Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network

This letter proposes an automated detection and classification of arrhythmia from the electrocardiogram (ECG) signals to employ deep learning (DL) framework based on long short-term memory (LSTM) network. Instead of using the classical LSTM network, a feature-based bidirectional LSTM (bi-LSTM) is employed, where a unidirectionally processed multifractal detrended fluctuation analysis is used to extract suitable features. The online available ECG signals are examined using multifractal parameters to study its nonlinear, stochastic, and complex fluctuations. A feature set comprising of ten features has been extracted from the segmented ECG beats followed by feeding to a single layer bi-LSTM network. Experimental results reveal that the feature-based bi-LSTM network outperforms the state-of-the-art DL methods compared on the same dataset. The proposed algorithm is a generic one and can be used for any computer-aided diagnosis of cardiovascular diseases.