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Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals

Mahendra Kumar Gourisaria, Harshvardhan GM, Rakshit Agrawal, Sudhansu Shekhar Patra, Siddharth Swarup Rautaray, Manjusha Pandey

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)12(6)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799867517|DOI: 10.4018/IJEHMC.20211101.oa9

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MLA

Gourisaria, Mahendra Kumar, et al. "Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals." IJEHMC vol.12, no.6 2021: pp.1-24. http://doi.org/10.4018/IJEHMC.20211101.oa9

APA

Gourisaria, M. K., Harshvardhan GM, Agrawal, R., Patra, S. S., Rautaray, S. S., & Pandey, M. (2021). Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-24. http://doi.org/10.4018/IJEHMC.20211101.oa9

Chicago

Gourisaria, Mahendra Kumar, et al. "Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.6: 1-24. http://doi.org/10.4018/IJEHMC.20211101.oa9

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Abstract

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.