Background and Objective: Malignant ventricular arrhythmias (MAs) occur unpredictably and lead to emergencies. A new approach that uses a timely tracking device e.g., photoplethysmogram (PPG) solely to predict MAs would be irreplaceably valuable and it is natural to expect the approach can predict the occurrence as early as possible. Method: We assumed that with an appropriate metric based on signal complexity, the heartbeat interval time series (HbIs) can be used to manifest the intrinsic characteristics of the period immediately precedes the MAs (preMAs). The approach first characterizes the patterns of preMAs by a new complexity metric (the refined composite multi-scale entropy). The MAs detector is then constructed by checking the discriminability of the MAs against the sinus rhythm and other prevalent arrhythmias (atrial fibrillation and premature ventricular contraction) of three machine-learning models (SVM, Random Forest, and XGboost). Results: Two specifications are of interest: the length of the HbIs needed to delineate the preMAs patterns sufficiently ($l_{spec}$) and how long before the occurrence of MAs will the HbIs manifest specific patterns that are distinct enough to predict the impending MAs ($t_{spec}$). Our experimental results confirmed the best performance came from a Random-Forest model with an average precision of 99.99% and recall of 88.98% using a HbIs of 800 heartbeats (the $l_{spec}$), 108 seconds (the $t_{spec}$) before the occurrence of MAs. Conclusion: By experimental validation of the unique pattern of the preMAs in HbIs and using it in the machine learning model, we showed the high possibility of MAs prediction in a broader circumstance, which may cover daily healthcare using the alternative sensor in HbIs monitoring. Therefore, this research is theoretically and practically significant in cardiac arrest prevention.

背景与目的：恶性室性心律失常（MAs）的发生难以预测并导致紧急情况。仅使用及时的跟踪设备（例如光电容积描记图（PPG））来预测MA的新方法将具有不可替代的价值，很自然地希望该方法可以尽早地预测事件的发生。方法：我们假设使用基于信号复杂度的适当指标，心跳间隔时间序列（HbIs）可用于显示紧接在MA（preMAs）之前的周期的固有特征。该方法首先通过新的复杂性度量（改进的复合多尺度熵）来表征preMA的模式。然后通过检查三种机器学习模型（SVM，Random Forest和XGboost）相对于窦性心律和其他普遍的心律失常（心房颤动和室性早搏）的可分辨性来构造MAs检测器。结果：有两个规格值得关注：充分描绘preMAs模式所需的HbI的长度（${升}_{spËC}$）以及HbIs会在MA发生前多长时间显现出足以预测即将到来的MA的特定模式（${Ť}_{spËC}$）。我们的实验结果证实，最佳性能来自随机森林模型，其平均精度为99.99％，使用800个心跳的HbI（召回率为88.98％）${升}_{spËC}$），108秒（ ${Ť}_{spËC}$）在发生MA之前。结论：通过对HbIs中preMAs独特模式的实验验证并在机器学习模型中使用它，我们显示了在更广泛的情况下进行MAs预测的可能性很高，这可能涵盖在HbIs监测中使用替代传感器进行日常医疗保健。因此，这项研究在预防心脏骤停方面具有理论和实践意义。