自己組織化マップと隠れマルコフモデルを用いた透析シャント音による血管狭窄診断支援装置

概要

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The number of dialysis patients is approximately 300,000 and is increasing every year in Japan. A renal failure patient requires a hemodialysis shunt for dialysis to be performed; however, the blood vessels around the hemodialysis shunt may become stenosed. The stethoscope auscultation of vascular murmurs has some use in the assessment of access patency; however, this diagnostic approach is skill dependent. Therefore, a diagnostic support system to detect stenosis is desirable. We developed an auscultating diagnosis support system for assessing hemodialysis shunt stenosis by using a self-organizing map (SOM) and short-time maximum entropy method. In this paper, for the purpose of improving the accuracy of stenosis detection, the Mel-frequency cepstrum coefficient (MFCC)-based hidden Markov model (HMM) was also used. As a result, a high correlation between an SOM system and HMM system was found. Therefore, the credibility of the each system was confirmed. Furthermore, the results indicated that the accuracy of stenosis detection could be improved by combining the SOM and HMM methods.