Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.

心律不齐是心律不齐或心跳过慢或过快时的一种情况。它是由于协调心跳的不适当电脉冲而发生的。由于某些危险的心律不齐状况，可能会导致心脏骤然死亡。因此，心电图（ECG）分析的主要目的是准确检测威胁生命的心律失常，并进行适当的治疗，以挽救生命。自最近十年以来，已报道了几种用于自动心电图搏动分类的方法。在这项工作中，我们对当前使用ECG信号检测心律不齐的最新方法进行了系统综述。它包括用于自动检测和决策过程的信号分解，特征提取和机器学习方法。文章涵盖了预处理，检测QRS复合波，特征提取和心电图搏动分类。根据过去的研究，可以理解，对于心律不齐的实时检测，非常需要使用计算机辅助决策过程的自动化方法。讨论了不同方法的优点和局限性，并在有效检测心律不齐的过程中强调了未来的范围。这项研究可能有助于研究人员分析用于检测心律不齐状况的现有技术。讨论了不同方法的优点和局限性，并在有效检测心律不齐的过程中强调了未来的范围。这项研究可能有助于研究人员分析用于检测心律不齐状况的现有技术。讨论了不同方法的优缺点，并在有效检测心律不齐的过程中强调了未来的范围。这项研究可能有助于研究人员分析用于检测心律不齐状况的现有技术。