Nimunkar Amit J, Tompkins Willis J
Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:1261-4. doi: 10.1109/IEMBS.2007.4352526.
This study used empirical mode decomposition (EMD) for R-peak detection in electrocardiogram signals in the presence of electromyogram-like noise. The EMG was modeled as random white Gaussian noise with a signal-to-noise ratio (SNR) in the range of around -10 dB to -20 dB. The EMD-based R-peak detection technique gives results comparable to those obtained with the Pan-Tompkins algorithm. The EMD technique is implemented for filtering of noisy ECG signals and is further compared with a traditional low-pass filtering approach. Finally signal averaging is performed using the EMD-based R-peak detection and filtering approach and compared with the standard signal averaging technique. We conclude that the EMD based technique for R-peak detection and filtering shows promise for enhancement of the stress ECG.
本研究在存在类肌电图噪声的情况下，使用经验模态分解（EMD）进行心电图信号中的R波检测。肌电图被建模为信噪比（SNR）在约-10 dB至-20 dB范围内的随机白高斯噪声。基于EMD的R波检测技术给出的结果与使用Pan-Tompkins算法获得的结果相当。实施EMD技术用于对有噪声的心电图信号进行滤波，并进一步与传统的低通滤波方法进行比较。最后，使用基于EMD的R波检测和滤波方法进行信号平均，并与标准信号平均技术进行比较。我们得出结论，基于EMD的R波检测和滤波技术有望增强应激心电图。
