VENTRICULAR TACHYCARDIA PREDICTION THROUGH DEEP LEARNING: ENHANCING CARDIAC MONITORING

Authors

Rekha Ravindran
Assistant Professor (SG), Department Biotechnology, Rajalakshmi Engineering College, Thandalam
R. Bharathi
Assistant Professor, Department of Information Technology, M.kumarasamy College of Engineering, Thalavapalayam, Karur-639113,
Khil Mathew Philip
Assistant Professor, Department of Computer Applications, Saintgits College of Engineering Kottayam
J. Bhavana
Associate Professor, Department of CS & AI, SR University
N. Venkatesh
Assistant Professor, School of CS & AI-CSE, S R University-Warangal-506371
T. P. S. Kumar Kusumanchi
Assistant professor, Department of IoT, Koneru Lakshamaiah Education Foundation, Green fields, Vaddeswaram, Guntur, AP, India-522502
Jegajothi B
Research Associate, SRS Tech Solutions, Chennai
DOI: https://doi.org/10.52549/ijeei.v13i4.6603
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Published: 11-12-2025 • Issue: Vol. 13 No. 4: December 2025 • Section: Regular paper

Abstract

Ventricular Tachycardia (VT) is a life threatening arrhythmia that needs to be detected early and correctly to avoid cardiac arrest. In this paper, the authors hypothesise a hybrid deep learning model based on WaveNet, Swin Transformer, and MISH activation function to make powerful predictions of VTs on the basis of ECG signals in the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). The preprocessing pipeline will consist of wavelet-based denoising, min-max normalization and HRV feature. WaveNet has the ability to capture short-term temporal variations whilst the Swin Transformer considers global relations via hierarchical attention. The suggested approach has excellent performance over baseline models, having accuracy, precision, recall, and F1-score of 97.57, 96.89, 97.42, and 97.15, respectively. The improved capability of the model to detect VT with a low number of false negative results shows that the model could be used in realtime cardiac monitoring and clinical decision support. The next steps to be considered in the future research will be the model optimization of wearable devices and testing on multi-center ECG data.
Keywords: Ventricular Tachycardia; ECG Classification; Machine Learning; Deep Learning; Confusion Matrix; Cardiac Arrhythmia Detection; Feature Extraction; Predictive Analytics

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How to Cite

[1]
R. Ravindran et al., “VENTRICULAR TACHYCARDIA PREDICTION THROUGH DEEP LEARNING: ENHANCING CARDIAC MONITORING”, IJEEI, vol. 13, no. 4, pp. 968–980, Dec. 2025, doi: 10.52549/ijeei.v13i4.6603.