Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks

TL;DR

This paper presents a novel deep learning approach combining CNNs and LSTMs for automatic detection and classification of cardiac arrhythmias from ECG data, achieving high accuracy without explicit feature selection.

Contribution

It introduces a new hybrid CNN-LSTM model for arrhythmia detection that improves accuracy over existing methods using end-to-end learning.

Findings

Achieved an F-measure of 0.83 on cross-validation

Attained 0.80 F-measure on hidden challenge data

Demonstrated effectiveness without explicit feature selection

Abstract

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10-0.015 on the held-out test data (mean-standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.