BCG Signal De-Noising Method ResearchBased on EMD-ICA
Jiang Xing1, Geng Duyan1,2*, Zhang Yuanyuan2, Fu Zhigang3
1.(Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin 300130, China) 2.(Hebei University of Technology Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Tianjin 300130, China); 3.(Liberation Army 254 Hospital, Tianjin 300142, China)
Abstract：Ballistocardiogram (BCG) signal is a physiological signal reflecting heart mechanical status. It can achieve continuous acquisition measurement without electrodes constraint. However, BCG signal is so weak that it would often be interfered by superimposed noises. Aiming to eliminate the noise and recognize BCG signal characteristics effectively, this paper proposed a de-noising method of BCG signal based on empirical mode decomposition (EMD) and independent component analysis (ICA). Firstly, the noisy BCG signal was decomposed by EMD to obtain a series of intrinsic mode components (IMF) ranked by frequency in descending order, and the EMD mode was used to distinguish the boundary of noise and useful signal and remove the maximum noise. Secondly, the IMF components of above the boundary were employed to construct a virtual noise channel, and the blind source was separated with the original BCG signal based on ICA algorithm to extract the de-noising BCG signal. Acquisition of 10 healthy subjects BCG signals for noise reduction processing. Quantitative evaluation results indicated that the proposed method significantly increased SNR (14.87±3.04,P<0.05) compared with wavelet method (11.01±1.58) and EMD method (5.19±1.29), significantly increasing energy percentage (96.64%±2.92%,P<0.05) compared with wavelet method (88.81%±2.81%) and EMD method (96.15%±2.96%), which proved that the proposed method was effective in the reconstruction of the characteristics of BCG.
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