基于PCG信号分析的智能算法诊断心脏病.pdf

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Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis

Abstract

Keywords

1. Introduction

2. Proposed Methodology

2.1. Data Acquisition

2.2. Signal Pre-Processing

2.3. Feature Extraction

2.4. Classification

3. Conclusions

References

Circuits and Systems, 2017, 8, 184-190 http://www.scirp.org/journal/cs ISSN Online: 2153-1293 ISSN Print: 2153-1285 Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis Mohammed Nabih-Ali1, El-Sayed A. El-Dahshan1,2, Ashraf S. Yahia2 1Faculty of Computer and Information Technology, Egyptian E-Learning University (EELU), Giza, Egypt 2Physics Department, Faculty of Sciences, Ain Shams University, Cairo, Egypt How to cite this paper: Nabih-Ali, M., El-Dahshan, E.-S.A. and Yahia, A.S. (2017) Heart Diseases Diagnosis Using Intelligent Algorithm Based on PCG Signal Analysis. Circuits and Systems, 8, 184-190. https://doi.org/10.4236/cs.2017.87012 Received: January 12, 2017 Accepted: July 25, 2017 Published: July 28, 2017 Copyright © 2017 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access Abstract This paper presents an intelligent algorithm for heart diseases diagnosis using phonocardiogram (PCG). The proposed technique consists of four stages: Data acquisition, pre-processing, feature extraction and classification. PASCAL heart sound database is used in this research. The second stage concerns with removing noise and artifacts from the PCG signals. Feature extraction stage is carried out using discrete wavelet transform (DWT). Finally, artificial neural network (ANN) has been used for classification stage with an overall accuracy 97%. Keywords Heart Diseases, Phonocardiogram (PCG), Feature Extraction, Discrete Wavelet Transform (DWT), Artificial Neural Network (ANN) 1. Introduction Cardiovascular disorders (CVDs) or Heart diseases are broad terms that can af- fect both vasculature and the heart muscle itself [1]. CVDs remains the first cause of mortality globally, responsible for 17.5 million people dying annually [2]. More than 75% of CVDs deaths occur in middle and low income countries [2]. The heart sounds are still the primary tool for screening and diagnosing many pathological conditions of the human heart. Using auscultation technique which was invented and defined by Laennec for heart sound analysis is still insufficient. The reason of insufficiency is reported by Avendano-Valencia et al., [3] [4] due to the human ear limitation and subjective of the analyst and the discriminatory skills that can take many years to acquire. Phonocardiography (PCG) is one of non-invasive techniques to diagnose DOI: 10.4236/cs.2017.87012 Jul. 28, 2017 184 Circuits and Systems

M. Nabih-Ali et al. condition of human heart generated by muscle contractions and closure of the heart valves produces vibrations audible as sounds and murmurs, which can be analyzed by qualified cardiologists [5]. The PCG recording consists of four heart sound components (S1, S2, S3 and S4). The first and second heart sound (S1 and S2) can be heard from the normal heart. They are produced by the closure and opening of the normal valves. For the abnormal heart, a third and a fourth sound (S3 and S4) may also exist in ad- dition to (S1) and (S2) [6]. These abnormal sounds are called murmurs. The ex- isting of murmur in PCG recording is often related to heart valve disease. The four heart sounds (S1, S2, S3 and S4) are shown in Figure 1. A major challenge, facing healthcare organizations (hospitals, medical cen- ters) is the provision of quality services at affordable costs. Quality service im- plies diagnosing patients correctly and administering treatments that are effec- tive [7]. Several researches, various methodologies and many tools that had been pro- posed were used in recently published papers for medical systems development for heart diseases diagnosis [8] [9] [10]. Unfortunately, the achieved accuracy was not satisfactory so, the work is devoted to proposing an algorithm for heart diseases diagnosis with high accuracy. The objective of this paper is to propose an intelligent algorithm based on PCG signals to integrate the clinical decision support with computer-based pa- tient records. This could reduce medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient outcome. This paper is organized as follows. Section 2 presents the proposed machine learning techniques and the aspects of data acquisition, pre-processing, feature extraction and classification processes. In Section 3, experimental results are shown. Finally, Section 4 concludes the paper and proposes future research work. 2. Proposed Methodology A methodology of a heart diseases diagnosis system usually mimics that of a DOI: 10.4236/cs.2017.87012 Figure 1. PCG heart sound signal. 185 Circuits and Systems

