52

CHAPTER 4

CLASSIFICATION OF HEART MURMURS USING

WAVELETS AND NEURAL NETWORKS

4.1 INTRODUCTION

Heart auscultation is the process of interpreting sounds produced by

the turbulent flow of blood into and out of the heart and the movement of

mechanical structures that control this flow. This non-invasive, low-cost

screening technique is used as a primary tool in the diagnosis of certain heart

disorders, especially valvular problems. The conventional method of

auscultation with a stethoscope has many limitations. The skills required for

interpretation are acquired by listening to the heart sounds of many different

patients. Therefore, it is a very subjective process that depends largely on the

physician’s experience and ability to differentiate between different sound

patterns. For an objective assessment of heart sounds for diagnosis of certain

cardiac disorders, digital recording and subsequent analysis of these acoustic

signals is the only reliable method. Digital sounds can be analysed efficiently

using a computer.

4.2 LITERATURE SURVEY

Faizan et al (2006) have developed a heart murmur classification

system with features extracted using Spectrogram. Seven types of murmurs

are classified using a Multilayer Perceptron Neural Network based on their

timing within the cardiac cycle using Smoothed Pseudo Wigner-Ville

distribution. Zhou et al (2008) have proposed a heart sound recognition

53

system based on features extracted from the normalized average Shannon

energy in wavelet domain. These features are used with Full Bayesian Neural

Networks to classify six types of heart sounds. Yadi et al (2008) report a

classification method based on statistical features extracted using wavelet

decomposition and MLP Neural Network to classify five murmurs. Many new

techniques have been introduced for efficient heart disease diagnosis.

However, they are expensive, require skilled technicians to operate the

equipment and experienced cardiologists to interpret the results (Faizan et al

2006). These facilities are usually available only in advanced hospitals and

not in rural and most urban hospitals. Furthermore, the wavelet transform has

demonstrated the ability to analyse the heart murmurs more accurately than

the techniques like STFT or Wigner Ville Distribution (WVD) cited in

literature. The WVD has the ability to separate signals in both time and

frequency. One advantage of the WVD over the STFT is that it does not

suffer from the time frequency trade-off problem. On the other hand, WVD

has a disadvantage since it shows cross terms in its response and attempts to

smooth these results in decreased resolution in both time and frequency

(Debbal and Bereksi-Reguig 2008). Considering these constraints, a new

approach based on wavelet transform and artificial neural networks has been

proposed for classifying heart murmurs into eight types, namely, normal,

early systolic, mid systolic, late systolic, holo systolic, early diastolic, mid

diastolic and late diastolic. Classification of heart murmurs into more number

of types leads to enhanced accuracy of diagnosis.

4.3 HEART MURMURS

4.3.1 Categories

In healthy adults, there are two normal heart sounds often described

as a lub and a dub, that occur in sequence with each heart beat. These are the

first heart sound (S1) and second heart sound (S2), produced by the turbulent

flow against the closed atrioventricular and semilunar valves respectively.

54

The period between S1 and S2 is referred to as systole and the period between

S2 and subsequent S1 is diastole. Murmurs are abnormal sounds that occur

between S1 and S2 called systolic murmurs and between S2 and S1 called

diastolic murmurs. The former occurs during ventricular contraction (systole)

and the latter occur during ventricular filling (diastole). These heart sounds

and murmurs are shown in Figure 4.1.

(a) Normal heart sounds S1 and S2

(b) Systolic murmurs between S1 and S2

(c) Diastolic murmurs between S2 and S1

Figure 4.1 Heart sounds and murmurs

55

The systolic murmurs are again sub classified into early, mid, late

and holo systolic murmurs depending on their position in the systolic period.

Similarly the diastolic murmurs are further sub classified into early, mid and

late diastolic murmurs. These pathological types of heart murmurs and their

associated abnormalities are shown in Table 4.1.

