Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

Early stage heartbeat classification using the electrocardiogram signals can prevent cardiovascular diseases that causes millions of deaths annually around the world. In the past, researchers have used deep neural networks to achieve significant performance for heartbeat classification but their black-box nature and prediction rationale limits real-world deployment. We propose a Penalty Induced Prototype based eXplainable Residual Neural Network (PIPxResNet) that addresses the black-box nature of deep neural networks. PIPxResNet encodes the temporal variations of heartbeats by employing pretrained residual neural network following the concept of task transfer learning. The algorithm further extracts prototypes that are most representative of the training dataset that explain model predictions to general physicians, making them clinically relevant. The prototypes of a particular class having close resemblance to other class prototypes are penalised and their contribution towards corresponding class is reduced. In addition, the classification performance is improved by synthesising regular and irregular heartbeats using a deep convolution conditional generative adversarial network. The proposed method can easily be adopted to other domains that requires explanations for the classification tasks. The PIPxResNet performs at par with existing state-of-the-art algorithms without compromising individual class performance when tested on four publicly available annotated datasets. The proposed model is capable to perform automated screening and provide medical attention by simulating a clinical decision support system for general physicians.