Arrhythmias such as Atrial Fibrillation (A_fib), Atrial Flutter (A_fl), and Ventricular Fibrillation (V_fib) are early indicators of Stroke and Sudden Cardiac Death, which are significant causes of death globally. Therefore, it is vital to detect patients with these conditions early. Manual inspection of ECG signals is tedious, time-consuming, and is limited by inter-observer variabilities. Further, it is challenging to accurately differentiate several types of arrhythmias in complex non-linear ECG signals. Computer-aided Decision Support Systems (CDSS) could be valuable in such a scenario. The CDSS uses machine learning techniques to learn the subtle differences in these rhythms and can be used for fast, accurate, repeated, and objective classification of arrhythmias. A novel CDSS has been proposed for the discrimination of normal rhythm (N_sr) from A_fib, A_fl, and V_fib using machine learning techniques. Predictive models have been developed for ECG segments of two durations: 2 s and 5 s. The number of samples from each of the four classes were balanced using synthetically generated samples with the ADASYN technique. Third-order cumulant images were determined from the ECG segments. 18 non-linear features, including entropies and other texture-based features, were extracted from the cumulant images, and significant features were selected using the t-test. The selected features were used to train several classifiers.On evaluating several different classifiers with the significant features using tenfold stratified cross-validation, the Random Forest classifier consistently performed better for both two and five second ECG duration studies. An accuracy of 98.2%, sensitivity of 98.1%, and specificity of 99.4% were obtained for the 2-s dataset. For the 5-s dataset, the accuracy, sensitivity, and specificity were 98.8%, 98.8%, and 99.6%, respectively. Due to the intermittent occurrence of arrhythmia, analysis of longer duration ECG signals will help detect the onset of critical episodes of arrhythmia more accurately. Since the proposed predictive models work effectively in detecting arrhythmia in two or five second ECG segments rather than single ECG beats, they have better clinical adaptability and can be incorporated into clinical monitoring systems.

心律不齐，例如房颤（A _fib），房扑（A _fl）和心室颤动（V _fib））是中风和心源性猝死的早期指标，是全球范围内重要的死亡原因。因此，至关重要的是及早发现这些情况的患者。手动检查ECG信号很繁琐，耗时，并且受观察者间差异的限制。此外，在复杂的非线性ECG信号中准确地区分几种类型的心律不齐具有挑战性。在这种情况下，计算机辅助决策支持系统（CDSS）可能很有价值。CDSS使用机器学习技术来学习这些节律中的细微差别，并可用于快速，准确，重复和客观的心律失常分类。一种新颖的CDSS已经提出了正常的节律（N的鉴别_SR）从A _FIB，A _FL，和V _fib使用机器学习技术。已经为两个持续时间：2 s和5 s的ECG段开发了预测模型。使用ADASYN技术通过合成生成的样本来平衡来自四类样本的样本数量。从ECG段确定三阶累积量图像。从累积图像中提取了18个非线性特征，包括熵和其他基于纹理的特征，并使用t检验选择了重要特征。选定的特征用于训练多个分类器。使用十倍分层交叉验证对具有显着特征的几个不同分类器进行评估时，随机森林分类器在2秒和5秒ECG持续时间研究中始终表现更好。准确性为98.2％，灵敏度为98.1％，特异性为99。2-s数据集获得4％。对于5秒数据集，准确性，敏感性和特异性分别为98.8％，98.8％和99.6％。由于心律不齐的间歇性发生，对较长时间的心电图信号进行分析将有助于更准确地检测出心律失常的关键发作。由于建议的预测模型可以有效地检测出两到五秒钟的心电图节律，而不是单个心电图节律，因此它们具有更好的临床适应性，可以纳入临床监测系统。