Millions of people worldwide are affected by arrhythmias. Arrhythmias are abnormal activity of the heart functioning. Some arrhythmias are harmful to the heart and can cause sudden mortality. The electrocardiogram (ECG) is a significant tool in cardiology for the diagnosis of arrhythmia beats. Computer-aided diagnosis (CAD) systems have been proposed in several studies to automatically classify different types of arrhythmias from ECG signals. To improve the classification of arrhythmias, a new end-to-end feature learning and classification model has been developed. This work focuses on the implementation of a one-dimensional convolution neural network (1D-CNN) model based on an auto-encoder convolution network (ACN) that learned the best ECG features from eachheartbeat window. After that, we applied a Support Vector Machine (SVM) classifier for auto-encode features in order to detect the four different types of arrhythmic beats, including normal beats. These arrhythmia beats are left bundle branch block (L), right bundle branch block (R), paced beats (P), and premature ventricular contractions (V). using the MIT-BIH arrhythmia database. The statistical performance of the model is evaluated using tenfold crossvalidation strategies and obtained as an overall accuracy of 98.84%, average accuracy of 99.53%, sensitivity of 98.24% and precision of 97.58%, respectively. This model has presents better results than other state-of-the-art models. Therefore, this approach may also help in clinical heart care systems.

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.