Letters: Feature extraction using fuzzy inverse FDA
Neurocomputing
Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models
Journal of Medical Systems
Classification of wrist pulse blood flow signal using time warp edit distance
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Computerized wrist pulse signal diagnosis using KPCA
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Traditional Chinese pulse diagnosis (TCPD) is one of the most important diagnostic techniques in Traditional Chinese Medicine (TCM) and computerized analysis of pulse signals is a crucial step in objectifying and standardizing TCPD. In this work, we use Doppler ultrasonic device to collect wrist-pulse signals from patients with gastritis and cholecystitis as well as healthy persons. After extracting the envelopes of ultrasonic pulse contour, wavelet (packet) transforms are applied to decompose the pulse signals and extract the wavelet features. Together with some Doppler ultrasonic diagnostic parameters, such as STI, RI, etc., a two-category classifier is employed to distinguish the unhealthy persons from healthy ones, and tell the patients from different diseases. 12 gastritis sufferers (Group G), 15 cholecystitis sufferers (Group C) and 19 healthy persons (Group H) were involved in the experiment. An accuracy of 80.77% and an accuracy of 86.21% are achieved in discriminating Group G and Group C from Group H, respectively, and the classification accuracy between Group G and Group C can reach 100%.