M. Nabih-Ali et al. pattern recognition system. Thus, it can be broken down into four main pro- cesses, namely; 1) data acquisition, 2) pre-processing; 3) feature extraction and 4) classification (subject identification). Figure 2 shows the proposed metho- dology for heart diseases diagnosis. In the following subsection each stage will be discussed. The experiments were carried out on the platform of core i3 with 3 G memory running under windows 10 64 bit. The proposed algorithms were developed us- ing MATLAB 2014b wavelet toolbox. 2.1. Data Acquisition Datasets were collected from PASCAL heart sound database [11]. A data base of 170 heart sound signals was created and divided into 121 signals (24 normal signals and 97 abnormal signals) for training and 49 signals (10 normal signals and 39 abnormal signals). The acoustic audio files have varying lengths, between 1 second and 30 seconds. Mainly the most information in heart sounds is enclosed in the low frequency components, having noise in the higher frequencies. 2.2. Signal Pre-Processing PCG signals usually suffers from noise like electromagnetic interference from surrounding environment, power frequency interference, electrical signal inter- DOI: 10.4236/cs.2017.87012 Figure 2. Proposed methodology. 186 Circuits and Systems

M. Nabih-Ali et al. ference with human body and lung sounds. These various noise components make the diagnosis of PCG records difficult or in some cases is impossible. DWT which is a series of high-pass and low-pass filters shows a superior per- formance in signal denoising due to its properties such as multi-resolution and windowing technique. Noises in PCG signals was removed using DWT family called Daubechies wavelet family of order ten (Db 10) with the fourth level of decomposition. Fig- ure 3 shows the noisy PCG signal and the denoised PCG signal. 2.3. Feature Extraction Feature extraction is one of the most important steps of heart sound classifica- tion systems [12]. Time-frequency domain wavelet based feature extraction technique has been successfully used to extract important features from non-sta- tionary heart sound signals for heart diseases classification. The spectrum of heart sound signals is divided into sub-bands (tree structure was used) to extract the most meaningful information from normal and abnor- mal heart sounds. Daubechies wavelet with 4th order (Db4) with 10th level of decomposition is used for determining wavelet coefficients from the PCG signals. DWT decom- poses the signal into approximation (Approximate coefficients (A)-low fre- quency coefficients of signal) and detail (Detail coefficients (D)-high frequency part components of signal). DOI: 10.4236/cs.2017.87012 Figure 3. (a) Noisy PCG signal (b) de-noised PCG signal. 187 Circuits and Systems

M. Nabih-Ali et al. 2.4. Classification The classification process of the artificial neural network begins with sum of multiplication of weights and inputs plus bias at the neuron, if the sum is posi- tive then only output elements fires. Otherwise it doesn’t fire. The artificial neural network is an adaptive system, in other words, the system adopting itself and changes the system weights during the operation [13]. The feature vector extracted from the PCG signal with size 44 features is used as an input to the proposed neural network to classify it into 2 classes (normal and abnormal). The proposed neural network architecture consists of three lay- ers input layer with 44 input neurons, one hidden layer with 10 neurons and one output layer with 2 neurons. Figure 4 shows the architecture of the proposed ANN. Levenverg-Marquardt back propagation algorithm is used for neural network training, sigmoid activation function, momentum constant equal 0.7, learning rate 0.9 and number of epochs 1000 iterations. Table 1 shows the parameters used for training the neural network classifier. Figure 4. Proposed artificial neural network. Table 1. Proposed Neural network training parameters. Network Type No. of layers Activation function Training algorithm No. of epochs Accuracy Momentum constant Learning rate Feed-forward back propagation 3 layers: input, hidden and output layers Sigmoid activation function Levenverg-Marquardt back propagation 1000 97% 0.7 0.9 DOI: 10.4236/cs.2017.87012 188 Circuits and Systems

