Detection of Heart Condition by Time and Frequency ... - IEEE Xplore

2014 17th International Conference on Computer and Information Technology (ICCIT)

Detection of Heart Condition by Time and Frequency based Template Al Jumlat Ahmed

M.K.M. Rahman

Centre for Energy Research United International University Dhaka, Bangladesh [email protected]

Department of Electrical and Electronic Engineering United International University Dhaka, Bangladesh [email protected]

whole cardiovascular system, triggered by pressure gradients, is responsible for the sound that is heard externally.

Abstract— The phonocardiogram (PCG) is an easy and costless yet powerful tool to detect the heart condition. While the cardiovascular disease is an increasing global threat to humanity, there is a decrease in doctors’ capability for diagnosing these diseases by auscultation. In this work, a neural network based automated system is proposed to aid early detection of these diseases. Two template-based feature representations are developed to effectively represent the characteristics of heart sound. These features, extracted from sound files of known cases, are then used to train a neural network. Our experimental results corroborate that the proposed method can efficiently detect the heart condition with good overall classification accuracy. In addition, a graphical user interface and a low cost device is developed which is user-friendly and can be used without much relevant knowledge.

Traditionally doctors have been using heart sound for to infer the presence of valvular disease for which they require to grow the clinical skills during their training period. This skill is difficult to master as various sounds occur in the short time interval such as lung sound along the heart sound [4, 5]. According to [6], human ear is poorly capable to obtain information both qualitatively and quantitatively from cardiac auscultation. Thus this method is very subjective as the sound analysis by auscultation heavily depends on the skills and experience of the listener as well as hearing device. According to [7] doctors are not trained enough to master this skills of auscultation. In modern days, even though heart auscultations are recorded through digital stethoscope to pc for later playback. Lack of common audio platform such as speaker or earphone can cause serious obstacle in learning heart auscultation. The phonocardiogram (PCG), however, can lead to more reliable diagnosis if it is presented visually to the doctors instead of listening to it. In [8], the use of electronic stethoscope and visual display of heart sounds (i.e. PCG) are shown to be promising to physicians. This will save some extra money of the patients by avoiding unnecessary referrals to cardiac specialists by general practitioners. This diagnosis process can further be automated by techniques of signal processing and artificial intelligence.

Keywords— Classification of Heart diseases; Neural Network; Time Domain Template; Frequency Domain Template

I.

INTRODUCTION

According to a statistics in 2003 by World Health Organization 29% of global deaths are caused by cardiovascular diseases [1]. While the cardiovascular disease is an increasing global threat to humanity, there is a decrease in doctors’ capability for diagnosing these diseases by auscultation [2]. Hence, any development to aid early detection of these diseases has great importance. Listening to heart sound, briefly known as Auscultation, is a primary and very important method to extract heart information because of higher cost and limited availability of other newer techniques such as ECG.

Some cardiovascular conditions are well-reflected in the heart sound before their signatures appears in other signals such as ECG [9]. Therefore early diagnosis by heart sound is crucial for many patients suffering from cardiovascular diseases. According to [10] detection and diagnosis of heart disease is a very sophisticated because of the dependence not only on heart sound but other factors as well. The traditional use of stethoscope does not provide us any quantitative analysis.

Listening to heart sound through stethoscope is the primary method by which a patient is primarily diagnosed for possible heart disease. While the lung sound is best heard on back, heart auscultation is best captured from thorax. Also, four different valves of the heart are best diagnosed by auscultation from four specific positions of thorax. The heart sound consists of two major parts called S1 and S2 that are also know as lubb-dubb. It is believed that S1 is associated with the closure of mitral and tricuspid valves while S2 is associated with the closure of aortic and pulmonary valves. However, according to recent study [3] the vibration of the

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Fast Fourier Transform (FFT) is used to characterize and classify PCG signals [11]. The used of FFT co-efficient can provide satisfactory classification to some extent; however, it

