10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)

Comparison of Different Mother Wavelets in PVC Detection Using PNN

Nur Asyiqin Amir Hamzah§ and RosH Besart §Center for Diploma Programme (CDP)

Multimedia University, 75450 lalan Ayer Keroh Lama, Melaka, Malaysia Tel: +606-252 3650, Fax: +606-231 3004

E-mail: asyiqin.hamzah@mmu.edu.my t Faculty of Engineering and Technology (FET)

Multimedia University, 75450 lalan Ayer Keroh Lama, Melaka, Malaysia Tel: +606-252 4022, Fax: +606-231 6552

E-mail: rosli@mmu.edu.my

Abstract - Recently, cardiac diagnosis becomes very important to determine cardiac health condition. Since electrocardiogram (ECG) plays important role in the diagnosis, many classification

methods are developed by means of analyzing ECG. The motivation of this study is to research on the optimal wavelet that would accurately classify ECG signal into two distinct classes; normal and Premature Ventricle Contraction (PVC) beats when using Probabilistic Neural Network (PNN).

About 35 mother wavelets are used to classify 400 R-to-R intervals of normal and PVC beats. The 400 R-to-R intervals are divided into two groups, Gl and G2. The purpose of this is to

inspect their consistency. Two features dataset are set up; one

with the ECG time information i.e. R-to-R time ratio as well as another two additional features; average power and energy. Meanwhile, the other dataset are without the time information, average power and energy.

Both datasets contain the statistical indices for eight wavelet coefficient (approximation and detail) of level seven. The datasets are then fed into PNN. Metric quantifications are computed to

examine the optimal mother wavelet. It is observed that "haar",

"db3" and "sym3" produce high accuracy, specificity and sensitivity at detail level 3.

Key words - ECG, DWT, PNN, PVC.

I. INTRODUCTION

Cardiovascular disease (CVD) remains the leading cause of death by a non-communicable disease (NCD) in Malaysia. Based on the National Cardiovascular Disease Database (NCVD) report for 2006, there were about 47.1 per population of incidence of acute cardiovascular syndrome admission [1]. Therefore, should necessary medical attention is given immediately, many lives are able to be saved.

This leads to the development of numerous algorithms and techniques in order to detect any cardiac problems. On the basis of these features classification are performed by template matching [2], Hidden-Markov model [3], Hermite functions [4], neural networks [5], [6], [7], [8] or by other recognition systems [9].

ECG analysis has proven to be the standard method in clinical settings for detection of dangerous cardiac conditions. It is measured at the body surface and results from electrical changes associated with activation first of the two small heart chambers; atria, and then of the two larger heart chambers; ventricles. ECG effectively presents the crucial clinical

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information related to the rate, morphology and regularity of the heart. Other obvious advantages of using ECG analysis are the low-cost and non-invasive test [10].

This research is limited to focus on the classification of an arrhythmia called PVC. PVC results from irritated ectopic foci in the ventricular part of the heart. These foci cause premature contractions in the ventricles that are independent of the pace set by the sinoatrial node. Studies indicated that the combination of PVC and myocardial infarction can cause sudden death.

In arrhythmia classification, certain aspects must be taken care. One of it is the features extraction. The techniques used to extract ECG features can be either temporal or transformed representation of the ECG waveforms. Temporal feature is feature related to time-information such as QRS complexes, amplitude and RR interval information while transformed representation can be obtained by using transforms method such as Fourier Transform (FT) or Wavelet Transform (WT).

There were several studies conducted to experiment the effect of both techniques. Using the temporal alone resulted [11] of 86.50% accuracy. An attempt to used Discrete Wavelet Transform for "dbl", "db4" and "dblO" at level seven in [12], resulted 74.2% accuracy.

Literature from previous works [10] highly recommended using the combination of waveform shape and timing interval features which are very essential to robust the classification. This is due to the fact that some ECG arrhythmias, particularly PVC in this case, is related with premature heart beats that has shorter RR intervals as to compare to other types of ECG signals. For this reason, work done in [10] which employed spline wavelet up to level five, able to boost the accuracy as high as 96.82%. Meanwhile, a promising accuracy of 97.53% obtained in [13] by utilizing "sym6" wavelet at level five. Also, in the same attempt, [16] managed to achieve as high as 99% recognition.

