Classification of pulse waveforms using edit distance with real penalty
EURASIP Journal on Advances in Signal Processing
Computerized wrist pulse signal diagnosis using KPCA
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
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A practical method is proposed to segment the wrist pulse waveform and estimate the average waveform. Some key issues that would affect the performance of the tasks are addressed. A zero-phase filtering was used to accommodate low frequency variations and high frequency noise without the phase-shift distortion, and a moving-window adaptive threshold based segmentation algorithm was used to ensure the segmenting performance. Waveform rotating and scaling, outlier elimination, cross-covariance based alignment, and average waveform estimation were introduced. Testing results show the effectiveness of segmentation performance, and the resulting average waveform well reflect the typical characteristics of the analyzed wrist pulse trend.