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cannot accommodate temporal information which is important to classify dynamic signals like PCG whose statistical properties change with time [6]. To encode temporal information Short Time Fourier Transform (STFT) [12] was applied where FT was performed through a small window in time domain and the window move along time axis. However, STFT lacks of encoding sensitive change in the time direction [13].Wigner Distribution (WD) and Choi-William Distribution (CWD) are techniques that provide 2-dimentional view of frequency and temporal information, and has been applied on PCG signals [6, 14, 15]. These techniques provide better resolutions than STFT but still not very suitable due to the presence of some unwanted cross-terms. Wavelet analysis [16], a time-scale representation where scale can be thought of as inverse-frequency, is another popular technique to include temporal information of signal. Coefficients from Discrete Wavelet Transform (DWT) or Fast Wavelet Transform (FWT) have been used to analyze PCG signals [12-13]. In modern days, advanced techniques like Fourier analysis and Wavelet analysis are adopted in digital phonocardiograph to quantitatively analyze the heart sound [17]. However, the numbers of heart diseases classified using these techniques were quite low till Artificial Intelligence (AI) has been introduced.

extracted that are analyzed by a trained neural network to detect the heart condition. The basic contribution of this work is signal processing and features extraction represented by second and third block in Fig. 1, where we have developed efficient features to effectively represent the characteristics of heart condition in time and frequency domains that is explained in the following section. PCG signal Record

Signal Processing

Feature Extraction

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Figure 1. Block Diagram of the proposed Method

A. Feature Extraction in Time Domain We have collected PCG signals from different sources which are used for training of physicians [24-26]. To avoid its affect on the performance of time domain features, a common starting point for all signals is determined and phase shifting is removed. After that, the envelope of PCG signal in time domain is extracted. The envelope of a signal gives a good indication about heart valve condition. Template based time domain feature is created by segmenting and down sampling the envelope signal. The flow chart of the process is shown in Fig. 2.

Among various AI techniques, Artificial Neural Networks (ANN) are most popular for classification of heart sound [1820]. ANNs are capable of modeling complex non-linear system like heart sound. Multi-Layer Perceptron (MLP) is most commonly used ANN model used for classification of heart sound. Besides ANN, Hidden Markov Models (HMM) has been used to analyze and classify heart sound [21-22]. In HMM, the heart sound is modeled by a set of observation states along the time, and a heart sound category is detected by detecting specific observation sequence. Different other classification schemes include decision tree [11], Linear discriminant function [23], and Bayes’ decision rule [15]. In this paper, we have explored the possibility of PCGbased detection of valvular diseases using both time domain and frequency domain information. We have used two feature representation called Time-Domain-Template (TDT) and Frequency-Domain-Template (FDT) to encode essential characteristic of different valvular diseases from heart sound. Using these two feature representations from four different valvular disease and healthy conditions, an MLP type neural network is trained for classification of heart condition. After training the neural network, the system can provide diagnosis on any unknown heart sound either it is from healthy condition or any of four valvular disease conditions. II.

Figure 2. Flow Chart of Time Domain Feature Extraction Process

To adjust the phase shift, we like to find the first point (k) whose value is less than 2% of average valve. A new vector X s is created from original time domain signal X. X s = [ X ( k ), X (k + 1), …… X ( N − 1), X ( N )]

Any abnormality in the valve of heart has an impact on the envelope of phonocardiogram signal. The Hilbert transform is a one of the envelop detection technique [27] which has been used to detect the envelope of PCG signal. We have used built-in function of MATLAB for Hilbert transform. Then Moving Average filter is used for smoothing the signal.

PROPOSED METHOD

Fig. 1 shows the basic block diagram of the proposed method for detecting heart condition. The whole system works in the following manner: at first, system records the PCG data from human subjects. Then characteristic features are

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To start, we do the same Hilbert Transform as before to detect the length of a period. We use the period information to collect the data segment (samples of 3 periods from the original signal). Then we take the Fast Fourier Transform (FFT) of the segmented signal and take the absolute value of FFT. First half of the FFT signal is used for down sampling. After down sampling we get a feature vector of 10 samples.

X h = H (X s )

X f (i ) =

1 [ X h (i + N ) + X h (i + N − 1) + …… + X h (i − N )] 2N + 1

After filtering the signal, we have detected period “ l ” of the signal with help of autocorrelation. Then a segment X p (equal to length of 3 l ) is selected to make time domain features concise and effective. Each segment has three period of the original signal X f .

