Cardiovascular diseases is one of the leading causes of death; arrhythmia is especially dangerous if it is not diagnosed in time. Electrocardiograms (ECGs) are very useful in diagnosing heart diseases as they give full information of the electrical conduction system of the heart. Up till now, conventional ECG analysis entails cumbersome manual procedures, high likelihood of error, and skills that are difficult to come by, hence the inability to diagnose fundamental arrhythmias correctly and more so in real-time or in large-scale studies. The following are the challenges that this thesis aims at solving them by developing an automated ECG analysis system based on deep learning technology. At the center of the proposed system is a Convolutional Neural Network, which is capable of training the raw ECG data to identify various types of arrhythmias while consuming minimum time. In addition, the system applies state of the art approaches, such as CNN, and attention mechanism to improve both spatial and temporal patterns recognition of ECG signal. The implementation of the system was performed and the process of training and validation of the system was done on the database known as the MIT-BIH Arrhythmia Database which is a standard benchmark for the ECG analysis. From the results, presented in the tables, it can be stated that the accuracy of the proposed model is 0.99087 per cent increase in arrhythmia detection accuracy and a precision of 0.99079, recall of 0.99087, and an F1 score of 0.99078 over more than one arrhythmia class. The addition of attention mechanisms and hybrid architecture allowed the model to detect less complex abnormalities such as premature ventricular contractions better. Furthermore, the performance of the system is extensively evaluated using the MIT-BIH NSTDB to ensure that the system performs well with real-life situations where there may be interference from different types of noises. Therefore, the proposed deep learning-based system is a huge improvement in automated ECG analysis. In this system, the accuracy and efficiency of the detection of arrhythmias are highly enhanced and may lead to an increased efficiency in the early diagnosis and treatment of such diseases; hence an improvement of patient’s well-being in the management of such conditions.
