Complex Frequency Features for TCD Signal Analysis

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Abstract：In this paper, we report on using pattern recognition techniques for embolic signal (ES) detection based on transcranial doppler ultrasound (TCD) audio data collected via machine EMS-9 (from Shenzhen Delicate Electronics, Co. Ltd). Firstly, we adopted complex discrete fourier transform to get spectra of audio recordings; secondly, we used principal component analysis (PCA) for the visualization of selected signals, which makes it easy and intuitive to verify whether a signal contains an embolic component; finally we designed the classifier with support vector machines (SVM) for detection. With contrast to traditional methods of ES detection systems, the proposed approach considers two channel signals from the audio data collected by single transducer, and there is no predefined features for classification. The primary experimental results on real data are promising.

Key words： embolic signal transcranial doppler ultrasound(TCD) complex discrete fourier transform principal component analysis support vector machine

收稿日期: 2016-12-28

PACS:

R743

引用本文:

. [J]. 中国生物医学工程学报(英文版）, 2017, 26(1): 17-23.
LIU Wei-xiang, WANG Tian-fu, CHEN Si-ping, LUO Laurence, WANG Xiao-yi. Complex Frequency Features for TCD Signal Analysis. Chinese Journal of Biomedical Engineering, 2017, 26(1): 17-23.

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http://manu32.magtech.com.cn/Jwk_zgswyx_en/CN/ 或 http://manu32.magtech.com.cn/Jwk_zgswyx_en/CN/Y2017/V26/I1/17

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