Browsing by Author "Chen, Jia-Ming"

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Bio-signal Analysis System Design with Support Vector Machines based on Cloud Computing Service Architecture
(2010-01-01)
Shen, Chia-Ping
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Chen, Wei-Hsin
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Chen, Jia-Ming
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Hsu, Kai-Ping
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Lin, Jeng-Wei
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.
High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation
(2013-10-01)
Shen, Chia-Ping
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Chen, Chih-Chuan
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Hsieh, Sheau -Ling
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Chen, Wei-Hsin
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Chen, Jia-Ming
The classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive feature selection, and support vector machine for feature classification. Performance of the system was tested on open source data, and the overall accuracy reached 99.97%. We further tested the performance of the system on clinical EEG obtained from a clinical EEG laboratory and bedside EEG recordings. The results showed an overall accuracy of 98.73% for routine EEG, and 94.32% for bedside EEG, which verified the high performance and usefulness of such a cascade system for seizure detection. Also, the prediction model, trained by routine EEG, can be successfully generalized to bedside EEG of independent patients.
Teachers\' Pro-Industry Professional Cognitive to Adjust Industry 4.0
(2016)
Chou, Chun-Mei
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Shen, Chien-Hua
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Hsiao, Hsi-Chi
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Chen, Su-Chang
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Chang, Hui-Tzu
This study examines 341 tertiary teachers\' pro-industry professional cognition and its influencing factors to serve as a school reference for adjust industry 4.0. The results show that teachers\' industry experience has a significant direct effect on tertiary teachers\' pro-industry professional cognition, and "teaching innovation self-efficacy has a significant effect on pro-industry professional cognition through industry experience. The influence pattern and empirical data of teaching innovation self-efficacy and industry experience on pro-industry professional cognition has a good fit.