The mentioned works above selected the mother wavelet based on variety considerations i.e. shape similarity with the QRS complexes, has compact support, has one vanishing moment etc. Therefore, in this work we are to use many types of mother wavelet for comparison of which will give the highest discrimination ability. Also, we are concerned on the study of temporal information when combined with the power

and energy signal along with DWT coefficients on the classification accuracy.

II. MATERIALS AND METHODS

Fig. 1 shows a proposed block diagram for this study. ECG Signal Database- MIT-BIH Database: As the ECG is being the substantial component in this study, therefore ECG collection is a mandatory. The collection is made from the Massachusetts Institute of Technology - Beth Israel Hospital [17].

We use a total of 400 beats datal 00 for normal and data107, lOS, 114, 116, 119, 123 and 200 for PVC. We focus on modified-lead II signals. The beats are then divided into two groups; Gl and G2. The G 1 serves as the original dataset and G2 serves as control dataset for consistency verification purpose. Pre-Processing: In processing part, the raw ECG signal collected is first pre-processed to normalize to a mean of zero and standard deviation of unity. The removal is necessary to have zero and flat baseline as well as to reduce the amplitude variance among the ECG files [10].

Our previous study [14], showed that not every PVC beat has R peak (refer to Fig. 2). Therefore, it is more appropriate and practical to have ECG signal partitioned into individual R­to-R interval. The interval is chosen to be in between of R peak of the previous beat from the processing beat and the R peak of the next beat from the processing beat (refer Fig. 3). Another important discovery was that the R-to-R interval for PVC beat was essentially longer than normal beat. Thus, we took these two characteristics as our advantage for study. Average Power, Energy and Peak-to-Peak Ratio

Computation: Average power of a signal is defined as:

Pavg = fSJf )df

Where, S x (j) = Power Spectral Density (PSD)

Energy of each R-to-R interval is also computed. Mathematically, the energy of a signal is dermed as:

N-J

E= IIxnl2 n""O Since we choose to analyze R-to-R interval which based on

the reason that the R-to-R interval for PVC beat is essentially longer than normal beat, consequently we also take into account the R-to-R interval in between of previous beat and the next beat.

R-to-R interval ratio is calculated as the ratio of the R-to-R interval between the processing beat and the previous beat, RRO with the R-to-R interval between the next beat to the previous beat, RRI. Mathematically, it can be expressed as:

R RR · RRO

atlo= --RRI

Peak-to-peak ratio is chosen because it provides a convenient differentiator between normal beats

( R _ RRatio > 0.50 ) and PVC betas ( R _ RRatio < 0.45 ). As

not all PVC beats have R peak, therefore the RRO for PVC beat which has no R peak is chosen to be the inverted R wave.

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Discrete Wavelet Transform (DWT): DWT is a sampled version of Continuous Wavelet Transform (CWT) as CWT provides considerably high redundancy. DWT on the other hand provides sufficient information to perform the analysis [14].

Mathematically, CWT is defined as,

CWT;(1:, s )= � fx(tfP'C � 1:}t

Where, x(t )= signal to be analyzed, 'P(t )= mother wavelet,

where f'P (t):it = 0 , qs ( t ) E L 2 , s=scale, t = time, and 1: =

translation. The wavelet transform has been demonstrated as a powerful tool to successfully isolating relevant properties of the waveform morphology from the other unwanted signals such as noise, baseline drift, and amplitude variance of the original ECG signal [10].

This part is conducted to compute the coefficient of DWT for each of R-to-R interval. A total of 35 wavelets are used for comparison; Haar, Daubechies (db2-db9), Biorthogonal (bior1.3-bior6.S), Coiflet (coifl-coif5) and Symlet (sym2-symS). The level of the DWT is seven with eight coefficients; A7, D7, D6, D5, ....... , Dl where A is approximation, D is detail and 7,6,5, ... ,1 are the level number.

