# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 1 Introduction  
The electrocardiogram (ECG) is a standard tool in clinical practice due to its low cost, quick measurement time, and simplicity. ECG is a noninvasive tool, meaning using it does not require any cut or incision; the only requirement is placing the electrodes in specific positions on the body with or without gel for better conductivity. Also, ECG data acquisition is relatively quick and easy if performed with a professional physician; simultaneously, since it captures the heart's activity, even slight abnormality keeps a trace in the ECG. It provides physiological, electrical, and mechanical activities of the heart for a specific window of time, which contains vast information, including symptoms for different diseases or heart conditions. Besides being user-friendly to doctors, ECG's usefulness in medical diagnosis is very significant in the case of diagnosing heart conditions such as arrhythmia, heart attack, stroke, heart failure, etc., which are all prevalent worldwide. Even when it comes to monitoring the heart condition of elderly or heart patients, ECG is the primary choice for frequent measurement. Also, while treating any patient during surgery or any medical procedure, keeping track of ECG is a standard preparation to monitor vital health. Even for preoperative evaluation, ECG is popular to ensure no underlying heart conditions might change the course of action during or before the operation. Until now, apart from the heart rate calculation, the other types of ECG diagnosis typically depend on the physician's expertise, so consistency and accuracy of the diagnosis varies a lot [1]. ECG has expanded beyond its conventional use and has shown significant promise in diagnosing illnesses unrelated to the heart. Notably, by spotting abnormal breathing patterns while sleeping, ECG data have proven useful in detecting sleep apnea. ECG measurements can be used in epilepsy diagnosis to identify specific electrical patterns and seizures. Furthermore, pulmonary illnesses can be detected by ECG since variations inwaveform features might indicate diseases such as chronic obstructive pulmonary disease (COPD). Surprisingly, heart rate variability analysis helps determine stress levels and emotional reactions. Therefore, the ECG's scope also includes emotional states. This wide-ranging use of ECG highlights its adaptability as a diagnostic tool, moving beyond its originally cardiac-centric focus. Healthcare practitioners glean vital insights into various physiological and psychological conditions by harnessing the heart's electrical signals. The use of ECG technology in non-cardiac diagnoses may increase as that technology develops, possibly changing medical procedures. This flexibility highlights the value of the ECG as a multifaceted tool for developing thorough and informative healthcare diagnostics across various medical specialties. Computer-aided diagnosis based on heart rate has been widely used for several years, depending on the device's rules or pattern recognition algorithm. However, with the advancement of artificial intelligence, such as machine learning and deep learning algorithms, the ECG data, image, and the features acquired from it can be used as input and classified with different output classes, such as different heart disease and other health conditions [2; 3; 4; 5]. Recently, researchers trained and tested computer models that could interpret ECG patterns using internet data. When given the same input data as clinicians, the algorithms could identify some cardiac problems with the same level of accuracy [5; 6]. However, there are still some challenges keeping the model or algorithms from being used in clinical settings, such as accuracy, reliability, consistency, interpretability, etc. [7; 8; 9; 10; 11; 12]. Unless these shortcomings are solved, the experimental accuracy or good performance metrics found by the researchers with a chosen dataset will not impact real life.