Table 4.1 Heart murmurs and associated abnormalities

Category Sub category Associated abnormality

Systolic

Early systolic

Mid systolic

Late systolic

Holo systolic

Mitral regurgitation

Aortic stenosis

Atrial septal defect

Mitral valve prolapse

Tricuspid valve prolapse

Papillary muscle dysfunction

Tricuspid insufficiency

Ventricular septal defect

Diastolic

Early diastolic

Mid diastolic

Late diastolic

Aortic regurgitation

Pulmonary regurgitation

Left anterior descending artery stenosis

Mitral stenosis

Tricuspid stenosis

Atrial myxoma

Complete heart block

4.3.2 Characteristics

Murmurs can be classified based on seven different characteristics :

timing, shape, location, radiation, intensity, pitch and quality. Timing refers to

whether the murmur is a systolic or diastolic murmur. Shape refers to the

intensity over time; murmurs can be crescendo, decrescendo or crescendo-

decrescendo. Location refers to where the heart murmur is auscultated best.

There are six places on the anterior chest to listen for heart murmurs; each of

these locations roughly corresponds to a specific part of the heart. Radiation

56

refers to where the sound of the murmur radiates. The general rule of thumb is

that the sound radiates in the direction of the blood flow. Intensity refers to

the loudness of the murmur, and is graded on a scale from 0-6/6. The pitch of

the murmur is low, medium or high and is determined by whether it can be

auscultated best with the bell or diaphragm of a stethoscope. The quality of a

murmur may be blowing, harsh, rumbling and musical. In the proposed work,

classification is done with respect to the timing characteristic.

4.4 HEART MURMUR CLASSIFICATION

4.4.1 Data Collection and Pre-Processing

Heart murmur files, normal and pathological, collected from the

Johns Hopkins Cardiac Auscultatory Recording Database (CARD), an

excellent resource for research purposes, and other internet sites, were used in

this classification work (CARD 2006; Raymond 2006). The pathological

types include early systolic, mid systolic, late systolic, holo systolic, early

diastolic, mid diastolic and late diastolic murmurs. The murmur files obtained

in mp3 format were converted to wav format for further processing. The

sampling frequency chosen was 8000Hz in all cases to cover the range for

analysing murmurs (Zhou et al 2008). Signals, when contaminated with noise,

might lead to erroneous classification. To remove the noise that may overlap

with the heart sounds, SureShrink, the Discrete Wavelet Transform (DWT)

based wavelet shrinkage denoising technique was used (Donoho and

Johnstone 1995; Cai and Harrington 1998).

4.4.2 Feature Extraction

The main objective of feature extraction process is to derive a set of

features that best represents the signal. So, the selection of features is an

important criterion for proper classification of different heart murmurs. In this

work, a set of 15 statistical features were extracted from the murmur files. The

steps involved in the feature extraction process are illustrated in Figure 4.2.

57

Figure 4.2 Feature extraction process

Digitized heart sound

sampled at 8000 Hz

16 bits/sample

Normalized to absolute

maximum

Extraction of single cycle

of the heart murmur

Wavelet decomposition

(db6 level 5)

3rd

level

detail

500-1000

Hz

4th level

detail

250-500

Hz

5th level

detail

125-250

Hz

5th level

approximation

0-125

Hz

Determining mean of

absolute values of

coefficients in each sub band

Determining standard

deviation of wavelet

coefficients in each sub band

Determining average power

of wavelet coefficients in

each sub band

Determining ratio of

absolute mean values of

adjacent sub bands

58

The denoised signal was segmented into cardiac cycles. A single

cycle containing first heart sound S1, systolic period, second heart sound S2

and diastolic period, was extracted for analysis. To nullify the effect of input

gain variations, the original signal was normalized to absolute maximum.

Heart murmurs are non-stationary signals exhibiting marked changes with

time and frequency (Debbal and Bereksi-Reguig 2008). Hence, Wavelet

Transform, a proven tool for time-frequency analysis, was used.

