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ECG signal classification and arrhythmia detection using ELM-RNN

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Abstract

Arrhythmia is a unique type of heart disease which produces inefficient and irregular heartbeat. This is a cardiac disease which is diagnosed through electrocardiogram (ECG) procedure. Several studies have been focused on the speed and accuracy on the learning algorithm by applying pattern recognition, artificial intelligence in the classification algorithm. In this work a novel classification algorithm is planned based on ELM (Extreme Learning Machine) with Recurrent Neural Network (RNN) by using morphological filtering. The popular publicly available ECG arrhythmia database (MIT-BIH arrhythmia DB) is used to express the performance of the proposed algorithm where the level of accuracy is compared with the existing similar types of work. The comparative study shows that performance of our proposed model is much faster than the models working with RBFN (radial basis function network), BPBB(back propagation neural network) and Support Vector Machine. The experimental result with the MIT BIH database with hidden neurons of ELM with RNN, the accuracy is 96.41%, sensitivity 93.62% and specificity 92.66%. The classification methodology follows main four steps the heart beat detection, the ECG feature extraction, feature selection and the construction of the proposed classifier.

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Authors and Affiliations

  1. Research Scholar, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Lucknow, Uttar Pradesh, India

    Sumanta Kuila & Subhankar Joardar

  2. Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow, India

    Namrata Dhanda

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  1. Sumanta Kuila
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  2. Namrata Dhanda
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  3. Subhankar Joardar
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Correspondence to Sumanta Kuila.

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Kuila, S., Dhanda, N. & Joardar, S. ECG signal classification and arrhythmia detection using ELM-RNN. Multimed Tools Appl 81, 25233–25249 (2022). https://doi.org/10.1007/s11042-022-11957-6

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