Title :
Independent Component Analysis using wavelet transform and its application to biological signals
Author :
Horihata, Satoshi ; Zhang, Zhong ; Enomoto, Takeshi ; Toda, Hiroshi ; Imamura, Takashi ; Miyake, Tetsuo ; Yasuda, Yoshifumi
Author_Institution :
Sch. of Dentistry at Matsudo, Nihon Univ., Matsudo
Abstract :
Independent Component Analysis (ICA) is a useful method for blind source separation of two signals or more. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT). In this case, the voice and the noise were separated using a new method. At that time, we used the simulation signal. In this study, we analyze measured biological signals by using this new method, and discuss its effectiveness. As an example, we tried the separation of the EMG signal and the ECG signal.
Keywords :
blind source separation; discrete wavelet transforms; electrocardiography; electromyography; independent component analysis; medical signal processing; ECG signal; EMG signal; biological signals; blind source separation; complex discrete wavelet transform; independent component analysis; simulation signal; Convolution; Discrete wavelet transforms; Electrocardiography; Electromyography; Independent component analysis; Pattern analysis; Signal analysis; Time frequency analysis; Wavelet analysis; Wavelet transforms; Biological signals; ECG; EMG; Independent component analysis; Wavelet transform;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-2238-8
Electronic_ISBN :
978-1-4244-2239-5
DOI :
10.1109/ICWAPR.2008.4635819
