Cardiovascular diseases (CVDs) in India and globally are the major cause of mortality, as revealed by the World Health Organization (WHO). The irregularities in the pace of heartbeats, called cardiac arrhythmias or heart arrhythmias, are one of the commonly diagnosed CVDs caused by ischemic heart disease, hypertension, alcohol intake, and stressful lifestyle. Other than the listed CVDs, the abnormality in the cardiac rhythm caused by the long term mental stress (stimulated by Autonomic Nervous System (ANS)) is a challenging issue for researchers. Early detection of cardiac arrhythmias through automatic electronic techniques is an important research field since the invention of electrocardiogram (ECG or EKG) and advanced machine learning algorithms. ECG (EKG) provides the record of variations in electrical activity associated with the cardiac cycle, used by cardiologists and researchers as a gold standard to study the heart function. The present work is aimed to provide an extensive survey of work done by researchers in the area of automated ECG analysis and classification of regular & irregular classes of heartbeats by conventional and modern artificial intelligence (AI) methods. The artificial intelligence (AI) based methods have emerged popularly during the last decade for the automatic and early diagnosis of clinical symptoms of arrhythmias. In this work, the literature is explored for the last two decades to review the performance of AI and other computer-based techniques to analyze the ECG signals for the prediction of cardiac (heart rhythm) disorders. The existing ECG feature extraction techniques and machine learning (ML) methods used for ECG signal analysis and classification are compared using the performance metrics like specificity, sensitivity, accuracy, positive predictivity value, etc. Some popular AI methods, which include, artificial neural networks (ANN), Fuzzy logic systems, and other machine learning algorithms (support vector machines (SVM), k-nearest neighbor (KNN), etc.) are considered in this review work for the applications of cardiac arrhythmia classification. The popular ECG databases available publicly to evaluate the classification accuracy of the classifier are also mentioned. The aim is to provide the reader, the prerequisites, the methods used in the last two decades, and the systematic approach, all at one place to further purse a research work in the area of cardiovascular abnormalities detection using the ECG signal. As a contribution to the current work, future challenges for real-time remote ECG acquisition and analysis using the emerging technologies like wireless body sensor network (WBSN) and the internet of things (IoT) are identified.

心血管的正如世界卫生组织（WHO）所揭示的那样，印度和全球范围内的疾病（CVD）是造成死亡的主要原因。心律不齐的心律不齐，称为心律不齐或心律不齐，是由缺血性心脏病，高血压，饮酒和压力性生活方式引起的常见被诊断的CVD之一。除了列出的CVD外，由长期精神压力（由自主神经系统（ANS）刺激）引起的心律异常对于研究人员来说是一个充满挑战的问题。自从心电图（ECG或EKG）和先进的机器学习算法发明以来，通过自动电子技术对心律不齐进行早期检测是一个重要的研究领域。心电图（EKG）提供了与心动周期相关的电活动变化的记录，心脏病专家和研究人员将其用作研究心脏功能的黄金标准。本工作旨在提供对研究人员在自动心电图分析以及通过常规和现代人工智能（AI）方法对常规和不规则心跳类别进行分类方面所做研究的广泛调查。在过去的十年中，基于人工智能（AI）的方法已普遍出现，用于自动和早期诊断心律不齐的临床症状。在这项工作中，近二十年来对文献进行了研究，以回顾AI和其他基于计算机的技术的性能，以分析ECG信号来预测心脏（心律）疾病。使用诸如特异性，灵敏度，准确性，正预测值等性能指标对用于ECG信号分析和分类的现有ECG特征提取技术和机器学习（ML）方法进行了比较。一些流行的AI方法，包括人工神经网络（ANN），模糊逻辑系统和其他机器学习算法（支持向量机（SVM），k最近邻（KNN）等）在本次审查工作中被考虑用于心律失常分类的应用。还提到了可公开获得的用于评估分类器分类准确性的流行ECG数据库。目的是向读者提供先决条件，最近二十年来使用的方法以及系统的方法，所有这些都集中在一个地方，以进一步开展使用ECG信号进行心血管异常检测领域的研究工作。作为对当前工作的贡献，使用无线人体传感器网络（WBSN）和物联网（IoT）等新兴技术对实时远程ECG采集和分析提出了未来的挑战。