Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms

TL;DR

This paper presents a compact deep learning system using RR-interval framed ECG data for arrhythmia detection, suitable for wearable devices, achieving performance comparable to traditional methods with minimal dataset requirements.

Contribution

The study introduces a novel, lightweight CNN-based arrhythmia detection system that requires only R-peak data, enabling real-time monitoring without complex feature extraction.

Findings

CADS matches conventional detection system performance

System is suitable for wearable and real-time applications

Features are fully implemented and publicly available

Abstract

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detection. This compact system can be implemented in wearable devices or real-time monitoring equipment because the feature extraction step is not required for complex ECG waveforms, only the R-peak data is needed. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs. All features of the CADS are fully implemented and publicly available in MATLAB.