<![CDATA[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.]]>
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