# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 1 Introduction  
Cardiovascular diseases (CVDs) are common chronic diseases that pose major threats to human health [1]. Electrocardiogram (ECG) is a kind of noninvasive technique that records the fluctuation of the heart's bio-electric activities. The phenomena of cyclical contractions and relaxations of the heart could be tracked by an ECG machine through electrodes placed on the patient's skin surface. Normal ECG signals consist of different types of waves, including T wave, P wave, and QRS complex. The statistical and morphological characteristics of those ECG waves are important health indicators that could reveal symptoms of heart-related health issues [2]. For example, the absence of P-waves and an irregular ventricular rate in ECG signals could relate to atrial fibrillation (AF) [3]. In daily medical routine, to identify heart abnormalities and provide effective treatment for those issues, cardiologists usually perform ECG screening for patients, which requires significanthuman efforts and expensive medical procedures. Due to the population aging, the number of patients having cardiovascular diseases is expected to increase explosively, which calls for efficient, accurate, and low-cost automatic ECG diagnosis [4]. In this review, we focus on the classification of heart arrhythmias, i.e., irregular heartbeats, which is a common medical procedure to identify CVDs. Deep learning (DL) has shown remarkable success in medical diagnosis and has been exploited for automatic heart abnormality classification with ECG signals in recent years. The mapping from ECG features to their corresponding medical categories is learned, which can be characterized by DL models consisting of multiple perception neural layers. The inference capability of the DL model is optimized by a training process with training datasets [5], where the neuron weights are optimized to minimize the mismatch between the inferred and the ground-truth categories of the training data. Compared to traditional machine learning-based classification methods such as clustering and support vector machine (SVM), the DL-based ECG classification could better map the characteristics of ECG signals to their corresponding categories thanks to its powerful multi-level abstraction capability of feature extraction [6]. In this work, the studies which consider DL-based arrhythmia classification with ECG signals are reviewed. The diagnosis for different arrhythmia types is a different clinical problem for cardiologists in practice. However, from the perspective of the classification task with DL, the classification methods for arrhythmia categories share an identical context and goal, i.e., establishing the accurate mapping from ECG characteristics to corresponding categories. Hence, this survey focuses on the current research status, challenges, and research opportunities for deep learning-based arrhythmia classification overall. According to [7], clinical trials of artificial intelligence-enhanced ECG (AI-ECG) diagnosis have been conducted at the Mayo Clinic for the detection of various cardiovascular diseases, which has demonstrated the potential benefit of AI-ECG. However, they conclude that the implementation of the AI-ECG diagnosis is still in its infancy. Hence, although DL techniques have proven their effectiveness for ECG classification in the research community, their applications in the practical clinical process have been limited due to challenges both from the perspectives of DL techniques and ECG data. For example, in the inter-patient paradigm, DL models need to infer arrhythmia types based on ECG signals from patients who are not included in the training process, which is more challenging than the intra-patient paradigm where the models could experience the same patients during both of the training and inference stages.