Abstract
Ventricular tachyarrhythmia (VTA), mainly ventricular tachycardia (VT) and ventricular fibrillation (VF) are the major causes of sudden cardiac death in the world. This work uses deep learning, more precisely, LSTM and biLSTM networks to predict VTA events. The Spontaneous Ventricular Tachyarrhythmia Database from PhysioNET was chosen, which contains 78 patients, 135 VTA signals, and 135 control rhythms. After the pre-processing of these signals and feature extraction, the classifiers were able to predict whether a patient was going to suffer a VTA event or not. A better result using a biLSTM was obtained, with a 5-fold-cross-validation, reaching an accuracy of 96.30%, 94.07% of precision, 98.45% of sensibility, and 96.17% of F1-Score.
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Barbosa, D., Pires, E.J.S., Leite, A., Oliveira, P.B.d.M. (2023). Prediction of Ventricular Tachyarrhythmia Using Deep Learning. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_5
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DOI: https://doi.org/10.1007/978-3-031-32029-3_5
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