Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches
Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches
复制标题
DOI：
10.1038/s41598-019-49092-2
复制
发表时间：
2019-10-21
影响因子：
4.6
通讯作者：
Chon, Ki H.
中科院分区：
文献类型：
--
作者：
Bashar, Syed Khairul;Han, Dong;Chon, Ki H.
从腕表光电容积描记图（PPG）信号检测心房颤动（AF）是重要的，因为腕表形状因子使得能够以简单且非侵入性的方式长期连续监测心律失常。我们已经开发了一种新的方法，不仅可以从智能手表PPG信号中检测AF，还可以确定记录的PPG信号是否被运动伪影破坏。我们检测运动和噪声伪影的基础上加速度计信号和变频复解调的PPG信号的时频分析。之后，我们使用连续差异的均方根和样本熵（从PPG信号的搏动到搏动间隔计算）来区分AF和正常节律。然后，我们使用房性期前收缩检测算法来进行更准确的AF识别并减少误报。本研究中使用了两个独立的数据集来测试所提出的方法的有效性，其在数据集上的综合灵敏度、特异性和准确性分别为98.18%、97.43%和97.54%。
Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets.