Fuzzy C-Means Clustering for Myocardial Ischemia Estimation with Pulse Waveform Analysis

The purpose of this study is to build an automatic disease classification system using pulse waveform analysis, based on a Fuzzy C-means clustering algorithm. A self designed three-axis mechanism was used to detect the optimal site to accurately measure the pressure pulse waveform (PPW). Considering the artery as a cylinder, the sensor should detect the PPW with the lowest possible distortion and hence an analysis of the vascular geometry and an arterial model were used to design a standard positioning procedure based on the arterial diameter changed waveform for the Xand Z-axes. A fuzzy C-means algorithm was used to estimate the myocardial ischemia symptoms in 35 elderly subjects with the PPW of the radial artery. Two type parameters are used to make the features, one is a harmonic value of Fourier transfer, and the other is a form factor value. A receiver operating characteristics curve is used to determine the optimal decision function. The harmonic feature vector (H2, H3, H4) performed at the level of 69% for sensitivity and 100% for specificity while the form factor feature vector (LFF, RFF) performed at the level of 100% for sensitivity and 53% for specificity. The modified clustering algorithm based on FCM and ROC curve is an efficient way for estimating the risk of myocardial ischemia based on the exercise ECG.