A feature-based bidirectional LSTM network outperformed state-of-the-art deep learning methods for the automated detection and classification of arrhythmia from ECG signals.
Does a feature-based bidirectional LSTM network improve the automated detection and classification of arrhythmia from ECG signals compared to state-of-the-art deep learning methods?
A novel feature-based bidirectional LSTM network improves automated arrhythmia detection and classification from ECG signals compared to existing deep learning methods.
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.
Ganguly et al. (Fri,) conducted a letter in Arrhythmia. Feature-based bidirectional LSTM (bi-LSTM) network vs. State-of-the-art deep learning methods was evaluated on Detection and classification of arrhythmia. A feature-based bidirectional LSTM network outperformed state-of-the-art deep learning methods for the automated detection and classification of arrhythmia from ECG signals.