Publication detail

Analyzing the performance of biomedical time-series segmentation with electrophysiology data

ŘEDINA, R. HEJČ, J. FILIPENSKÁ, M. STÁREK, Z.

Original Title

Analyzing the performance of biomedical time-series segmentation with electrophysiology data

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Time-series Segmentation; Electrophysiology Study; Rule-based Delineation; Support Vector Machines; U-Net; Faster R-CNN; DENS-ECG

Authors

ŘEDINA, R.; HEJČ, J.; FILIPENSKÁ, M.; STÁREK, Z.

Released

6. 4. 2025

Publisher

NATURE PORTFOLIO

Location

BERLIN

ISBN

2045-2322

Periodical

Scientific Reports

Year of study

15

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

15

Pages count

15

URL

https://www.nature.com/articles/s41598-025-90533-y#Sec23

Full text in the Digital Library

http://hdl.handle.net/11012/250890

BibTex

@article{BUT197662,
  author="Richard {Ředina} and Jakub {Hejč} and Marina {Filipenská} and Zdeněk {Stárek}",
  title="Analyzing the performance of biomedical time-series segmentation with electrophysiology data",
  journal="Scientific Reports",
  year="2025",
  volume="15",
  number="1",
  pages="1--15",
  doi="10.1038/s41598-025-90533-y",
  issn="2045-2322",
  url="https://www.nature.com/articles/s41598-025-90533-y#Sec23"
}