Abstract— Heart sound is generally used to determine the human heart condition. Recent reported research proved that cardiac auscultation technique which uses the characteristics of phonocardiogram (PCG) signal, can be used as biometric authentication system. An automatic method for person identification and Verification from PCG using wavelet based feature set and Back Propagation Multilayer Perceptron Artificial Neural Network (BP-MLP-ANN) classifier is presented in this paper. The work proposes a time frequency domain novel feature set based on Daubechies wavelet with second level decomposition. Time-frequency domain information is obtained from wavelet transform which in turn is reflected in wavelet based feature set which carries important information for biometric identification. Database is collected from 10 volunteers (between 20-40 age groups) during one month period using a digital stethoscope manufactured by HDfono Doc. The proposed algorithm is tested on 4000 PCG samples and yields 90.52% of identification accuracy and Equal Error Rate (EER) of 9.48%. The preprocessing before feature extraction involves selection of heart cycle, filtering for noise reduction, aligning and segmentation of S1 and S2. Performance of the classifier is determined from the Receiver operating curve (ROC). The experimental result shows that the performance of the proposed algorithm is better than the other reported technique which uses Linear Band Frequency Cepstral coefficient (LBFCC) feature set.

Index Terms—Heart sound; Authentication; Daubechies; Identification; Verification

I. INTRODUCTION Many human physiological and behavioral traits are used

as parameters for identifying or verifying an individual. Among many parameters like fingerprints, voice, signature etc, the possibility of counterfeiting is present [1]. Heart sound, which is produced by sudden closure of atrioventricular and semilunar valves, on other hand are Heart completely natural physiological signals, which is almost impossible to reproduce exactly similar by artificial means. sound consists of three major sounds, first heart sound (S1), second heart sound (S2) and Murmur [2]. Figure 1 shows a normal heart sound and positions of S1, S2 and murmur. Features which are considered as most important traits for a biometric

Girish Gautam is with Electronics and Communication Department,

National Institute Of Technology, Rourkela, India email: girish.nitrkl@gmail.com

Deepesh Kumar is with Electronics and Communication Department, National Institute Of Technology, Rourkela, India email: deepeshnitrkl@gmail.com

.

system are: [3] Correct detection: A good biometric system should be ac- curate with minimum false accept rate (FAR) and high true accept rate (TAR). Speed: A quick response is very desirable from a biometric system. This includes quick data acquisition, quick enrollment of inputs and quick processing.

Fig. 1: Normal Heart sound in time domain. Reliability: The system should be resistant to forgery and the detection should be trustworthy in changing environment. Universality: The biometric feature trait should be present in every living individual. Easy accessibility: The feature trait that the system uses should be easily accessible. Invariability: The system performance should remain same over a long duration of time. Apart from these, there are many selling qualities, like data storage requirements, cost, sensor quality etc.

Biometric authentication systems are composed of two phases. In the first phase the database is created where the feature sets which describes each individual is stored. In the second phase the extracted feature sets are compared with the feature templates stored in the database to find a match. Figure 2 shows these two phases in block diagram.

Fig. 2: Process of Identification

The proposition of phonocardiogram signal for

identification has been studied in many literatures so far. The option of Phonocardiogram signal in biometric identification was first introduced in [4]. The proposed method required

Biometric System from Heart Sound using Wavelet based feature set

Girish Gautam, and Deepesh kumar

978-1-4673-4866-9/13/$31.00 ©2013 IEEE

International conference on Communication and Signal Processing, April 3-5, 2013, India

551

localization and delineation of S1 and S2 which is followed by Z-Chirp (CZT) transform. Feature set was classified using Euclidean distance measure. [5] Proposes a Short Time DFT (STDFT) method to get Cepstral feature set followed by classification using Gaussian Mixture Model (GMM). [6] Further improves the method using wavelet based noise reduction technique. But GMM based technique is slow which creates a disadvantage for biometric usage. A fusion technique of Mel Frequency Cepstral Coefficient (MFCC) and First to Second Ratio (FFR) was introduced in [7]. But the fusion and selection of clean heart sound increases the computation cost and time both. [8] used filter bank based Cepstral analysis. Parameters of the filter banks were varied to get the optimum values. Wavelet based approach described in [10], used sum of the energy of different scales but no results upon % of accuracy or EER was given.