Because the period of PCG data or Instantaneous Heart Rate (IHR) may vary from subject to subject and we are using three periods of data, we may end of feature vector of different lengths even for a fixed sampling frequency. It is specially difficult for a classifier to deal with such problematic situation. Therefore, an interpolation technique (IP) is used project initial PCG data X pf into new data vector X ip such

Now the signal X p has got two issues: it has too many samples and its lengths from different sources are different. To reduce the sample number and to make the sample number of different segmented signals to be in same size, we have used the down sampling technique to convert each segmented signal into template based time-domain-feature vector X dt of

that the new vector maintain same length for all PCG data of different IHR and sampling frequencies. X ip = IP ( X pf ) In our feature extraction process, we have used the “Linear Interpolation Method”. Fast Fourier Transform “fft” is applied to the signal X ip to get the data X fft .

length 120. X dt (i ) =

X p (i × N − ( N − 1) ) + X p (i × N − ( N − 2) )…… + X p (i × N ) ) N

Where, N is the rate of down sampling. This X dt is the feature vector called time domain template (TDT).

X fft = fft (X ip ) = [X a (i ) X a (i + 1) …… X a ( N )

]

N +1 ⎤ ⎡ X hfft = ⎢ X a (i ) X a (i + 1) …… X a ( )⎥ 2 ⎣ ⎦

B. Feature Extraction in Frequency Domain Cardiovascular diseases can be detected by extracting high and low frequency components from the ECG signal [28]. The frequency domain representation can contain as much information as time domain. In addition, it is independent of time shifting property so we do not need to define the unique starting for every signal. The flow chart in Fig. 3 illustrates the process.

After FFT, we have taken only the magnitudes and discarded second half of the output vectors because of symmetrical nature of the signal. Again, to make the feature vector concise and robust, we have taken a down sampled version the signal X df that preserves the shape of the FFT. X df = DS ( X hfft ) .

Interesting thing about this part of down sample is that, only 10 sample is sufficient to hold the characteristics of FFT. This is the desired frequency domain signal known as Frequency Domain Template (FDT). C. Hybrid Feature Hybrid feature (HF) contains both Time and Frequency Domain Features. We cascade template based time and frequency domain features vector to construct the hybrid feature vector. First 120 samples of HF are time domain template (TDT) features and last 10 samples are frequency domain template (FDT) features. Therefore, the total length of HF vector is 130. HF = [X dt

D. Neural Network Based Classifier We use neural network as classifier to learn and recognize patterns. Fig. 4 describes a feed forward neural network [18] where information moves only in one direction: from input to output. The neural networks can contain multiple layers. For

Figure 3. Flow Chart of Frequency Domain Template Extraction

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X df ]

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our problem, a three-layer network is used. There are weights in between two layers that are multiplied with information when passing from one layer to another. There are neurons in middle or hidden layer and in the output layer. These neurons receive and combine information from previous layer and use a linear or non-linear activation function to generate new values. It should be noted that the number of input of neural network depends on the length of feature vector used for a specific problem. The number of output neurons depends on how we model the output or class information for a classification task. And finally the number of hidden neuron depends on the complexity of the relation between input and output, or in other words it depends on classification problem.

Stenosis, Aortic Regurgitation, and Mitral Regurgitation respectively. This dataset is divided into two parts: training dataset and testing dataset. The training dataset together with data class labels have been used to train the Neural Network. We have not used the testing dataset for training of neural network so that we can evaluate the performance of neural network in the case of unseen data. B. Signals at Different Stages of Extracting TDT and FDT The PCG signals at different stages of TDT feature extraction are shown in Fig. 5, where we can see how the signals changes after 1) phase-adjustment, 2) Hilbert Transform & Moving Average Filter and 3) Segmentation and down-sampling. Similarly, Fig. 6 shows the PCG signals at different stages of FDT feature extraction. We can see that phase-shift-adjustment was not performed during FDT extraction. 0.4

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A three-layer MLP network is used to learn different heart conditions. The number of inputs for the network was set according to number of features which was 120, 10 and 130 for TDT, FDT and hybrid features respectively. For our problem, we have used 20 hidden neurons and 200 training cycles that appears to be sufficient for our problem. The number of output of the network was set to 5 to handle 5 different classes of heart conditions, for which class information are numerically encoded as [10000], [01000], [00100], [00010] and [00001]. The network weights are initialized with a zero mean and unit variance isotropic Gaussian. ‘tanh’ and ‘softmax' activation functions are used for hidden and output neurons respectively. The network is optimized using Scaled Conjugate Gradient (SCG) optimization algorithm. After training the neural network with the training dataset, neural weight vectors are saved for future classification of unseen data.

