Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute, Daegu 42994, Korea.
TriBell Labs, Kyungpuk 38541, Korea.
Sensors (Basel). 2020 Oct 29;20(21):6144. doi: 10.3390/s20216144.
With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.
随着医疗物联网技术的进步，许多生命体征感应设备正在被开发出来。在众多的医疗设备中，便携式心电图（ECG）测量设备随着传感器技术的最新发展，其开发最为活跃。这些 ECG 测量设备根据硬件条件使用不同的采样率，这是 ECG 分析技术开发中需要考虑的第一个变量。在此，我们提出了一种基于采样率的自适应中值滤波器的 R 点检测方法，并利用信号特征分析主要心律失常。首先，根据设定的采样率确定滑动窗口和中值滤波器的大小，并在滑动窗口内对 QRS 段应用方差较高的较宽中值滤波器。然后，通过从原始信号中减去滤波信号来检测 R 点。提出了使用检测到的 R 点检测主要心律失常的方法。对不同类型的 ECG 信号进行了模拟，包括来自 MIT-BIH 心律失常数据库和 MIT-BIH 心房颤动数据库的 ECG 信号、模拟器生成的信号以及具有不同采样率的实际测量信号。实验结果表明了所提出的 R 点检测方法和心律失常分析技术的有效性。
