Key result
The proposed CNN-BiLSTM deep learning model achieved an accuracy of 99.37% and sensitivity of 98.15% in classifying ventricular tachycardia and ventricular fibrillation from ECG signals.
Why the study?
Early identification of ventricular tachyarrhythmia risk is essential to improve clinical diagnostic systems and prevent life-threatening complications such as cardiac arrest.
Does a CNN-BiLSTM deep learning model improve the accuracy of identifying ventricular tachyarrhythmia from ECG signals?
Does a CNN-BiLSTM deep learning model improve the accuracy of identifying ventricular tachyarrhythmia from ECG signals?
A novel CNN-BiLSTM deep learning model accurately classifies ventricular tachycardia and fibrillation from ECG signals with over 99% accuracy, potentially aiding early automated detection of life-threatening arrhythmias.
May facilitate automated VT/VF detection; leaves open prospective validation before clinical adoption.
An electrocardiogram (ECG) signal is used widely to detect ventricular tachyarrhythmia (VTA) and to diagnose heart disease. Deep learning models and large ECG data have made the diagnosis of VTA an attractive task to demonstrate the power of artificial intelligence in clinical applications. One of the life-threatening complications of VTA is cardiac arrest (CA). The VTA is divided into two categories: ventricular fibrillation (VF) and ventricular tachycardia (VT). Abnormal electrical activity in the ventricle causes VT, which leads to CA, whereas the chaotic electrical activity in the ventricle leads to VF. To improve the clinical diagnostic system and to help cardiologists, it is essential to identify the risk of VTA at an early stage. The goal of this paper is to develop an end-to-end (E2E) deep learning model that uses a convolution neural network (CNN) and a bidirectional long-short term memory network (BiLSTM) to classify VT and VF arrhythmias from multiple ECG databases. The CNN extracts features from ECG signals, and BiLSTM learns information. The ECG signals are acquired from the MIT-BIH malignant ventricular arrhythmia database (VFDB) and the Creighton University VTA database (CUDB). These ECG signals indicate that heart rate variability is a fast and dynamic event. Before the method's implementation, ECG signals are windowed at a fixed size according to annotation information and then normalized within each window. In terms of accuracy and sensitivity, the proposed CNN-BiLSTM deep learning model outperforms existing state-of-the-art methods. These results made it possible to obtain a relatively higher average accuracy (AC) of 99.37%, precision (PE) of 97.12%, a sensitivity (SE) of 98.15%, and F-score (FS) of 98.43%, and an overall accuracy of 99.07%, respectively.
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Ojha et al. (2021) studied Ventricular Tachyarrhythmia (n=12,355). CNN-BiLSTM deep learning model vs. Existing machine learning methods was evaluated on Classification accuracy for ventricular tachyarrhythmia (VT and VF) vs normal sinus rhythm. The proposed CNN-BiLSTM deep learning model achieved an accuracy of 99.37% and sensitivity of 98.15% in classifying ventricular tachycardia and ventricular fibrillation from ECG signals.
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