Browsing by Author "Shen, Chia-Ping"

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Adaptive feature selection for real-time face recognition in portable surveillance systems
(2007)
Kao, Wen-Chung
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Shen, Chia-Ping
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Kao, Chih-Chung
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Hsu, Ming-Chai
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Wu, Hung-Hsin
Real-time face recognition is a necessary feature in advanced portable surveillance systems. However, the high complexity of available algorithms to objects segmentation and recognition makes it impossible to include such a feature into a portable device. In this paper, we aim at designing a portable surveillance system which can take MPEG audio/video currently with recognizing human faces based on a digital camera platform. The proposed flow fully utilizes the available intermediate data passing from standard video compression flow in a digital camera. An efficient face recognition approach based on adaptive feature extraction and support vector machines (SVMs) are proposed to address the performance issues. The experimental result shows that the recognition speed running on a commercial digital camera platform can achieve 1.216 seconds/frame.
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.
Newborn Screening System Based on Adaptive Feature Selection and Support Vector Machines
(2009-01-01)
Hsieh, Sung-Huai
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Chien, Yin-Hsiu
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Shen, Chia-Ping
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Chen, Wei-Hsin
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Chen, Po-Hao
The clinical symptoms of metabolic disorders during neonatal period are often not apparent, if not treated early irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is very important to prevent neonatal from these damages. In this paper, the newborn screening system used support vector machines (SVM) classification technique is proposed in place of cut-off value decision to evaluate the metabolic substances concentration raw data obtained from tandem mass spectrometry (MS/MS) and determine whether the newborn has some kinds of metabolic disorder diseases. On the basis of the proposed features, new analytic combinations are identified with superior discriminatory performance compared with the best published combinations. Classifiers built with the feature selection to find C3/C2, C3 and C16 of three key point features achieved diagnostic sensitivities, specificities and accuracy approaching 100%.
Web Services Based Bio-signal System Leveraging Support Vector Machines
(2009)
Hsieh, Sung-Huai
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Hsieh, Sheau-Ling
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Shen, Chia-Ping
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Chen, Wei-Hsin
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Cheng, Po-Hsun
Bio-signal analysis is one of the most important approaches to biomedical engineering. The health information such as ECG, PCG, EMG and EEG are often recorded in digital format to be analyzed. In this paper, a bio-signal analyzing web service system using Support Vector Machines (SVM) classifier technique is proposed. The bio-signals are recorded in digital format as the input of the system. In addition, based on the concept of Service-Oriented Architecture (SOA), the system under web services .NET is designed. The system provides features including heterogeneous platform, protocols, as well as applications. The system has been designed with the purpose of seamless integration into the Health Information System.