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III.

RESULT AND DISCUSSION

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A. Dataset and Neural Network Training We have applied our method to identify five different conditions of heart valve and they are Normal heart valve condition, Mitral Stenosis, Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation. These signals are used to train physicians. PCG signals related with different valve diseases of heart are available in these sources in wma and au format. We have used multi layer perception (MLP) type Neural Network Classifier (NNC). The collected signals from [24-26] contains 83 data, where 9, 17, 18, 21 and 18 data are from Normal heart valve condition, Mitral Stenosis, Aortic

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Figure 6. a) Original Signal, b) Segmented signal after Linear Interpolation, c) Fast Fourier Transform, d) Frequency Domain Template (FDT)

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C. Comparison of TDT and FDT Feature

Confusion matrix summarizes the complete results in tabular form where we can see what percent of data from each class is classified correctly, and how the rest of the data are wrongly classified in the other classes. Table 1 summarizes the results from TDT features: normal heart data 100% correctly classified and no other classes get confused with this class. However, Aortic Stenosis and Mitral Regurgitation got relatively poor classification results. Some data from these two classes are wrongly classified into the class of Aortic Regurgitation. Thus, Aortic Regurgitation, even though having around 90% accuracy for its own data, got more confusion with other classes. Similarly, confusion matrix for the FDT features is given in Table 2. We can see that FDT based results are comparatively better. Only 10% data of Aortic Regurgitation got confused with the class Mitral Regurgitation. Rest of the data is correctly classified in their respective classes. Finally, the results using Hybrid features are show by the confusion matrix in Table 3. The results are similar but improved compared to that of TDT features.

Now we have both TDT and FDT features, we like to compare them for different heart conditions. The TDT features for different heart conditions are compared in Fig. 7. It is easily understandable that templates of signals corresponding to different heart valve conditions are not similar to each other and the differences are effortlessly identifiable by visual inspection. Similarly, FDT features of different heart valve conditions are shown in Fig. 8 that shows the differences in their shape. If we examine these signals carefully then we will get some different which are key points to detect different heart valve conditions 0. 7

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TABLE 1. CONFUSION MATRIX OF CLASSIFICATION OF THE TESTING DATASET FOR TDT (IN PERCENTAGE) Mitral Heart Valve Normal Mitral Aortic Aortic Conditions Stenosis Stenosis Regurg Regurg -itation -itation Normal 100 0 0 0 0 Mitral 0 88.89 0 11.11 0 Stenosis Aortic 0 0 77.78 22.22 0 Stenosis Aortic 0 0 0 90 10 Regurgitation Mitral 0 0 0 22.22 77.78 Regurgitation

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Figure 7. TDT features for a) Normal Heart Valve, b) Mitral Stenosis, c) Aortic Stenosis, d) Aortic Regurgitation, e) Mitral Regurgitation 50

TABLE 2.

CONFUSION MATRIX OF CLASSIFICATION OF THE TESTING DATASET FOR FDT (IN PERCENTAGE) Mitral Heart Valve Normal Mitral Aortic Aortic Conditions Stenosis Stenosis Regurg Regurgitation -itation Normal 100 0 0 0 0 Mitral 0 100 0 0 0 Stenosis Aortic 0 0 100 0 0 Stenosis Aortic 0 0 0 90 10 Regurgitation Mitral 0 0 0 0 100 Regurgitation

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TABLE 3. CONFUSION MATRIX OF CLASSIFICATION OF THE TESTING DATASET FOR HF (IN PERCENTAGE) Mitral Heart Valve Normal Mitral Aortic Aortic RegurConditions Stenosis Stenosis Regurggitation itation Normal 100 0 0 0 0 Mitral 0 100 0 0 0 Stenosis Aortic 0 0 77.78 11.11 11.11 Stenosis Aortic 0 0 0 90 10 Regurgitation Mitral 0 0 0 22.22 77.78 Regurgitation

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Figure 8. FDT features for a) Normal Heart Valve, b) Mitral Stenosis, c) Mitral Regurgitation, d) Aortic Stenosis, e) Aortic Regurgitation

D. Performance Analysis of Neural Network At first, we like to present the classification results of testing dataset through confusion matrix. As the testing dataset has not been used during the network training, it will tell us how good the performance of NN classifier is for unseen data.