The most important aspect of feature extraction is the selection of a

suitable wavelet and the number of levels of decomposition. Usually, the

signal is visually inspected first and if they are kind of continuous, Haar or

other sharp wavelet functions are used; otherwise a smoother wavelet can be

employed. Another method is to perform tests with different types of wavelets

and the one which gives maximum efficiency can be selected for the

particular application. The number of levels decomposition is chosen based

on the dominant frequency components of the signal. The levels are chosen

such that those parts of the signal that correlate well with the frequencies

required for classification of the signal are retained in the wavelet

coefficients. Since the frequency range of heart murmurs is 0 to 1000Hz

(Zhou 2008), the number of levels was chosen to be 5. The normalized signal

was decomposed into five subbands using Daubechies db6 wavelet

decomposition. The DWT decomposition of the input signal x[n] is

schematically shown in Figure 4.3.

Figure 4.3 Level-5 wavelet decomposition to obtain wavelet coefficients

2 h[n]

g[n]

2

x[n]

A2

D2

A

1

D1

2

g[n] 2

h[n] 2

h[n]

g[n]

2

A5

D5

Level 5

Details

…..

.

Level 2

Details

Level 1

Details

Level 5 Approximations

59

The decomposition of the signal into different frequency bands is

simply obtained by successive highpass and lowpass filtering of the signal

(Kandaswamy et al 2003; Sadik and Mustafa 2007). The decomposed

subbands with their corresponding ranges of frequency are shown in

Table 4.2. The resulting wavelet coefficients provide a compact

representation of the energy distribution of the signal in each subband. As the

frequency spectrum of heart murmurs range from 0 to 1000 Hz and the values

of the observed wavelet coefficients in D1 and D2 were also very close to

zero, the higher subbands D1 and D2 were discarded from further processing.

Table 4.2 Frequency range of sub bands

Decomposed sub band Frequency Range(Hz)

D1 2000-4000

D2 1000-2000

D3 500-1000

D4 250-500

D5 125-250

A5 0-125

The time domain representation of the subbands for a normal heart

sound file is shown in Figure 4.4.

From the wavelet coefficients in each subband (D3, D4, D5 and

A5), a set of 15 statistical parameters were derived in order to represent the

time-frequency characteristics of the heart murmurs. These are shown in

Table 4.3.

60

Figure 4.4 Decomposition of normal heart sound with db6 wavelet

Table 4.3 Features derived for classification of heart murmurs

Sl

No Description of feature

No.of

features

1 Mean of the absolute values of the coefficients in each sub band 4

2 Standard deviation of the wavelet coefficients in each sub band 4

3 Average power of the wavelet coefficients in each sub band 4

4 Ratio of the absolute mean values of adjacent sub bands 3

Total 15

In order to automate the whole feature extraction process, Matlab

was linked with Microsoft Excel. The features were then exported to the

worksheet directly. These 15 parameters along with the corresponding output

(type of murmur) forms the input feature vector for classification (Criley et al

2000; January and Zahra 2007). The dataset used in this work has 441 feature

vectors or instances.

61

4.5 CLASSIFICATION USING ARTIFICIAL NEURAL

NETWORKS

4.5.1 Neural Network Classifier

Artificial Neural Networks (ANN) are computational models that

are patterned after the structure of the human brain. They have the ability to

‘learn’ mathematical relationships between a series of input (independent)

variables and the corresponding output (dependent) variables. This is

achieved by training the network with a training dataset consisting of input

variables and the known or associated outcomes. Networks are programmed

to adjust their internal weights based on the mathematical relationships

identified between the inputs and outputs in a dataset. The knowledge gained

by the learning experience through training is stored in the form of connection

weights, which are used to make decisions on test inputs. Once a network has

been trained, it can be used for classification tasks with a separate test data set

for validation (Rajendra et al 2003; Jack 1996).

