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Home > Archives > Volume 20, No 7 (2022) > Article

DOI: 10.14704/nq.2022.20.7.NQ33431

Autonomous Detection of Cardiac Ailments using Long-short term Memory Model based on Electrocardiogram signals

L. Alekhya, P. Rajesh Kumar, A. Venkata Sriram


A Recurrent Neural Network (RNN) models are widely used for automation among the latest emerging technologies of Artificial Intelligence. Because of their ability to process temporal data they are applied for Cardiac ailments detection based on the hand-crafted feature set obtained by processing Electrocardiogram (ECG) of the ailments. In this work ECG data from MIT-BIH database of three cardiac ailments such as Arrhythmia, Atrial Fibrillation and Congestive heart failure were considered to detect and classify these ailments from normal signals using Long short-term memory (LSTM) which is an algorithm of RNN considered in this work to classify the cardiac ailments. Maximal overlap discrete wavelet packet transform (MODWPT) is used to transform and process the ECG signal of ailments and detect the characteristic waves (P, QRS, T) of the ECG signal. Various hand-crafted features are determined from characteristic waves and given as input to train LSTM architecture. It classifies the considered cardiac ailments with Accuracy and Mean of MCC (Mathew’s Correlation coefficient) of around 96.11% and 94.83%, The proposed technique performs better in terms of accuracy with existing state-of-the-art methods.


Recurrent Neural Network, Long short-term memory, Maximal Overlap Discrete wavelet packet transform, Mathew’s Correlation Coefficient.

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