From these subband coefficients, the statistical indices are determined to become Feature Dataset! as well as a part of Feature Dataset2. The indices are including average, standard deviation, variance (averaged AC power), skewness, kurtosis, variance of the autocorrelation (averaged AC power of the autocorrelation function), and relative amplitude. Feature Dataset 1 & 2: For Feature Dataset 1, the features are selected to be only seven; average, standard deviation, variance, skewness, kurtosis, variance of autocorrelation, and relative amplitude.

In contrast to Feature Dataset 1, in Feature Dataset 2, the features are selected to be ten. This include all the seven features from Feature Dataset 1 and three added features; average signal power, energy signal, and peak-to-peak ratio. The two feature sets is set up in order to see the effect of the morphology features alone and when it combined with the timing information, average power and energy on the classification accuracy.

Finally, both of the dataset are normalized to [-1,+ 1] range. This is important because the quantities of the features may be varied to each other. Thus, a normalization process is essential to standardize all the features to the same level [16]. Probabilistic Neural Network (PNN): PNN is a special type of radial basis-function networks [16]. It has three layers; an input layer, a hidden layer, and an output layer.

The input layer is merely a distribution layer and no computation is done at this layer. The second layer is the hidden layer is also known as the pattern layer. Its neurons utilize multi-dimensional kernels to estimate the probability density function for classification. One of the well-known kernels is Gaussian function since it guarantees the convergence of the neural network [16]. The output layer is a competition layer. The number of neurons in the competition

layer is the same as that of the desired classes, which in this case is two; Normal and PVC beats.

Since neural network has to be trained and tested, therefore, there is a need to prepare the dataset for each. Consequently, since we have 100 beats from normal and 100 beats for PVC both for each of Gl and G2, we distribute their Feature Dataset 1 and 2 into five groups for cross validation. Each of the cross validation group has 80 samples of training set and 20 samples of testing set. Performance Metric (Feature Dataset 1 & 2): Highest

Specificity, Sensitivity, and Accuracy: We quantified our classifier performance using the most common metrics found in literature: accuracy, sensitivity and specificity. The first expressed the overall system performance over all types of beats while the last two quantities are specific to each class of beat.

The most vital metric for determining overall system performance is usually accuracy. We expressed the overall accuracy of the classifier for each file as follows:

A = (1- :: Jxl00

Where, N e = total number of error classification, and

N b = total number of beats. Another two metrics of classifier

performance used in this work are sensitivity and specificity. Sensitivity is defined as probability that a symptom is present (or a test predicted that the person had the disease) given that the person had the disease. Mathematic expression for sensitivity was given by,

S . . . TP

enSltIvlty = --­TP+FN

Meanwhile, the specificity is defined as the probability that a condition is not present (or a test predicted that the person did not have the disease) given that the person did not have the disease. This can be mathematically expressed as:

S ·fi · TP

peci IClty = ---TP+FP

In conclusion, sensitivity measured how successfully a classifier recognized beats of a certain class without missing them whereas positive specificity measured how exclusively it classified beats of a certain type [10].

III. RESULT AND DISCUSSION

Referring to Table 1, results obtained are supported by [18] which concluded that much more of the QRS complex energy is confined in D3 and D4 as to compare to the other details. Addition to that, the amount of data in D3 is twice larger than D4 and is acmally a large portion of the total signal data. However, they ignored the data in D3 as it is too large and only consider D4. Since we attain a good average accuracy with small deviation for D3, therefore we still take it into our consideration.

On the other hand, the P and T waves' energy primarily appears in D6 and D7 [18]. Nevertheless, since this smdy do not emphasis on the P and T waves as the feature dataset,

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therefore, the D6 and D7 do not appear to be as the best performed wavelet level.

Addition to that, D 1 and D2 are also not appear to be the best coefficient level as the frequencies covered by these levels were higher than frequency content of the ECG [9].

The result obtained for Feature Dataset 2, are also supported by [9] and [18]. From FeatIIre Dataset 1 and 2, there are three common wavelets; "haar", "db3", and"sym3" at D3. However, the results obtained by the FeatIIre Dataset 1 are significantly higher than FeatIIre Dataset 2.

