Ventricular Tachycardia Prediction Through Deep Learning: Enhancing Cardiac Monitoring

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 real-time 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.