Automatic Detection Algorithm for Transthoracic Impedance Signal Using K-means Clustering Based on Density Weighting and Preference Information
Li Yong-ming①② Chen Bo-han① Wang Pin①
①(College of Communication Engineering, Chongqing University, Chongqing 400044, China) ②(College of Biomedical Engineering and Medical Imaging, The Third Military Medical University, Chongqing 400038, China)
Abstract：In order to recognize automatically the compression and ventilation waveforms of the TransThoracic Impedance (TTI) signal, and obtain the important parameters, for evaluating the CardioPulmonary Resuscitation (CPR) quality, this paper proposes an automatic detection algorithm for TTI signal based on density weighting and preference information. The TTI signals that come from the pig model based on electrically induced cardiac arrest are preprocessed, and the potential compression and ventilation waveforms are marked by using the searching algorithm of multiresolution window after the pretreatment. After that, the width, amplitude and the difference between the adjacent waveforms of the marked waveforms are selected as the features and the signal is divided into several sections according to the width of marked waveforms. Then the original signal is decomposed by wavelet transform. The ratio of the power of each section to the amplitude of the original one is taken as one feature. Finally, k-means clustering algorithm based on density weighting and preference information is used to recognize and classify the compression and ventilation of the marked waveforms. The experimental results show the accuracy and sensitivity of the recognition are high, the robustness is good and the running time (0.43±0.07 s) can meet the requirement of clinical application.
李勇明, 陈勃翰, 王品. 采用基于密度加权和偏好信息的K均值聚类的胸阻抗信号自动检测算法[J]. 电子与信息学报, 2015, 37(4): 824-829.
Li Yong-Ming, Chen Bo-Han, Wang Pin. Automatic Detection Algorithm for Transthoracic Impedance Signal Using K-means Clustering Based on Density Weighting and Preference Information. , 2015, 37(4): 824-829.
2015, Vol. 37 