This result proves that combining the wavelet transform coefficients, timing interval information, average signal power and signal energy do not help to boost the accuracy. In contrast to result obtain in [10] and [13], using the wavelet transform coefficients coupled with timing interval information, yield a promising accuracy. In other words, we can conclude that the average signal power and signal energy are not the appropriate featIIres in classifying the beats. We examine the average power and energy values for normal and PVC beats. The values are almost identical and this would not give much discrimination. Hence, results a considerably low accuracy.

IV. CONCLUSION

We have made comparison smdy for 35 wavelets using PNN to observe the optimal mother wavelet for two featIIre datasets in PVC detection. We found that there are three common wavelets; "haar", "db3", and"sym3" at D3 that able to produce high accuracy for both datasets. However, the results obtained by the Feature Dataset 1 are higher than FeatIIre Dataset 2. This shows that the average signal power and energy signal are not the appropriate features in detecting PVC beats because their values are significantly identical to the normal beats values. This also suggests that the wavelet coefficients should be only combined with peak-to-peak ratio in order to obtain high accuracy as in [10] and [13].

This study also proves that the mother wavelet selection may not be crucial for this purpose as high accuracy achieved in [13] and [16] are not using the optimal mother wavelet which obtained in this study. Also, the feature dataset is greatly influenced the recognition. Addition to that, R-to-R interval information plays important role along with other wavelet coefficient features.

T ABLE I S UMM ARY OF COMMON MOTHER WAVELETS FOR FEATURE DATASET I AND 2 AND ITS PER FORMANCE

METRIC S V AL UE(FDl�FEATUREDATASET I ANDFD2�FEATUREDATASET2)

WT % OF AC CURACY % OF SPECIFICITY % OF SENSITIVITY

FDI FD2 FDI FD2 FDI FD2

haar Gl: 89.0 Gl: 84.5 GI: 91.9 Gl: 83.3 Gl: 86.0 Gl: 88.0 G2: 97.5 G2: 96.0 G2: 100.0 G2: 99.0 G2: 95.0 G2: 93.0

db3 Gl: 85.0 Gl: 80.0 Gl: 83.5 Gl: 80.0 Gl: 89.0 Gl: 89.0 G2: 90.5 G2: 90.5 G2: 90.4 G2: 87.3 G2: 91.0 G2: 96.0

sym3 Gl: 85.0 Gl: 82.0 Gl: 83.5 Gl: 80.0 Gl: 89.0 Gl: 89.0 G2: 90.5 G2: 90.5 G2: 90A G2: 87.3 G2: 91.0 G2: 96.0

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[18] YoungKyoo Jung and Tompkins W.J. (2003). Detecting and classifying life-threatening ECG ventricular arrythmias using wavelet decomposition. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, vol. 3, pp. 2390 - 2393.

Average Power, Energy and Peak-Ie-Peak Ratio

Computation

(a) High R POlk

1.5

0.5

-0.5 L-__ ....LL __ --' __ ---' 15 15.5 16

Time �) 16.5

;;-Ji.

j f '"

;;-Ji.

u � .B l '"

(b) Inverted R POlk 1.5

0.5

-0.5 -1

94.5 95 Time �)

(c) Inverted ST Segment

1.5

0.5

-0.5 ·1 482.5 483 483.5 484

Time �) Fig. 2: Various of PVC beat shapes

R-to-R Irderval for Nonna} Beat

1.2

0.8

i 0.6

] 0.4 " � 0.2 '"

0

-0.2

-0.4

--R J=eak before the

processing beat

Processing beat

(normel) -­

Rpeak after the processing beat

95.5

484.5

-°:i%G'-.6=--=3-=-56'- . 8::--",35'=7--:3",57'--.2:--:3",57'-- .4--:3",5 "- 7 . .,.-6--,3=-=5:':-7 . .,.-8--:3-'::58:--3::-:58:':-.""2-3::-:58:"- .-4--::-::'358.6

Tim.(,) Fig. 3: A sample of R-to-R interval for normal beat

Performance Metric Highest Specificity, Sensitivity,

Accuracy

Performance Metric Highest Specificity, Sensitivity,

Accuracy

Comparison

Comparison

Fig. 1. Proposed block diagram to determine the optimal mother wavelet

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