Paper Info
Title
KPD Based Signal Preprocessing Algorithm for Pulse Diagnosis
Abstract
In the traditional Chinese medicine (TCM) wrist pulse diagnosis plays a major role in detecting the health status of an individual. It depends strongly on the doctors' long-term experience as well as on their different inferences. Being subjective and based on long-term experience, pulse detection methods are difficult to standardize. Kim pulse diagnosis (KPD), established by Wei Jin, is an efficient method validated by both traditional Chinese medicine and in recent years also by western medicine. The key step to automatic implementation of KPD, using signal processing and analysis, is the developed KPD signal acquisition device. However, the raw wrist pulse signal acquired from KPD device includes a significant amount of noise. This paper proposes several preprocessing algorithms for pulse diagnosis, that includes wavelet transform and Gaussian filter to remove noise and the iterative sliding window (ISW) algorithm to remove the baseline wander and split the continuous signal into single periods. Experimental results show, that the algorithm for baseline wander removal is efficient and that the segmented signal matches the signal described in KPD.
Year
DOI
Venue
2016
10.1109/IIKI.2016.75
2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)
Keywords
Field
DocType
preprocessing algorithm,Kim pulse diagnosis (KPD),e-noising,terative sliding window (ISW)
Pulse diagnosis,Gaussian filter,Signal processing,Continuous signal,Sliding window protocol,Pattern recognition,Computer science,Computer network,Pulse (signal processing),Artificial intelligence,Adaptive filter,Wavelet transform
Conference
ISBN
Citations 
PageRank 
978-1-5090-5953-9
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Zhichao Zhang164.88
Yuan Zhang216016.82
Wei Jin38325.25
Anton Kos48017.96