A Systematic Review on Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases: Challenges and Opportunities

Cardiovascular health-related problem is a rapidly increasing integrated field concerning the processing and fetching the information from cardiovascular systems for early detection and treatment of cardiovascular diseases. Artificial Intelligence (AI) techniques, especially machine and deep learning techniques are more impactful and powerful tools for upgrading the capabilities of an application, and they have been applied to medical data for analysis and disease detection purposes. This paper, represents a comprehensive view of AI-based computational modeling with the abilities of powerful AI techniques that can play a crucial role in developing smart and enhanced systems in a real-world application. The paper outlines the broad overview of AI-based modeling that can be utilized in various application domains. An electrocardiogram (ECG) plays a major role in biomedical applications to record the heartbeat activity. Regular monitoring of ECG through wearable devices like the band, watches, etc. can be done for early detection of cardiovascular diseases. The competency of each method discussed is related to ECG classification approaches that have been compared in terms of some parameters like accuracy, sensitivity, specificity, positive predictivity, and F-score. The noise affects the ECG signal which may deteriorate the features of the respective signal that leading to improper treatment. De-noising has been done by pre-processing of the signal, which enables the prediction of the heart condition. After detecting the positions of the P wave, QRS wave, and T wave, feature extraction has been done. The efficiency of ECG classification with different computational methods was evaluated with proposed algorithms by using different databases like MIT-BIH, PTB, and MIT-BIH Atrial Fibrillation test. It has been observed from the literature that Convolutional Neural Networks (CNN) are best suited for the classification and detection of arrhythmia. The challenges of existing techniques to analyze the ECG signal for the classification and detection of arrhythmia are summarized.