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Bio-signal analysis system design with support vector machines based on cloud computing service architecture

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8 Author(s)
Chia-Ping Shen ; Grad. Inst. of Biomed. Electron. & Bioinf., Nat. Taiwan Univ., Taipei, Taiwan ; Wei-Hsin Chen ; Jia-Ming Chen ; Kai-Ping Hsu
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Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of .NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.

Published in:

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Date of Conference:

Aug. 31 2010-Sept. 4 2010

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