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influence of emotion on the activity of the nervous system is effectively reflected in the physiological signals employed. Unlike the case of speech recognition or facial expression recognition, where knowledge of the correct class label of a given data point is self-evident, the acquisition of a high-quality physiological signal database with confidence in the underlying emotional status is an intricate task. The physiological signal data of ECG is from the MIT-BIH, Standard MIT-BIH Arrhythmia database are collected by Beth Israel Hospital Arrhythmia Laboratory from 1975 to 1979. There are more than 4000 data Holtel sets [6]. Database has a total of 48 records, which are from 47 individuals (of which 201 and 202 are from the same individual). In MIT-BIH database, each record contains data on two channels, which leads settings as follows: The first channel is the use of calibration limb leads Ⅱ; The second channel used correction V1 (occasionally have V2, V5 leads, there is another V4 leads). Data’s sampling frequency is 360HZ and the sampling accuracy is 11 bits (sample data range between 0 ~ 2047). analysis figure, and this paper extracts the maximum value as the feature vector of different pattern of ECG. Wavelet Transform analyzed ECG parameters are statistically classified as emotional pattern joy, anger, sadness and pleasure. These parameters are then applied to ANN as training and testing data. Also, these parameters are considered as neurons in ANN. The neurons in a feedforward neural network are organized as a layered structure and connected in a strictly feedforward manner. The structure of a basic feedforward neural network is presented in Fig.1. The feedforward neural network is one of the most widely used ANNs. A great number of successful applications of this type of network have been reported [7]. So, after the four-scale decomposition procedure, we get wavelet coefficients of maximum value value of a typical ECG pattern. There are only four representative samples given. Each ECG classification pattern signal is composed by five coefficients. Then decompose the fourscale wavelet to get the 5-dimensional feature vector as the input feature vector of ECG for pattern recognition with method of BP neural network. Beacuse each line has only one element of each column (under the painted lines are) which is much larger than other elements, which indicates that separation algorithm is very satisfactory. For describing the separation effect quantitatively, we n 1 si − yi 2 ∑ MN s i= 1 i 2 use the expression to compute the average relative error of the source signals and separated signals, where N is the number of the source, M is the sampling points). The error curve is showed in Figure 4. We can know the system has large error when starting the train but the error slowly getting smaller and smaller, soon met the desired error and the convergence effect is also obvious in Figure 4. 18 16 14 12 10 8 6 Figure 3 The ECG signals of sample 100 and wavelet transform in four scales This paper uses a quadratural, compact Daubechies 2 wavelet as the base function for four-scale decomposition of the ECG physiological signal data each day. And extract the maximum values composition vector of each layer in wavelet decomposition as the feature vector of the ECG signal vector, constituting a 4- dimensional feature vector. The ECG signals Waveform of sample 100 and wavelet transform coefficients Wf (a, b) in different scales are shown in fig.3. Then we extract wavelet coefficients of the signal’s maximum value and minimum value. We classify and save four kinds of models of the wavelet coefficients. The features of the ECG can be extracted from the statistical 4 2 0 0 500 1000 1500 2000 2500 3000 Figure 4. The graph of the error IV. CONCLUSINO In this study, a new method concluding wavelet transform and artificial neural network is proposed for classification of ECG arrhythmias. This method includes two phases, which are valued feature extraction and classification. In feature extraction phase of the proposed method, the feature values for each arrhythmia are extracted using discrete wavelet transform (DWT). In classification phase, obtained valued features are used as 248
input to the artificial neural network for classification of ECG arrhythmias. Experiment shows that ANN is a valuable tool in the medical field for the development of decision support systems. The actual implementation of ANN analysis of ECG signals involves several stages of varying complexity. ACKNOWLEDGMENT We wish to thank our colleagues at Taizhou University for many suggestions and stimulating discussions. REFERENCES [1] Sunwen Duo. Twelve-lead ECG acquisition and analysis system [D]. Jilin, Jilin University, 2006:7-7. [2] Yu, S. N., and Chou, K. T., Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34:2841–2846, 2008. [3] Li An,Li Dong. The Application of Wavelet Analysis inElectrocardiological Signal Digital Filter[J]. Computer Simulation,2001,18(6):70-73(In Chinese) [4] F. Nasoz, K. Alvarez, C. L. Lisetti, N. Finkelstein: Emotion Recognition from Physiological Signals for Presence Technologies, International Journal of Cognition, Technology and Work, Special Issue on Presence, Vol 6(1), 2003 [5] Xinliang Zhang, Yonghong Tan. The adaptive control using BP neural networks for a nonlinear servo-motor [J]. Journal of Control Theory and Applications, 2008, 6(3): 273-276. [6] Zhou Qunyi. ECG heart beat Identification Modeling and Analysis of [D]. Zhejiang, Hangzhou : Zhejiang University, 2004:1-1. [7] K. H. Kim, S. W. Bang, S. R. Kim. Emotion recognition system using short-term monitoring of physiological signals [J]. Med. Biol. Eng. Comput., 2004, 42, 419–427 249
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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ISBN 978-952-5726-09-1 (Print) Proc
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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