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

Volume

1

fYear

2008

fDate

30-31 Aug. 2008

Firstpage

436

Lastpage

441

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICWAPR.2008.4635819

Filename

4635819