Deep Learning for Preictal Change Point Detection: Enhancing Seizure Prediction in EEG Signals

Deep Learning for Preictal Change Point Detection: Enhancing Seizure Prediction in EEG Signals | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Deep Learning for Preictal Change Point Detection: Enhancing Seizure Prediction in EEG Signals Authors : Mohammadmahdi Ghasemloo and Hadi Gholami 0009-0005-2444-1956 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173640807.77294873/v1 407 views 150 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Seizure prediction is a critical challenge in healthcare, with significant implications for improving the quality of life of patients with epilepsy. Traditional methods often assume a fixed preictal period, typically 30 minutes, which may not adequately capture patient-specific and channel-specific variations. In this paper, we propose a novel approach to modeling preictal durations as probabilistic distributions and detecting change points in EEG time series using autoencoders. By leveraging the CHB-MIT Scalp EEG Database, we investigate preictal dynamics across multiple channels and evaluate the effectiveness of our method in identifying individualized preictal periods. Our approach segments EEG signals into fixed-size windows and trains autoencoders to detect deviations in reconstruction errors, which serve as indicators of change points. This framework captures complex temporal patterns, ensuring sensitivity to both abrupt and gradual transitions. Numerical experiments demonstrate that the assumed 30-minute preictal period exhibits significant variance, underscoring the necessity of adaptive modeling techniques. Analysis across 17 EEG channels highlights substantial variability in preictal durations, revealing the need for channel-specific detection strategies. Furthermore, we identify channels with higher rates of unpredicted events and propose weighting strategies to optimize model performance. The proposed method achieves stationary time series for preictal periods and aligns predicted preictal start times with observable transitions in EEG signals. Our findings emphasize the importance of personalized seizure prediction models that account for variability across patients and channels. By advancing the understanding of preictal dynamics and offering a robust change point detection framework, this study contributes to the development of reliable and scalable seizure prediction systems for diverse clinical applications. Supplementary Material File (deep learning eeg signals.pdf) Download 783.68 KB Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning eeg signals seizures time series analysis Authors Affiliations Mohammadmahdi Ghasemloo Sharif University of Technology View all articles by this author Hadi Gholami 0009-0005-2444-1956 [email protected] University of Science and Culture View all articles by this author Metrics & Citations Metrics Article Usage 407 views 150 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammadmahdi Ghasemloo, Hadi Gholami. Deep Learning for Preictal Change Point Detection: Enhancing Seizure Prediction in EEG Signals. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173640807.77294873/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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