Journal of medical engineering & technology
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Comparative Study
Comparison of three arterial pulse waveform classification techniques.
Peripheral pulse waveforms can become stretched and damped with increasing severity of peripheral vascular disease (PVD) and hence could provide valuable diagnostic information. This study compares the diagnostic performance of 3 established classification techniques (a linear discriminant classifier, a k-nearest neighbour classifier, and an artificial neural network) for the detection of lower limb arterial disease from pulse waveforms obtained using photoelectric plethysmography (PPG). Pulse waveforms and pre- and post-exercise Doppler ultrasound ankle to brachial pressure indices (ABPI) were obtained from patients attending a vascular measurement laboratory. ⋯ The value of Kappa for the optimized k-nearest neighbour classifier (k = 27) was intermediate at 47%. This study has shown that classifiers can be taught to discriminate between small, and perhaps subtle, differences in features. We have demonstrated that artificial neural networks can be used to classify arterial pulse waveforms, and can perform better overall than k-nearest neighbour or linear discriminant classifiers for this application.