Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.

中文翻译：

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使用机器学习预测心室颤动的心率变异性数据的最佳长度和预测时间

据世界卫生组织称，心室颤动（VF）是一种心血管疾病，是全世界死亡的主要原因之一。心率变异性 (HRV) 是一种生物标志物，用于检测和预测危及生命的心律失常。提前预测室颤的发生对于挽救患者免于猝死具有重要意义。我们从 7 个 HRV 数据长度中提取特征来预测 9 个不同预测时间之前的 VF 发作，并观察预测准确性。仅使用五个特征，就可以基于 10 倍交叉验证来训练和验证人工神经网络分类器。在 HRV 数据长度为 10 秒和 20 秒、预测时间为 0 秒时，最大预测精度分别为 88.18% 和 88.64%。最差的预测精度是在 HRV 数据长度为 70 秒、预测时间为 80 秒时记录的。我们的结果表明，从 VF 起始点附近的 HRV 信号中提取的特征可以产生相对较高的 VF 预测精度。