目的:通过准确和立即的识别,可以预防由心律失常引起的大量全球死亡。可穿戴设备可以通过使用心电图(ECG)连续监测心脏活动来在这种识别中发挥关键作用。现有的研究集中在通过研究各种电极位置和用于ECG解释的算法开发来从身体表面提取心脏信息。本研究旨在使用上臂记录的信号进行心跳检测。
方法:首先,针对休息和肘部屈曲(EF)条件,确定了上臂上的最佳电极位置。接下来,使用选定的电极生成合成的心电图,在受试者和试验中具有广义的权重,然后应用零相位小波(Zephlet)进行特征提取。最终使用与多代理检测方案(MDS)结合的提取的细节系数执行心跳检测。
结果:对于三种不同水平的肌肉收缩(C1至C3)的Rest和EF,心跳检测的F1评分为0.94±0.16、0.86±0.22、0.79±0.26和0.67±0.31,分别。将检测到的心跳和实际心跳之间的可接受距离从50ms更改为20ms,Rest和C1至C3的F1评分分别变为0.81±0.20、0.66±0.26、0.57±0.26和0.44±0.26。
结论:这些发现对目前的文献做出了一些贡献,总结为精确和一致的电极定位的各种肌肉收缩水平和准确的心跳检测方法的发展为每一个这些条件。
OBJECTIVE: A significant number of global deaths caused by cardiac arrhythmias can be prevented with accurate and immediate identification. Wearable devices can play a critical role in such identification by continuously monitoring cardiac activity using electrocardiogram (ECG). The existing body of research has focused on extracting cardiac information from the body surface by investigating various electrode locations and algorithm development for ECG interpretation. The present study was designed for heartbeat detection using the signals recorded from the upper arm.
METHODS: Firstly, optimal electrode locations on the upper arm were identified for Rest and elbow flexion (EF) conditions. Next, a synthesized ECG was generated using the selected electrodes with generalized weights over subjects and trials, and then zero-phase wavelet (Zephlet) was applied for feature extraction. Heartbeat detection was finally performed using the extracted detail coefficients incorporated with a multiagent detection scheme (MDS).
RESULTS: The F1-score for heartbeat detection was 0.94 ± 0.16, 0.86 ± 0.22, 0.79 ± 0.26, and 0.67 ± 0.31 for Rest and EF with three different levels of muscle contraction (C1 to C3), respectively. Changing the acceptable distance between the detected and actual heartbeats from 50 ms to 20 ms, the F1-score changed to 0.81 ± 0.20, 0.66 ± 0.26, 0.57 ± 0.26, and 0.44 ± 0.26 for Rest and C1 to C3, respectively.
CONCLUSIONS: These findings make several contributions to the current literature, summarized as precise and consistent electrode localization for various muscle contraction levels and accurate heartbeat detection method development for each of these conditions.