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 X- and
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.
Relation:
Applications, Basis and Communications 21(2):139-147
