Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

中文翻译：

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基于utoencoder的心律失常检测和RRI数据修改，可进行精确的心率变异性分析

心率变异性是心电图（ECG）中RR间隔（RRI）的波动，已被广泛用于自主评估。由于从RRI数据中提取的HRV特征在发生心律不齐时很容易波动，因此在进行HRV分析之前，需要适当地修改具有心律不齐的RRI数据。在这项研究中，我们考虑了两种类型的收缩前期-室性早搏（PVC）和房性早搏（PAC）-这两种每天都会发生的收缩前期，即使在没有心血管疾病的健康人中也是如此。提出了异位RRI检测的统一框架和利用自动编码器（AE）类型的神经网络的修改算法。拟议的框架包括根据RRI数据检测出收缩前期事件并进行修改，目标是PVC和PAC。在检测阶段，通过AE实时监视RRI数据，并且降噪自动编码器（DAE）修改由检测到的收缩期引起的异位RRI。这些分别称为基于AE的收缩前期检测（AED）和基于DAE的收缩前期修饰（DAEM）。所提出的框架已应用于具有PVC和PAC的实际RRI数据。结果表明，AED达到了93％的灵敏度，每小时的假阳性率为0.08次。与DAEM原始RRI数据相比，改性RRI的均方根误差在PVC中降低到31％，在PAC中降低到73％。此外，通过对临床癫痫发作问题的应用对提出的框架进行了验证，表明该框架可以正确地抑制由PVC引起的假阳性。因此，