Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter
Computers in Biology and Medicine
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 framework for automatic time-domain characteristic parameters extraction of human pulse signals
EURASIP Journal on Advances in Signal Processing
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Web-based remote human pulse monitoring system with intelligent data analysis for home health care
Expert Systems with Applications: An International Journal
Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models
Journal of Medical Systems
EasiCPRS: design and implementation of a portable Chinese pulse-wave retrieval system
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Computerized wrist pulse signal diagnosis using KPCA
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Will you have a good sleep tonight?: sleep quality prediction with mobile phone
Proceedings of the 7th International Conference on Body Area Networks
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In recent years, mobile healthcare has received increasing attention. As the wrist-pulse diagnosis in traditional Chinese medicine(TCM) only needs the wrist pulse information of a patient, without any other physiological data and invasive checking, it is a promising technique for mobile healthcare in terms of cost and convenience. But the pulse-based diagnosis requires the sophisticated and long-term training of the physicians. So it is urgent to develop a digitalized method to objectify and standardize the pulse-based diagnosis process. In this paper we design a wrist-pulse sensing and analyzing prototype system which involves a general pulse-sensing device and a cirrhosis diagnosis scheme based on the captured pulse information. The experimental results show that the accuracy of the proposed system reaches to 87.09% in cirrhosis identification.