The peaks and valleys of S1 and S2 carry important biometric information for individual [11]. To entrap these information a time frequency domain analysis like wavelet transform should be suitable. The rest of the paper is organized as follows. In the next section we introduced the theoretical background of wavelet based approach. The process of database collection is described in section three. Next, the proposed methodology for extracting the feature set followed by the classification process is described step wise. The performance and comparison is given in section five.The rest of the paper is organized as follows. In the next section we introduced the theoretical background of wavelet based approach. The process of database collection is described in section three. Next, the proposed methodology for Extracting the feature set followed by the classification process is described step wise. The performance and comparison is given in section five. Finally, section six gives a summary and conclusions.

II. THEORETICAL BACKGROUND In wavelet theory a function )( xf of a continuous

variable x, which belongs to a square integrable subspace L2(R), i.e. )()( 2 RLxf ∈

[ ]∞

=

≥+=0

,,,0,0 0),()()(rr s

srsrs

srsr rrherexbxaxf ψφ (1)

Here φ r,s (x) represents a scaled and shifted (r=scaling parameter, s=shifting parameter) version of a function (x), called basic scaling function . It is of the form r,s(x)=2r/2 (2rx s) (2)

r0,s (x) is a scaling function whose scaling parameter is fixed in r = r0. And r,s (x) represents a scaled and orthogonally Shifted version of function (x) called ’basic wavelet function’. And it is of the form: r,s (x) = 2r/2 (2r x s) (3)

The psi(x) function can be represented as series sum of phi function in the following manner,

−=n

nxnhx )2(2)()( φψ ψ (4)

Provided the function f (x), the scaling function (x), and wavelet function (x) given the coefficients and can be found by the following equations respectively,

dxxxfa srsr )()( ,0,0 φ∞

∞−= (5)

dxxxfb srsr )()( ,0,0 ψ∞

∞−= (6)

In real life however we more often than not encounter discrete signals for processing. So instead of continuous function x if we consider a function of discrete samples n=0,1,....., M-1, then in discrete domain the equation (1) can be represented as:

)(),()(),0(1)( ,0

,0 nkjWnkjWM

ns kjjj k

kjk

ψφ ψφ

∞

=

+= (7)

j and k are discrete scaling and shifting parameter

M1 is

the normalizing term j0,k (n) and j,k (n) are the discrete counter part of the coefficient ar0,s and br0,s These coefficient can be found out from the discrete equipment of the equation (4) and (5)

)()(1)( ,0,0 nnsM

jW kjn

k φφ = (8)

)()(1),( ,0 nnsM

kjW kjn

ψψ = (9)

The discrete equivalent of equation (3) and equation (4) can be written in discrete domain respectively as, j,k = 2j/2 (2j n k) (10)

)2(2)()( pnphn

p

−= φψ ψ (11)

Putting knn j −= 2 in equation (11) and substituting the )2( knj −ψ term in equation (10) we get the value of .,kjψ is

substituted in equation (9) to determine ),( kjWψThis value

can then be represented as , +−= ),1()2(),( mjWkmhkjW φψψ

(12)

Here kpm 2+= and by similar process it can also be shown +−=

m

mjWkmhkjW ),1()2(),( φφφ (13)

Equation (12) and (13) is easily realizable [12] As shown in following figure

Fig. 3-Realization of wavelet coefficient

552

III DATABASE In this work ten volunteers contributed to the construction of the database of heart sound. A digital stethoscope manufactured by HD Medical Services (India) Pvt Ltd, was used. Through USB connectivity the instrument can directly store the heart sound into PC in Waveform Audio File Format, which is more commonly known as WAV format. The volunteers were in the age group 20-35. For a particular volunteer each sample (of 20 second duration) was collected with a minimum time gap of one hour.

There are four auscultation sites in the chest region from where the heart sound can be collected. These are Aortic, Pulmonary, Lower sternal border and Mitral [4]. We preferred the Mitral region during data collection

IV PROPOSED METHODOLOGY Heart sound is in the frequency range 20-200 Hz. Figure 4 shows block diagram of our proposed methodology

Fig. 4: Block diagram of Proposed. Methodology

A. Normalization Normalization is used to restrict the signal within a fixed range (1 to -1). The maximum amplitude is taken as 1. B. Low pass Butterworth filter This technique removes all the noise component beyond 300 Hz. Most of the high frequency noises are removed in this stage. C. Heart cycle Extraction by Autocorrelation Heart cycle is a quasi-periodic signal with normal period is in the range of 0.7-0.8 second. Three seconds PCG sample were extracted. A lag index is created ranging from 0.4 to 1.2 second with step of 0.05 second. Now sum of the Autocorrelation of these lag indexes were determined. The position of the peak is determined and its corresponding time is considered as duration of one heart cycle. The whole sample is divided into cycle blocks.