Figure 4.5 Feedforward neural network to classify heart murmurs

I1

I2

H1

H2

Hn

O1

O8

X1

X2

Inputs

Outputs

Input Layer Hidden Layer Output Layer

Y8

Y1

I15

X15

62

Among the several existing neural network architectures, the

feedforward neural network trained using supervised learning with

backpropagation algorithm has been chosen for this work. This model, which

is considered the most useful learning model, is being widely used in the

biomedical field (William 1995; Maurice and Donna 2006). The feedforward

neural network used in this classification work is shown in Figure 4.5. The

input layer has 15 nodes representing the 15 input features and the output

layer has 8 nodes representing normal and seven different classes of murmurs.

4.5.2 Encoding of Data for ANN

The classification scheme of 1-of-C coding has been used for

classifying the heart murmurs into one of the eight output types. For each type

of heart murmur, a corresponding output class is associated. The feature

vector set, x represents the ANN inputs and the corresponding class once

coded, constitutes the ANN outputs. In order to make the neural network

training more efficient, the input feature vectors were normalized so that all

the values fall in the range between 0 and 1 (Jack 1996; Richard and Michael

2005). The encoding of output is as shown in Table 4.4.

Table 4.4 Encoding of output for classification of heart murmurs

Sl No. Output vector Classification

1 1 0 0 0 0 0 0 0 Normal

2 0 1 0 0 0 0 0 0 Early systolic

3 0 0 1 0 0 0 0 0 Mid systolic

4 0 0 0 1 0 0 0 0 Late systolic

5 0 0 0 0 1 0 0 0 Holo systolic

6 0 0 0 0 0 1 0 0 Early diastolic

7 0 0 0 0 0 0 1 0 Mid diastolic.

8 0 0 0 0 0 0 0 1 Late diastolic

63

The value corresponding to the correct class of output is entered as

1 and other values are entered as zero. The modified input vector is xk, with k

= 1, 2,…..K where K is the number of heart murmur signals. The output

vector associated with xk is denoted by yk.

4.5.3 Training with Backpropagation Learning

As inputs and desired outputs are known, the training is done in a

supervised fashion. In the basic back propagation (BP) training algorithm, the

weights are moved in the direction of the negative gradient. The BP learning

updates the network weights and biases in the direction in which the

performance function decreases most rapidly – the negative of the gradient. A

single iteration of this algorithm can be written as

wk+1 = wk – αk gk (4.1)

where wk is a vector of current weights and biases, gk is the current gradient,

and αk is the learning rate. Convergence is sometimes faster if a momentum

term is added to the weight update formula. When using momentum, the net

is proceeding not in the direction of the gradient, but in the direction of a

combination of the current gradient and the previous direction of weight

correction. In backpropagation learning with momentum, the weights for

training step t+1 are based on the weights at training steps t and t-1. The newff

function in Matlab’s neural network toolbox was used for the generation of

feedforward backpropagation neural network architecture. This newff function

has ‘learngdm’ as the default learning function which chooses ‘initnw’

initialization function for initializing a layer’s weights and biases according to

the Nguyen-Widrow initialization algorithm. Learning occurs according to

learngdm’s learning parameters with their default values as 0.01 for learning

rate and 0.9 for momentum constant.

64

4.5.4 Activation Function

There are different types of activation functions. The most

commonly used are : (i) hyperbolic tangent sigmoid (ii) log sigmoid and (iii)

linear. The first criterion of an activation function is that the function must

output values in the interval range [0,1]. A second criterion is that the

function should output a value close to 1 when sufficiently excited. The

sigmoid function meets both the criteria. The performance of these three

activation functions were experimented in the neural network models

constructed.

4.5.5 Stopping Criteria

Network training continues until a specific terminating condition is

satisfied. The terminating condition chosen for the network convergence is

the minimum mean squared error (MSE) as defined below :

1 2MSE ( )

1

pd y

m mp m

(4.2)

where p = number of training instances

d = desired outputs

y = output obtained from the neural network

The target for mean squared error set in our work is 0.001. Training

stopped when the value of MSE dropped below 0.001.

