This paper aims to early arrhythmia prediction and investigate the use of robust adaptive filters to forecast the ECG signal. Different robust adaptive filters are examined for ECG prediction. Features in time and time-frequency domains have been extracted, and the Hurst index has been calculated in two domains. The performance of the SVM, KNN, and the ensemble of LogitBoost trees for model construction has been examined for detecting the occurrence of an arrhythmia in the predicted ECGs in an inter-patient scenario. Results show that pseudo-Huber adaptive filter is the best choice for ECG prediction. Also, classification performance measures besides the McNemar test show that the predicted signal is suitable to use for early arrhythmia detection with accuracy, precision, sensitivity, and specificity of at least 98\(\%\).

本文旨在早期心律失常的预测，并研究使用稳健的自适应滤波器来预测ECG信号。检查了用于ECG预测的不同鲁棒自适应滤波器。提取了时域和时频域中的特征，并在两个域中计算了Hurst指数。SVM，KNN和LogitBoost树的集成用于模型构建的性能已经过检查，以检测患者间场景中预测的ECG中心律失常的发生。结果表明，伪Huber自适应滤波器是心电图预测的最佳选择。此外，除McNemar测试外的分类性能指标表明，预测信号适用于早期心律失常检测，其准确度，精确度，灵敏度和特异性至少为98 \（\％\）。