Analyzing the performance of biomedical time-series segmentation with electrophysiology data
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Date
2025-04-06
Authors
Ředina, Richard
Hejč, Jakub
Filipenská, Marina
Stárek, Zdeněk
0000-0002-4901-0504Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
NATURE PORTFOLIO
Altmetrics
Abstract
Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.
Description
Citation
Scientific Reports. 2025, vol. 15, issue 1, p. 1-15.
https://www.nature.com/articles/s41598-025-90533-y#Sec23
https://www.nature.com/articles/s41598-025-90533-y#Sec23
Document type
Peer-reviewed
Document version
Published version
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Language of document
en
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Date of acceptance
Defence
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Document licence
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://creativecommons.org/licenses/by-nc-nd/4.0/