Background. Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. Objective. The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. Methods. During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. Results. The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. Conclusion. This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

中文翻译：

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基于改进时间同步平均的脉率变异性和心房颤动检测新方法

背景。脉搏变异监测和房颤检测算法已广泛应用于可穿戴设备，但这些算法的准确性受到脉搏波信号质量的限制。时间同步平均是一种强大的周期性和近似周期性信号降噪方法。它通常用于提取单周期脉搏波形，但与传统的脉率变异监测和房颤检测无关。如果对该方法进行适当改进，它可能会为测量脉率变异性和检测房颤提供一种新的方法，在信号质量较差的情况下可能具有一些潜在的优势。客观的. 本文的目的是通过改进现有的时间同步平均来开发一种新的脉率变异性测量方法，并通过脉率变异性的新测量方法检测心房颤动。方法. 时间同步平均时，以相邻两个周期为基本单位计算平均信号，相邻两个周期波形之差是衡量脉率变异性的新指标。在容量为 1000 的模拟训练集上测试了 3 种距离测量（欧几里得距离、曼哈顿距离和余弦距离）来测量这种差异。距离测量可以准确区分规则脉搏和不规则脉搏，是用于在容量为 62 的测试集上检测心房颤动（心房颤动 11 人，过早收缩 8 人，窦性心律 43 人）。受试者工作特征曲线用于评价指标的性能。结果. 两个相邻周期的波形之间的欧几里得距离在训练集上表现最好。在测试集上，房颤组的欧几里得距离明显高于其他两组。识别房颤的受试者工作特征曲线下面积为0.998。阈值为 2.1 时，准确度、灵敏度和特异性分别为 98.39%、100% 和 98.04%。这个新指标可以从脉搏波信号中检测心房颤动。结论。该算法不仅为房颤检测提供了新视角，而且通过同一技术路线完成了PRV的监测和单周期脉搏波的提取，有望促进脉搏波的推广应用。