Application of FastICA to pulse wave

Abstract

We consider fast independent component analysis (FastICA), which is one of the independent component analysis algorithms. FastICA was proposed by Aapo Hyvarinen et al., (2001). It adopts the method of extracting the independent components one after another by the batch method using kurtosis. This method has fast convergence. The purpose of this research is to apply FastICA to the feature extraction of pulse waves of a human being, and to verify its effectiveness. The pulse waves contain a lot of information concerning the circulation of the blood from the heart to the various parts of the body. When blood flows from the heart and is transmitted to the tips as a wave motion, it is modified by physiological conditions such as the heart beat movement, the circulation of the blood flow, and changes in the state of the minor artery system, which leads to the distortion of the shape of the waves. The individual distortions have been evaluated and several trials have been performed to evaluate the health of a person. SOM is used to cluster the pulse waves and the features extracted from each cluster are considered.