M. Nabih-Ali et al. Table 2. Comparison between our proposed methodology and previous proposed me- thodologies. Author Database Methods Mandeep Singh (2013) [14] Elsa Ferreira (2013) [15] N. R. Sujit (2016) [16] Zichun Tong (2015) [17] PASCAL dataset Naїve Bayes classifier PASCAL dataset PASCAL dataset PASCAL dataset Decision tree classification algorithm Regression Tree Hilbert Transform + Support vector machine Our proposed System PASCAL dataset DWT and ANN Results Accuracy 93.33% Accuracy 72.76% Accuracy 78.33% Accuracy 90.5% Accuracy 97% For 170 heart sound signals was created and divided into 121 signals (24 nor- mal signals and 97 abnormal signals) for training and 49 signals (10 normal sig- nals and 39 abnormal signals) the neural network classifier misclassified one signal achieving an accuracy of 97%. Finally, Table 2 shows a comparison between our proposed algorithm results with the previous methodologies results to see the efficiency of the proposed methodology. From Table 2, it is clear that our proposed algorithm reached better classifica- tion accuracy than the compared studies. 3. Conclusions This paper proposed an algorithm for heart diseases diagnosis based on PCG signals. The proposed algorithm consists of four stages: Data acquisition, pre- processing, feature extraction and classification. PASCAL PCG signal database was used for training and testing the proposed algorithm. We applied DWT in noise elimination and feature extraction stages and neural network is used for PCG signals classification. A feature vector of size 44 features is formed using DWT used as an input to the classifier for training and testing. ANN is trained using the obtained features. The results showed accuracy of 97% for PASCAL heart sound database. Despite the high accuracy obtained, the system still has some drawbacks like using small dataset, existence of low energy events or noise and the limitation of classifier used for detecting and diagnosis of heart diseases can affect on the di- agnosing accuracy. In the future work we can mention that working towards hybrid techniques which hybrids more than one classification techniques to achieve more accurate results. References [1] Salem, A.-B.M., Revett, K. and El-Dahshan, E.-S.A. (2009) Machine learning in electrocardiogram diagnosis. International Multiconference on Computer Science 189 Circuits and Systems DOI: 10.4236/cs.2017.87012

M. Nabih-Ali et al. DOI: 10.4236/cs.2017.87012 and Information Technology. IEEE, 429-433. https://doi.org/10.1109/imcsit.2009.5352689 [2] http://www.who.int/cardiovascular_diseases/en/ [3] Reed, T.R., Reed, N.E. and Fritzson, P. (2003) Heart Sound Analysis for Symptom Detection and Computer-Aided Diagnosis. Simulation: Modelling Practice and Theory, 12, 129-146. https://doi.org/10.1016/j.simpat.2003.11.005 [4] Avendaňo-Valencia, L.D., Ferrero, J.M. and Castellanos-Domınguez, G. (2007) Im- proved Parametric Estimation of Time-Frequency Representation for Cardiac Murmur Discrimination. Computers in Cardiology, 35, 157-160. [5] Abbas, A.K., Bassam, R. and Kasim, R.M. (2008) Mitral Regurgitation PCG-Signal Classification Based on Adaptive Db-Wavelet. 4th Kuala Lumpur International Conference on Biomedical Engineering, Springer Berlin Heidelberg, 212-216. [6] Roy, A.K., Misal, A. and Sinha, G.R. (2014) Classification of PCG Signals: A Survey. [7] Mann, D.L., et al. (2014) Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences. [8] Sivagowry, S., Durairaj, M. and Persia, A. (2013) An Empirical Study on Applying Data Mining Techniques for the Analysis and Prediction of Heart Disease. 2013 In- ternational Conference on Information Communication and Embedded Systems (ICICES), IEEE. https://doi.org/10.1109/icices.2013.6508204 [9] Dokur, Z. and Ölmez. T. (2008) Heart Sound Classification Using Wavelet Trans- form and Incremental Self-Organizing Map. Digital Signal Processing, 18, 951-959. https://doi.org/10.1016/j.dsp.2008.06.001 [10] Kao, W.-C. and Wei, C.-C. (2011) Automatic Phonocardiograph Signal Analysis for Detecting Heart Valve Disorders. Expert Systems with Applications, 38, 6458-6468. https://doi.org/10.1016/j.eswa.2010.11.100 [11] http://www.peterjbentley.com/heartchallenge/ [12] Hanbay, D. (2009) An Expert System Based on Least Square Support Vector Ma- chines for Diagnosis of the Valvular Heart Disease. Expert Systems with Applica- tions, 36, 4232-4238. [13] Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River. [14] Singh, M. and Amandeep, C. (2013) Heart Sounds Classification Using Feature Ex- traction of Phonocardiography Signal. International Journal of Computer Applica- tions, 77, 13-17. https://doi.org/10.5120/13381-1001 [15] Gomes, E.F., Jorge, A.M. and Azevedo, P.J. (2013) Classifying Heart Sounds Using Multi-Resolution Time Series Motifs: An Exploratory Study. Proceedings of the In- ternational Conference on Computer Science and Software Engineering, Porto, 10-12 July 2013, 23-30. https://doi.org/10.1145/2494444.2494458 [16] Sujit, N.R., Kumar, C.S. and Rajesh, C.B. (2016) Improving the Performance of Cardiac Abnormality Detection from PCG Signal. Advancement in Science and Technology: Proceedings of the 2nd International Conference on Communication Systems, 1715. https://doi.org/10.1063/1.4942735 [17] Tong, Z. (2015) An Integrated Framework for Cardiac Sounds Diagnosis. 190 Circuits and Systems

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