Abstract
We attempted health and disease state estimation with data-mining using Self-Organizing Maps (SOM) for application to plethysmogram information. Such information is easily gained from patient fingertip sensors. We used eight variables, such as chaotic analysis values calculated by the trajectory parallel measure method, and the recurrence plot method, in addition to the waveform component ratio, which is a linear analysis value of acceleration plethysmogram. As conventional studies have reported, SOM also confirmed that the waveform component ratio is related to aging. Self-organized acceleration plethysmogram information showed that trajectory parallel measure method values such as chaotic analysis values are useful for disease state estimation of circulatory failure, arteriosclerosis, or acute inflammation. Moreover, the recurrence plot method may reflect the presence and gravity of a disease state. This study, rather than offering general diagnoses of topical named diseases, suggests the possibility of health evaluation by the blood flow state.