Fig. 5: Figure shows extracted three second heart sound sample (up left

corner), output of the sum of the Autocorrelation of lag indexes in 0.4 - 1.2 second (up right corner) and the determination of the peak and its

corresponding time lag (down left and right corner).

D.Aligning It becomes very important to align the heart cycle properly

so that in every block the location of S1 and S2 matches. This will ensure that every block has one S1 followed by S2. The heart sound was aligned using cross correlation between two different blocks for a same person. Figure 6 shows two extracted cycle blocks which were correlated. Correlated function will have three regions which are labeled as A, B and C in the figure. Region A in the cross correlation is generated because S2 of second block coming under S1 of first block, B because S1 and S2 of the second block coming under the S1 and S2 of the second block, C is coming when S1 of second block coming under S2 of the first block. The peak is coming when the both blocks are perfectly aligned. The distance of peak from the Centre point is the amount of shift required to align the two blocks properly.

E.Segmentation

Step 1: We perform y(n)= (x(n))2 -0.5*{x(n-1)+x(n+1)} (14)

After this operation the effect of the murmur part is reduced and the effect of the S1 and S2 segment part is increased. Step 2: Divide y (n) into small segments (50 samples) and find out the variance of the window. The variance operations further enhances the S1 and S2 segments

Fig. 6: Aligning by Cross correlation operation

553

Fig. 7: Segmentation process

Step 3: By windowing and threshold operation we deter- mined the exact locations of the S1 and S2. By selecting a window by this process the position of S1 and S2 is determined and extracted.

Fig. 8: Process of finding out location of S1 and S2 F. Feature Extraction

The extracted S1 is sent first followed by S2. Using Daubechie’s 2 2nd order wavelet the signal is decomposed up to second level. Selected second level detail coefficient D2 is divided into N= 20 windows. And the average energy is determined from each window using the equation 14.

Fig. 9: Block diagram of Feature Extraction operation

=

=N

nWT nX

NkF

1

2 ][1][ (15)

So final feature length is 40. Where first 20 is feature extracted from S1 and next 20 is feature extracted from S2 . G. Classification

Back propagation MLP ANN is very popular due to its simplicity and quick processing. MLP ANN is preferred choice of classification for speech recognition [13]. The features extracted have dimension of 40. Therefore the input layer of the ANN will have 40 input nodes. Input training vector is provided to the input nodes. These input values flow in the forward direction from input to hidden to output layers to get the output values. The output values are compared with desired target values to generate error E signal. This error signal is then send back to change the weight Wi j. This process continues until the error E reaches a permissible optimal value ET. A detail of Multilayer Perceptron structure and its algorithms are given in [14] . Once the training or learning is complete we sent the testing samples in the input nodes and check the output from output nodes.

Fig. 10: Structure of Artificial Neural Network

For training, feature was extracted from four minute long

heart sound for each individual. And for testing 20 sec samples were used. For each extracted feature vector the trained system will give an output. The output which is repeated most of the times for full feature vectors is taken as the person indicated by the system. Table 1 shows the output of the system for 10 samples, one from each individual of 20 second duration, randomly fed to the trained system. It can be seen that the highest no of output is taken as the indicated person.

V. RESULT AND DISCUSSION

It was observed that the proposed work the Network with 10 hidden nodes is giving best results when both accuracy and computation time are considered. We compared with LBFCC features by same classification process. The data collected in normal laboratory condition and was corrupted with noises and interferences due to hand movement, change of the stethoscopes air column transfer function, Electromagnetic Magnetic noise etc. Table 1 shows the identification results. There were 10 classes. The Network was trained using 200 heart cycle samples features for each class. After training the network it is tested with testing features of 8 heart cycles. Each of such testing sample can be acquired from 15 to 20 seconds of PCG.

The classification performance of the network is presented in figure 11. Receiver operating curve (ROC) is a graphical plot of sensitivity or true positive rate vs. false positive rate, as discriminative threshold is varied. If we take threshold c, define binary test from continuous test result Y as, Positive if Y > c and negative if Y < c . The corresponding true and false positive fractions at the threshold c be T P F (c) and F P F (c) respectively, where T P F (c) = P [Y > c D = 1] (16) F P F (c) = P [Y < c D = 0] (17)

The ROC curve is the entire set of possible true and false Positive fraction attainable by dichotomizing Y with different thresholds. That is ROC curve is ROC (.) = (F P F (c), T P F (c)), c ( , ) (18)

