A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Computational methods for Traditional Chinese Medicine: A survey
Computer Methods and Programs in Biomedicine
Wavelet Based Analysis of Doppler Ultrasonic Wrist-pulse Signals
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
A Practical Approach to Wrist Pulse Segmentation and Single-period Average Waveform Estimation
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Advanced Pattern Recognition Technologies with Applications to Biometrics
Advanced Pattern Recognition Technologies with Applications to Biometrics
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models
Journal of Medical Systems
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
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Wrist pulse signals can reflect the pathological changes of a person's body condition due to the richness and importance of the contained information. In recent years, the computerized pulse signal analysis has shown a great potential to the modernization of traditional pulse diagnosis. In this paper, we attempted to use the wrist pulse signals collected by a Doppler ultrasonic blood analyzer to perform wrist pulse signal diagnosis. We first cropped the wrist pulse signal to obtain the single-period waveform, and then employed KPCA to extract features from the waveform. Finally, we used a nearest neighborhood classifier to classify the extracted features. We adopted a wrist pulse signal dataset, which includes pulse signals from both healthy persons and patients. Several experiments on the dataset were carried out and the results show that our developed approach is feasible for computerized wrist pulse diagnosis.