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 em...

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Bibliographic Details
Published in:IEEE sensors letters 2020-08, Vol.4 (8), p.1-4
Main Authors: Ganguly, Biswarup, Ghosal, Avishek, Das, Anirbed, Das, Debanjan, Chatterjee, Debanjan, Rakshit, Debmalya
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia
automated detection
Automation
Cardiac arrhythmia
Classification
deep learning (DL)
Electrocardiography
Feature extraction
Fractals
Heart beat
Machine learning
Measurement
Sensor applications
Short term
signal processing
Testing
Training
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Summary: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.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2020.3006756