Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms

M. D. Oudkerk Pool, B. D. de Vos, M. M. Winter, I. Isgum

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

Low-cost wearables with capability to record electrocardiograms (ECG) are becoming increasingly available. These wearables typically acquire single-lead ECGs that are mainly used for screening of cardiac arrhythmias such as atrial fibrillation. Most arrhythmias are characteruzed by changes in the RR-interval, hence automatic methods to diagnose arrythmia may utilize R-peak detection. Existing R-peak detection methods are fairly accurate but have limited precision. To enable data-point precise detection of R-peaks, we propose a method that uses a fully convolutional dilated neural network. The network is trained and evaluated with manually annotated R-peaks in a heterogeneous set of ECGs that contain a wide range of cardiac rhythms and acquisition noise. 700 randomly chosen ECGs from the PhysioNet/CinC challenge 2017 were used for training (n=500), validation (n=100) and testing (n=100). The network achieves a precision of 0.910, recall of 0.926, and an F1-score of 0.918 on the test set. Our data-point precise R-peak detector may be important step towards fully automatic cardiac arrhythmia detection.Clinical relevance- This method enables data-point precise detection of R-peaks that provides a basis for detection and characterization of arrhythmias.

Cite this