Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3501-3504. doi: 10.1109/EMBC.2017.8037611.
An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.
提出了一种基于自适应傅里叶分解（AFD）的R波检测方法，用于处理有噪声的心电图信号。尽管文献中已经提出了许多QRS检测方法，但大多数检测方法都要求信号质量较高。该方法利用AFD从能量域中提取R波，并根据关键分解参数确定R波峰值位置，实现了去噪和R波峰值检测同时进行。经麻省理工学院-比哈尔心律失常数据库中的临床心电图信号验证，该方法在原始潘-汤普金（PT）算法以及带有去噪过程的PT算法这两种情况下，均表现出比PT算法更好的性能。
