System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Classification of wrist pulse blood flow signal using time warp edit distance
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Wrist pulse diagnosis using LDA
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
| Hi-index | 0.00 |
The wrist pulse signals can be used to analyze a person's health status in that they reflect the pathologic changes of the person's body condition. This paper aims to present a novel time series analysis approach to analyze wrist pulse signals. First, a data normalization procedure is proposed. This procedure selects a reference signal that is `closest' to a newly obtained signal from an ensemble of signals recorded from the healthy persons. Second, an auto-regressive (AR) model is constructed from the selected reference signal. Then, the residual error, which is the difference between the actual measurement for the new signal and the prediction obtained from the AR model established by reference signal, is defined as the disease-sensitive feature. This approach is based on the premise that if the signal is from a patient, the prediction model previously identified using the healthy persons would not be able to reproduce the time series measured from the patients. The applicability of this approach is demonstrated using a wrist pulse signal database collected using a Doppler Ultrasound device. The classification accuracy is over 82% in distinguishing healthy persons from patients with acute appendicitis, and over 90% for other diseases. These results indicate a great promise of the proposed method in telling healthy subjects from patients of specific diseases.