ArticleSensors (Basel, Switzerland)2022
Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification.Frontiers in physiology · 2026Article
- Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.Diagnostics (Basel, Switzerland) · 2025Article
- Utilizing Nanomaterials in Microfluidic Devices for Disease Detection and Treatment.Nanomaterials (Basel, Switzerland) · 2025Review
- Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram.PeerJ. Computer science · 2024Article
- ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems.Bioengineering (Basel, Switzerland) · 2023Article
- A Model to Predict Heartbeat Rate Using Deep Learning Algorithms.Healthcare (Basel, Switzerland) · 2023Article
- A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques.Bioinformatics and biology insights · 2023Review
- Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12Digital healthArticle
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Authors and funding
6 authors.
Funding
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Abstract
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
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Registered trials
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