Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Estimating the Support of a High-Dimensional Distribution
Neural Computation
ECG pattern classification based on generic feature extraction
CISST'09 Proceedings of the 3rd WSEAS international conference on Circuits, systems, signal and telecommunications
The computer for the 21st Century
IEEE Pervasive Computing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
SVM based context awareness using body area sensor network for pervasive healthcare monitoring
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
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In this paper we proposed an ECG (electrocardiogram) signal monitoring system working on a ZigBee based wireless sensor network. An ECG signal acquisition module is implemented on a wireless platform that can acquire heart signals from ECG sensors and do wirelessly transmit the acquired heart signals based on a ZigBee protocol. Moreover, the ECG signal acquisition module is accompanied by an ECG signal monitoring module implemented in a host PC, which analyzes transmitted ECG signals from the ECG signal acquisition module and generates monitoring signals indicating normal and abnormal states. The proposed ECG signal monitoring system operating based on wireless communication of these two modules is aimed to be developed as a personalized heart signal processing system. In order to develop such a personalized system, a generic feature extraction method and an OCSVM (one-class support vector machine) classifier are applied. A histogram technique and a principal component analysis method are considered for generating features with general characteristics by extracting initial features and refined features from input ECG signals, respectively. Moreover, OCSVM is considered for developing a personalized heart signal classifier working for discriminating abnormal heart signals from normal heart signals aimed at a personalized system operating. For performance verification of the proposed system, experiments using supraventricular arrhythmia and normal ECG signals of MIT-BIH DB are conducted. The proposed system correct classification rates of 93.3% and 92.6% for normal ECG signals and supraventricular arrythmia ECG signals, respectively. Theses experimental results shows that the proposed system outperforms compared with different approaches with other classifiers.