Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Bartosz Puszkarski1

  • 1Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75, Warsaw, 00-662, Poland.

Physiological Measurement
|July 15, 2026
PubMed
Summary

Machine learning (ML) advances wearable electrocardiography (ECG) interpretation. Rigorous evaluation, diverse datasets, and reproducible preprocessing are key for reliable rhythm monitoring, not just novel algorithms.

Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification.

Physiological measurementยท2022
See all related articles

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Wearable and mobile electrocardiography (ECG) offers accessible rhythm monitoring but faces challenges in interpretation due to limited leads and motion artifacts.
  • Machine learning (ML) presents potential solutions for analyzing these complex ECG signals.

Purpose of the Study:

  • To review and synthesize recent ML approaches for wearable ECG analysis.
  • To identify key challenges and limitations in current ML applications for wearable ECG.
  • To provide a structured comparison of ML methods and guide future research.

Main Methods:

  • Systematic review of ML approaches for wearable ECG across signal-quality assessment (SQA), single-lead, and multi-lead interpretation.
  • Analysis of preprocessing pipelines, quality handling strategies, and 18 benchmark datasets.
Keywords:
ECG preprocessingarrhythmia detectiondeep learningedge deploymentsignal quality assessmentwearable ECG

Related Experiment Videos

  • Evaluation of architectural paradigms, dataset characteristics, and common evaluation deficiencies.

    • Main Results:

    • No single ML architecture consistently outperforms others; classical methods are competitive for rhythm tasks, while deep models offer better generalization with sufficient data.
    • Dataset characteristics (scale, demographics, free-living conditions) are stronger predictors of performance than architectural choice.
    • Recurring evaluation issues include data leakage, incomplete SQA reporting, and waveform similarity validation without diagnostic concordance.

    Conclusions:

    • Future progress in wearable ECG ML relies on improved evaluation rigor, representative datasets, and reproducible preprocessing.
    • Architectural novelty alone is insufficient; focus must shift to robust methodology and data quality.
    • Standardized evaluation protocols are crucial for reliable clinical translation of wearable ECG ML tools.