1. BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM
- Author
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Tetsuo Miyake, Zhong Zhang, Yoshifumi Yasuda, Satoshi Horihata, Hiroshi Toda, and Takashi Imamura
- Subjects
Discrete wavelet transform ,business.industry ,Computer science ,Applied Mathematics ,Stationary wavelet transform ,Second-generation wavelet transform ,Pattern recognition ,Blind signal separation ,Wavelet packet decomposition ,Wavelet ,Signal Processing ,Artificial intelligence ,Complex wavelet transform ,Harmonic wavelet transform ,business ,Information Systems - Abstract
Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.
- Published
- 2010
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