Wavelet Based Analysis of Doppler Ultrasonic Wrist-pulse Signals
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
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Recent study reported that wrist pulse blood flow signal is effective for disease diagnosis. The multiscale entropy, which was developed for quantifying the complexity of a time series of physiological signals over a range of scales, had been widely applied for feature extraction from medical signals. In this paper, using the multiscale sample entropy (Multi-SampEn) algorithm, we compute the value of SampEn of wrist pulse blood flow signal that includes 83 samples healthy persons, 45 samples of patients with liver diseases (LD), and 45 with sugar diabetes (SD). Then we use the values of SampEn as the feature input of the support vector machine classifier for disease diagnosis. Experimental results show that the proposed method could achieve the classification accuracy of 76.30% with the dimension m = 2 and the threshold r = 0.6, which is promising in diagnosing the healthy subjects, liver diseases, and sugar diabetes.