4.6 EXPERIMENTAL RESULTS AND DISCUSSIONS

The training algorithm, activation function and the number of

neurons in the hidden layer that provide the best results were determined

65

empirically. For this purpose, several feedforward backpropagation neural

networks were constructed using various combinations of training algorithms,

activation functions and different number of neurons in the hidden layer. The

training algorithms used are Levenberg-Marquardt (LM), Gradient Descent

with Adaptive learning rate (GDA), Resilient Backpropagation (RP) and

Scaled Conjugate Gradient Descent (SCG). The activation functions used are

logsigmoid, tansigmoid and purelin. Each network was trained with 331

instances (75% of the dataset with uniform distribution of all classes) and

tested with 110 instances (25% of the dataset with uniform distribution of all

classes). The performance of these neural network models was measured

through accuracy, sensitivity and specificity. The results obtained are

presented in Table 4.5.

Accuracy, sensitivity and specificity were calculated using the

following formulae (Sadik and Mustafa 2007) :

TP TN

AccuracyTotal

(4.3)

TP

SensitivityTP FN

(4.4)

TN

SpecificityTN FP

(4.5)

where TP = True positive, positive cases classified as positive

TN = True negative, negative cases classified as negative

FN = False negative, positive cases classified as negative

FP = False positive, negative cases classified as positive

66

Table 4.5 Performance of different neural network models

Model

number

Training

Algorithm

Activation

Function

Number

of Epochs

Number of

neurons in

hidden layer

Accuracy

%

Sensitivity

%

Specificity

%

1 LM Logsig 8 21 91.4892 91.7584 91.4494

2 LM Logsig 10 26 93.6419 83.4166 95.1568

3 LM Logsig 6 31 97.9473 91.7584 98.8642

4 LM Logsig 10 35 95.9768 96.5837 91.9587

5 LM Purelin 6 21 85.0311 75.0750 86.5062

6 LM Purelin 5 26 85.0311 75.0750 86.5062

7 LM Purelin 4 31 85.0311 75.0750 86.5062

8 LM Purelin 5 35 86.1174 87.7788 75.0898

9 LM Tansig 19 21 97.9473 91.7584 98.8642

10 LM Tansig 15 26 99.0236 100.000 98.8642

11 LM Tansig 18 31 99.0236 91.7584 100.000

12 LM Tansig 16 35 99.0943 100.000 91.8272

13 GDA Logsig 1734 21 94.7183 75.0750 97.6284

14 GDA Logsig 1400 26 96.8710 83.4166 98.8642

15 GDA Logsig 1472 31 94.7183 91.7584 95.1568

16 GDA Logsig 1371 35 92.7117 95.3390 75.1152

17 GDA Purelin 2000 21 88.2602 75.0750 90.2136

18 GDA Purelin 2000 26 86.1075 75.0750 87.7420

19 GDA Purelin 2000 31 85.0311 75.0750 86.5062

20 GDA Purelin 2000 35 81.8838 81.5821 83.3919

21 GDA Tansig 2000 21 95.7946 75.0750 98.8642

22 GDA Tansig 2000 26 96.8710 91.7584 97.6284

23 GDA Tansig 2000 31 95.7946 91.7584 96.3926

24 GDA Tansig 2000 35 94.7916 95.3834 91.8117

25 RP Logsig 147 21 95.7946 83.4166 97.6284

26 RP Logsig 73 26 95.7946 91.7584 96.3926

27 RP Logsig 140 31 94.7183 91.7584 95.1568

28 RP Logsig 110 35 95.9117 98.9725 75.2631

29 RP Purelin 2000 21 85.0311 75.0750 86.5062

30 RP Purelin 2000 26 85.0311 75.0750 86.5062

31 RP Purelin 2000 31 85.0311 75.0750 86.5062

32 RP Purelin 2000 35 86.1025 87.8596 75.2463

33 RP Tansig 2000 21 99.0236 91.7584 100.000

34 RP Tansig 2000 26 99.0236 91.7584 100.000

35 RP Tansig 2000 31 99.0236 91.7584 100.000

36 RP Tansig 2000 35 95.9419 96.5213 91.8808

