A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Gupta, U and Paluru, N and Nankani, D and Kulkarni, K and Awasthi, N (2024) A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. In: Heliyon, 10 (5).

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Abstract

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients. © 2024 The Author(s)

Item Type:	Journal Article
Publication:	Heliyon
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to authors.
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	22 May 2024 03:32
Last Modified:	22 May 2024 03:32
URI:	https://eprints.iisc.ac.in/id/eprint/84817

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