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E. Graphical User Interface (GUI) We have developed a simple MATLAB based Graphical User Interface (GUI).With help of our (GUI) and a simple hardware connected to a computer anyone can check up his/her heart valve condition. The person also can see signals at different stages of processing. The system gives a report within few seconds. Fig. 12 shows such GUI named PCG Signal Analyzer. It is very user friendly and previous knowledge is not required to operate the GUI. There are mainly three sections in the GUI, Signal Display Unit, Result Display Unit and Signal Input Unit Anyone can use this after getting a short introduction about the system.

The overall Classification results of Neural Network for Time Domain Template (TDT) Feature, Frequency Domain Template (FDT) Feature and Hybrid Feature (HF) are shown in Fig. 9, Fig. 10 and Fig. 11 respectively. Here, we have summarized the results from training, testing and combined dataset. All classification results are averaged over several trials. Comparing all these results it is evident that the FDT features deliver better classification results compared with other two features.

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Figure 9. Performance of Neural Network for TDT

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Figure 10. Performance of Neural Network for FDT Input Unit Figure 12. The Graphical User Interface (GUI).

F. Hardware A very simple hardware setup for recording audio signal in computer is shown in the Fig. 13. There are mainly three components in the hardware setup, a normal Stethoscope, microphone and an audio jack to connect it with the computer. The microphone is inserted into the pipe of stethoscope and two pieces of supporting sheet is used in the connecting part of the stethoscope and microphone to make it strong. The microphone is connected to computer with help of an audio jack. With this simple setup, it is possible to record audio signal of heart but it needs further improvement to reduce the effect the noise.

Figure 11. Performance of Neural Network for HF

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[6] Microphone (Inserted into the pipe of stethoscope to convert sound signal into electrical signal)

[7] [8]

Audio Input Jack (To connect microphone with t )

[9] [10]

[11] Stethoscope (To capture heart sound from the chest of the patient)

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Figure 13. Hardware Setup [13]

IV.

CONCLUSION

In this work, a neural network based automated system is proposed to aid early detection of these diseases. Two template-based feature representations are developed to effectively represent the characteristics of heart sound. These features, extracted from sound files of known cases, are then used to train a neural network. Our experimental results corroborate that the proposed method can efficiently detect the heart condition with good overall classification accuracy.

[14] [15]

[16] [17]

In addition, a graphical user interface and a low cost device is developed which is user-friendly that can be used as a home healthcare system as well as professional diagnosis machine. Some improvement of the hardware setup is required to nullify noise more effectively. In this work only five conditions of heart valve are considered. But it is possible to determine other valvular diseases with the same setup and algorithm as long as they have some effect on phonocardiogram signal.

[18] [19]

[20]

ACKNOWLEDGMENT We like to thank Obaidul Haque, Mohammad Fattah Halim and Sadia Anjum Jeba, who helped us carrying out the experiments and MATLAB coding.

[21]

[22]

REFERENCES [1]

[2]

[3] [4] [5]

American Heart Association: International Cardiovascular Disease Statistics. April 2006. Available at: http://www.americanheart.org/downloadable/heart/1140811583642 -InternationalCVD.pdf S. March, J. Bedynek and M. Chizner, “Teaching cardiac auscultation:e ectiveness of a patient-centered teaching conference on improving cardiac auscultatory skills,” Mayo Clinical Proceedings, vol. 80, pp. 1443–1448, 2005. R.M. Rangayyan, “Biomedical Signal Analysis: A Case-Study Approach,” Wiley-Interscience, New York, 2002. G. Saha and P. Kumar, “An e cient heart sound segmentation algorithm for cardiac diseases.” In: Proceedings of the IEEE India Annual Conference. 2004. T.R. Reed, N.E. Reed and P. Fritzon, “Heart sound analysis for symptom detection and computer-aided diagnosis,” Simulation Modelling Practice and Theory, vol. 12, pp. 129–146, 2004.

