Sudden cardiac death (SCD) generally applied to an unpredicted death from a cardiovascular cause in a subject with or without preexisting heart disease. The main goal of this study was analyzing the Electrocardiogram (ECG) signal to design an algorithm to predict SCD risk. In this paper, ECG signals of 23 subjects (13 males, 8 females and 2 unknown), ranging from 17 to 89 years old necessary for the research were obtained from the Physionet database. For this purpose, we developed a new method to predict SCD, a 10-min prior heart attack using the return map. The aim of this study is a novel method based on Lag return map for in control patients and SCD classes. Return map with six different lags (1–6) was constructed in two-time intervals. After that, the non-linear features that include SD1, SD2, SD1/SD2 for each Lag was measured. The result shows that the rate of changes in SD1 and SD1/SD2 with increasing lags were increased significantly but in SD2 with increasing lags was decreased in two intervals. Statistical analysis indicates that return map parameters show changes in the transition to death episode (p < 0.05). Besides, there were significant changes (p < 0.01) in closer segments to death. In conclusion, it will be possible to predict SCD based on the nonlinear feature that can alarm doctors of an imminent SCD, helping them provide timely treatments that can increase the survival rate of patients and thus reduce the mortality rate.

突发性心脏死亡（SCD）通常适用于患有或不患有先前存在的心脏病的受试者中因心血管原因导致的意外死亡。这项研究的主要目的是分析心电图（ECG）信号，以设计一种预测SCD风险的算法。在本文中，从Physionet数据库中获得了研究所需的17至89岁的23位受试者（13位男性，8位女性和2位未知）的ECG信号。为此，我们开发了一种新的预测SCD的方法，即使用返回图在10分钟之前发生心脏病发作。这项研究的目的是针对对照患者和SCD类的基于Lag返回图的新方法。在两次间隔中构造了具有六个不同滞后（1-6）的返回图。此后，测量每个滞后包括SD1，SD2，SD1 / SD2的非线性特征。结果表明，SD1和SD1 / SD2随滞后增加的变化率显着增加，而SD2随着滞后增加的变化率在两个间隔内减小。统计分析表明，返回图参数显示了向死亡发作过渡的变化（p  ＜0.05）。此外， 死亡的更近阶段也有显着变化（p <0.01）。总之，将有可能基于非线性特征预测SCD，该非线性特征可以警告即将发生SCD的医生，帮助他们提供及时的治疗，以增加患者的存活率，从而降低死亡率。