37 SCG Logsig 141 21 97.9473 91.7584 98.8642

38 SCG Logsig 113 26 97.9473 91.7584 98.8642

39 SCG Logsig 143 31 88.2602 91.7584 87.7420

40 SCG Logsig 116 35 96.9502 97.6854 91.8179

41 SCG Purelin 713 21 85.0311 75.0750 86.5062

42 SCG Purelin 756 26 85.0311 75.0750 86.5062

43 SCG Purelin 827 31 85.0311 75.0750 86.5062

44 SCG Purelin 538 35 86.1234 87.7144 75.0984

45 SCG Tansig 931 21 97.9473 83.4166 100.000

46 SCG Tansig 773 26 96.8710 83.4166 98.8642

47 SCG Tansig 633 31 99.0236 91.7584 100.000

48 SCG Tansig 659 35 96.9289 98.9593 83.3982

67

The network models that performed best with each of the four

training algorithms were compared. The comparison results are presented in

Table 4.6 and illustrated in Figure 4.6.

Table 4.6 Performance comparison of training algorithms

Training

algorithms

Activation

function

No. of

neurons in

hidden

layer

Number

of

Epochs

Accuracy

%

Sensitivity

%

Specificity

%

LM Tansig 26 15 99.02 100.00 98.86

SCG Tansig 31 633 99.02 91.76 100.00

GDA Tansig 26 2000 96.87 91.76 97.63

RP Tansig 21 2000 99.02 91.76 100.00

86

88

90

92

94

96

98

100

102

LM SCG GDA RP

Per

cen

tage

Training Algorithms

Accuracy

Sensitivity

Specificity

Figure 4.6 Performance comparison of training algorithms

It was observed that the neural network model with LM training

algorithm and 26 neurons in the hidden layer showed better performance

when compared to the models with other training algorithms. It was also

noted that tansigmoid activation function outperforms the other two activation

functions. Also, given the speed of convergence of the network during

68

training is indicated by the number of epochs, the networks trained with LM

algorithm were observed to converge faster than those with SCG, GDA and

RP. Hence, the neural network model (Sl. No.10 in Table 4.5) with 26

neurons in the hidden layer, trained with Levenberg-Marquardt (LM)

algorithm and tansigmoid activation function for both the hidden and output

layer was found to yield the best results for the dataset used. The performance

of the proposed neural network classifier is comparable with other classifiers

cited in literature and the comparison is presented in Table 4.7.

Table 4.7 Performance comparison of different classifiers

Reference Classifier

No of

types of

murmurs

classified

Average

Classification

Accuracy (%)

Faizan et al (2006) MLP-ANN 7 86.40

Zhou et al (2008) FBNN 6 95.83

Yadi et al (2008) MLP-ANN 5 92.00

Proposed MLP-ANN 8 99.02

4.7 SUMMARY

In this chapter, the development of a heart murmur classifier was

presented. Discrete Wavelet Transform was used to extract a set of 15

features from the heart murmur signals. Feedforward neural network models

with backpropagation learning were developed with different combinations of

training algorithms and activation functions to classify the heart murmurs into

one of the eight types namely normal, early systolic, mid systolic, late

systolic, holo systolic, early diastolic, mid diastolic and late diastolic. The

neural network model with Levenberg-Marquardt training algorithm and

tansigmoid activation function was found to yield the best performance. The

results demonstrate the capability of the developed system as a support tool

for physicians in the diagnosis of heart murmurs.