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[23] [24] [25] [26] [27]

[28]

412

M. Obaidat, “Phonocardiogram signal analysis: techniques and performance comparison,” Journal of Medical Engineering & Technology, vol. 17, pp. 221–, 1993. M. Kearney, “Medical research council statistics of 2003,” Heart Foundation South Africa, April 25 2006. M.E. Tavel, “Cardiac auscultation: A glorious past - and it does have a future!,” Circulation, vol. 113, pp. 1255–1259, 2006. L. Huiying, L. Sakari and H. Iiro, “A heart sound segmentation algorithm using wavelet decomposition and reconstruction,” In Computers in Cardiology, pp. 1630–1633. 1997. S.A. Pavlopoulos, A.C.H. Stasis and E.N. Loukis, “A decision treebased method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds,” BioMedical Engineering Online, vol. 3, no. 21, 2004. S.R. Bhatikar, C. DeGro and R.L. Mahajan, “A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics,” Artificial Intel ligence in Medicine, vol. 33, pp. 251– 260, 2005. I. Turkoglu, A. Arslan and E. Ilkay, “An expert system for diagnosis of the heart valve diseases,” Expert Systems with Applications, vol. 23, pp. 229–236, 2002. S.M. Debbal and F. Bereksi-Reguig, “Heartbeat sound analysis with the wavelet transform,” Journal of Mechanics in Medicine and Biology, vol. 4, pp. 133–141, 2004. F. Auger, P. Flandrin, P. Goncalves and O. Lemoine, “TimeFrequency Toolbox for use with Matlab,” CNRS, 1996. P. Bentley, P. Grant and J. McDonnell, “Time-frequency and timescale techniques for the classification of native and bioprosthetic heart valve sounds,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 1, pp. 125–128, 1998. Wavelet Toolbox User’s Guide, Chapter 1. Wavelets: A New Tool for Signal Analysis. Mathworks Inc., 1996. S. Leeder, S. Raymond and H. Greenberg, “A race against time: The challenge of cardiovascular disease in developing economies,” 2004. Available at: http://www.ahpi.health.usyd.edu.au/pdfs/colloquia2004/leederracep aper.pdf T. Ölmez and Z. Dokur, “Classification of heart sounds using an artificial neural network,” Pattern Recognition Letters, vol. 24, pp. 617–629, 2003. N. Andrisevic, K. Ejaz, F. Rios-Gutierrez, R. Alba-Flores, G. Nordehn and S. Burns, “Detection of heart murmurs using wavelet analysis and artificial neural networks,” Journal of Biomechanical Engineering, vol. 127, pp. 899–904, 2005. Y.M. Akay, M. Akay, W. Welkowitz and J. Kostis, “Noninvasive detection of coronary artery disease,” IEEE Engineering in Medicine and Biology, vol. November/December, pp. 761–764, 1994. A.D. Ricke, R.J. Povinelli and M.T. Johnson, “Automatic segmentation of heart sound signals using hidden markov models,” Available at: http://povinelli.eece.mu.edu/publications/papers/cinc2005b.pdf, [2006, May 08], 2005. M. El-Hanjouri, W. Alkhaldi, N. Hamdy and O.A. Alim, “Heart diseases diagnosis using hmm,” In IEEE Melecon, May 7-9, pp. 489–492. Cairo, Egypt, 2002. ISBN 0-7803-7527-0. A. Voss, A. Mix and T. Hübner, “Diagnosing aortic valve stenosis by parameter extraction of heart sound signals,” Annals of Biomedical Engineering, vol. 33, no. 9, pp. 1167–1174, 2005. http://courses.washington.edu/bioen303/Lab/labdatafiles.htm http://www.easyauscultation.com/heart-sounds http://www.openheartsurgery.com/heart-murmurs.php M. Schlaikjer, J.P. Bagge, O.M. Sorensen, J.A. Jensen, “Trade off study on different envelope detectors for B-mode imaging,” IEEE Uitrasonics, vol. 2, pp. 1927-1930, 2003, ISBN: 0-7803-7922-5 M.R. Islam, S. Ahmad, K. Hirose and Md.K.I. Molla, “Data adaptive analysis of ECG signals for cardiovascular disease diagnosis,” In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), vol., no., pp.2243,2246, 2010.