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ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 246-249 ECG Pattern Recognition Based on Wavelet Transform and BP Neural Network Shanxiao Yang, and Guangying Yang School of Physical & Electronics Engineering, Taizhou University, Taizhou, China ysxtzc@126.com Abstract—This paper introduces the Electrocardiogram (ECG) pattern recognition method based on wavelet transform and standard BP neural network classifier. Experiment analyzes wavelet transform of ECG to extract the maximum wavelet coefficients of multi-scale firstly. We then input them to BP classify for different kinds ECG. The experimental result shows that the standard BP neural network classifier’s overall pattern recognition rate is well. The ECG in this paper was from MIT-BIH. Experimental result shows that feature vector extracted by the wavelet transform can characterize ECG patterns, and BP neural network classifier has a stronger ECG recognition effect. Index Terms—Electrocardiogram (ECG); Pattern Recognition; BP neural network; Wavelet Transform; MIT-BIH I. INTRODUCTION Since 1903 the Electrocardiogram(ECG) was introduced to clinical medicine, the techniques have been developed rapidly in the record, processing and diagnosis of the ECG whether it is in the biomedical area or in engineering and we accumulate considerable experience. ECG plays an important role in the clinical diagnosis of the heart disease. It provides an objective indicator for correct analysis, diagnosis, treatment and care of the heart disease. Because of its important social value and economic value, it has a very wide range of applications in the modern medicine. ECG is still a major research subject in the biomedical engineering [1]. ECG is the recording of the electrical activity of the heart, and has become one of the most important tools in the diagnosis of heart diseases [2]. ECG signal is shaped by P wave, QRS complex, and T wave. In the normal ECG beat, the main parameters including shape, duration, R-R interval and relationship between P wave, QRS complex, and T wave components are inspected. Any change in these parameters indicates an illness of the heart. The study of ECG recognition has an important significance in understanding human hear in the role of human intelligence. Although many efforts have been taken recently to recognize ECG using different methods, current recognition systems are not yet advanced enough to be used in realistic applications. This work is supported by education department Program of Zhejiang Province in University (2010). II. THE SOURCE OF THE ECG A. Data Acquisition from the Acquisition Card Human ECG is a non-linear, non-stationary, weak signal and low signal to noise ratio. The voltage of the conventional ECG is just mv level and its frequency is 0.05-100HZ. Due to equipment, human factors, the acquisition of ECG accompany with interference. Common ECG interference has three types 1Electrocardiogram (ECG) interference. It is caused by human activities and muscle tension. Its frequency is 5-2000HZ. ECG has wide spectrum and often mixed with the ECG’s spectrum. So it is difficult to use the general filtering to separate them. 2 The frequency interference of the power-line. It is fixed frequency interference. It is form of 50HZ and its harmonic components. It is caused by the space electromagnetic interference to human body from powersupply network and its equipment .In essence, the alternating current are non-stationary random process in the frequency and amplitude with a slow fluctuation. 3 Baseline drift. It is caused by the low-frequency interference, such as the movement of the measuring electric class, respiratory of the human. Its general frequency is less than 1HZ [3]. Therefore, the collected ECG is often accompanied with the above noise. B. Recognition Methods --BP neural network BP neural network is fully named as the Back- Propagation Network, that is, back-propagation network. It is a forward multi-layer network, which uses the error back-propagation algorithm to train the network. BP algorithm [4,5] was proposed by Rumelhart et al in 1986, and since then, due to simple structure, multi-adjustable parameters, much training algorithm and good operational performance, BP neural network got a wide range of practical application. The network structure of the three-layer BP neural network is shown in Figure 1, from which we can see that, BP neural network contains an input layer, a middle layer (hidden layer) and an output layer. There is a full connectivity between the upper and lower layers and no connections between neurons in each layer. For the input signal, it needs to spread towards to hidden layer nodes and transformed by the function, then transmit the input signal of hidden layer nodes to the output layer nodes. Usually, the transfer function of BP neural network is Sigmoid Type differentiable function, which can achieve arbitrary non-linear mapping between the input and © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 246
output, so BP network has been widely applied in pattern recognition, function approximation and other areas [5]. The three nodes of the BP network is represented as: x j input node , hidden node y σ i , output node j , network w ij weight of input node , network weight of hidden node T and output node li , the expectation output the output t node l , Figure 1 shows the BP neural network structure. e j ( n) = d ( n) − O ( n) j 2 back-calculation of δ : For the output units, ( l ) ( ) ( L δ n e ) ( n ) O ( n ) [ O ( n ) j = j j 1− j ] (7) and for the hidden layer units, ( l ) ( ) ( l ) ( ) ( l [ ) ( ) ( l+ 1 n y n y n ] ) ( n) w ( l+ 1 δ ) j = j 1− j ∑δ k kj ( n) k (8) 3 Fix the right values according to the following: ( l ) ( ) ( l ) ( ) () l ( ) ( l−1 w n w n n y )( n ) jk + 1 = ji + ηδ j i (9) 5) Enter a new sample until it reaches the error requirement, and the input order of each cycle in training samples needs a re-random order. The specific program flow chart of training network using BP algorithm shows as fig.2. j Begin (6) Input layer Hidden layer Output layer Figure 1. BP neural network structure The basic learning algorithm of BP neural network: l) Determine the various learning parameters on the basis of the known network structure, including input layer, hidden layer, output layer neuron number, learning rate and error parameters. 2) Initialize the network weights and thresholds. 3) Provide learning sample: input vector and target vector. 4) Start to learn, and do the following for each sample: 1 Forward-calculation of the j unit in the l layer: T () l l l−1 v j ( n) = ∑ w ji ( n) yi ( n) i= 0 (1) y l −1 ( n) Equation (7) i y the i (i=0,set l −1 ( ) then then l−1 0 = − is the signal transmitted from l l 1 w j0 ( n) = θ j ( n) , ) unit of the layer. If the j unit activation function is sigmoid function, y 1 () l j ( n) = () l 1+ exp − v ( n) ( ) and ∂y f ′( v j ( n) ) = = y j 1 ∂v () l j ( n) ( n) j j [ ] ( n) − y ( n) j (2) (3) l = 1 , If the j unit belongs to the first hidden layer ( ) ( 0) y j = x j ( n) ( ) If the j unit belongs to the output layer l = L ( L y ) ( n) O ( n) j = j (4) , then (5) NO Network initiation Initialize learning sample Compute network layer output of each neuro neuron Compute the train error Modify the network weight Meet the error precision End YES Figure 2. BP Algorithm Flow Chart III. EXPERIMENT PROCESS Experiment was carried out in the Matlab7.6 environment. Acquisition of a high-quality database of physiological signals is vital for the emotion recognition algorithm development. An important concern is the selection of signals that are to be used as input to the emotion recognition system. It is desirable that the 247
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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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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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According to maximum membership deg
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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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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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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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Combinational logic unit failures i
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Figure 9. (a)the original image.(b)
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module, communication module, apper
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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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Keyboard event Application User mod
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some drilling fluid produces hydrog
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and minimum structural elements in
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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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