dataset. In \u0026gt;\u0026thinsp;8,000 non-VT/VF Field Episodes, the revised ADA reduced per-patient total FPAs by 51.9% (95% CI 43.8\u0026ndash;60.0) and sustained FPAs by 69.4% (59.2\u0026ndash;78.3). Testing with legacy datasets confirmed continued compliance with standard performance criteria.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eUsing a controlled comparative validation methodology, the revised ADA demonstrated substantial reductions in FPAs while maintaining high VT/VF sensitivity, supporting improved patient experience, adherence, and reduced exposure to potential inappropriate therapy.\u003c/p\u003e","manuscriptTitle":"Enhancing Wearable Cardioverter Defibrillator Performance: Comparative Validation of a Revised Algorithm to Reduce False Positive Alarms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 08:27:54","doi":"10.21203/rs.3.rs-9453768/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0263c50-f835-4b41-a709-8d10af7dfb62","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66677390,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2026-04-22T08:27:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 08:27:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9453768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9453768","identity":"rs-9453768","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}