554

TABLE-I- CONFUSSION MATRIX P1 P2 P3 P4 P5 P6 P7 P8 P9 P10P1 179 3 5 2 0 3 4 1 2 0 P2 4 183 3 2 2 0 5 1 2 0 P3 8 0 180 5 4 3 2 0 2 1 P4 8 0 0 189 0 3 6 2 0 2 P5 0 0 6 7 191 3 1 1 1 1 P6 10 2 0 4 0 171 2 0 1 0 P7 9 0 1 3 0 1 180 2 1 2 P8 8 2 2 0 2 4 1 190 0 1 P9 0 5 4 3 2 1 3 0 193 0 P10 3 2 2 0 1 1 5 3 2 195 Total sample tested=2044, Correct identification=1851 Accuracy=90.52% Performance of a biometric system is determined from the EER. To check the effectiveness of [13] this feature extraction process we compared with the feature extracted in [13]. Table 2 shows the comparison between linear band frequency Cepstral coefficient and Wavelet based feature extraction. LBFCC is extracted by performing Short Time Discrete Fourier Transform (STDFT) with 0.5 sec windows and

Fig. 11: Receiver Operating Curve

TABLE-II EQUAL ERROR RATE OBTAINED FROM IDENTIFICATION

Cases No. of feature vector EER

1 8 10.32 2 8 9.48

then sending the transformed signal through filter banks, followed by logarithm and DCT and dimension compression. We selected the first 24 coefficients after performing spike removal operation as illustrated in [5]

TABLE-III COMPARISION WITH LBFCC

Feature Classification Accuracy LBFCC MLP-ANN 89.68 Proposed Wavelet based feature

MLP-ANN 90.52

VI. CONCLUSION This paper has proposed a technique for automatic

identification system based on the wavelet based feature set. Because a complete feature set is created from a single heart cycle the proposed method is capable of taking decision on short duration testing sample. The technique is tested upon a collected heart sound data base using digital stethoscope. Comparison with Linear Frequency Cepstral Coefficient based feature set shows that the proposed method gives higher accuracy. REFERENCES [1] R. Bolle and S .Pankanti, “Biometric Personal Identification in

Networked Society : Personal Identification in Networked Society, A.K.Jain, Ed. Norwell, MA, USA : Kluwer Academic Publisher, 1999 [2] S. Lehrer , Understanding Pediatric Heart Sound Saunders, 2003.

[3] C.Vielhauer, Biometric User Authentication for it Security: From Fundamental to Handwriting , ser. Advance in Information Security Springer, 2005. [4] F.Beritelli and S.Serrano, “Biometric Identification based on frequency Analysis of cardiac sounds,” Information Forensics and Security, IEEE Transactions on vol. 2, no. 3, pp. 596-604, sept. 2007 [5] K.Phua, J. Chen, T.H.Dat and L.Shue, “Heart sound as a biometric,” Pattern Recogn, vol 41 no. 3 pp. 906-919, Mar 2008. [6] S.Fatemian , “A wavelet-based approach to electrocardiogram (ECG) and phonocardiogram (PCG) subject recognition,” University of Toronto 2009. [7] F.Beritelli and A. Spadaccinni, “Human Identity Verification based on Mel frequency analysis of digital Heart Sound.” in Digital Signal Processing, 2009, 16th International Conference on july 2009, pp. 1-5 [8] Beritelli Francesco, “An improved biometric identification system based on Heart sound and Gaussian mixture models,” in Biometric Measurement and System for Security and Medical Application (BIOMS), 2010 IEEE Workshop on sept. 2010, pp.31-35 [9] Beritelli, Francecso, “Heart Sounds quality analysis for automatic cardiac biometry application,” in Information Workshop on dec. 2009 pp. 61-65 [10] S.Al-Shamma and M. Al-Noaemi, “Heart sound as a physiological biometric signature,” in Biomedical Engineering Conference (CIBEC), 2010 5th Cairo International, dec. 2010, pp. 232-235 [11] T.Ye-Wei, S.Xia, Z.Hui-xiang, and W.Wei, “A Biometric Identification System based on Heart sound signal,” in Human System Interaction s (HSI), 2010. 3rd Conference on, may 2010, pp. 67-75 [12] S. Mallat, “A Wavelet Tour of Signal Processing,” Third Edition : The Sparse Way, 3rd ed. Academic Press 2008. [13] S.Sae-Tang and C. Tanprasert, “Feature windowing-based Thai text- dependent speaker Identification using mlp with back propagation algorithm,” in Circuits and System, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on, vol. 3, 2000, pp. 579-582 vol3.

555