JMIR Publications

ABSTRACT

Background:

Electrocardiogram (ECG) recording and interpretation is the most common method used for the diagnosis of cardiac arrhythmias, nonetheless this process requires significant expertise and effort from the doctors’ perspective. Automated ECG signal classification could be a useful technique for the accurate detection and classification of several types of arrhythmias within a short timeframe.

Objective:

To review current approaches using state-of-the-art CNNs and deep learning methodologies in arrhythmia detection via ECG feature classification techniques and propose an optimised architecture capable of different types of arrhythmia diagnosis using publicly existing annotated arrhythmia databases from the MIT-BIH databases available at PHYSIONET (physionet.org) .

Methods:

A hybrid CNN-LSTM deep learning model is proposed to classify beats derived from two large ECG databases. The approach is proposed after a systematic review of current AI/DL methods applied in different types of arrhythmia diagnosis using the same public MIT-BIH databases. In the proposed architecture the CNN part carries out feature extraction and dimensionality reduction, and the LSTM part performs classification of the encoded ECG beat signals.

Results:

In experimental studies conducted with the MIT-BIH Arrhythmia and the MIT-BIH Atrial Fibrillation Databases average accuracies of 96.82% and 96.65% were noted respectively.

Conclusions:

The proposed system can be used for arrhythmia diagnosis in clinical and mHealth applications managing a number of prevalent arrhythmias such as VT, AFIB, LBBB etc. The capability of CNNs to reduce the ECG beat signal’s size and extract its main features can be effectively combined with the LSTMs’ capability to learn the temporal dynamics of the input data for the accurate and automatic recognition of several types of cardiac arrhythmias. Clinical Trial: Not applicable.