V.KARTHIKEYAN PUBLISHED ARTICLE A.A

International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013) 11

www.arpublication.org

Classification of Cardiac Arrhythmias Using

Heart Rate Variability Signal

V.Karthikeyan1, V.J.Vijayalakshmi

2, P.Jeyakumar

3

1Department of ECE, SVS College of Engineering, Coimbatore, India

[email protected] 2Department of EEE, Sri Krishna College of Engg & Tech., Coimbatore, India

[email protected] 3 Department of ECE Karpagam University, Coimbatore, India

[email protected]

Abstract

The project aims at the determination of an effective arrhythmia classification algorithm

using the Heart Rate Variability (HRV) signal. HRV signal is nothing but the RR interval

in an ECG signal. The method is based on the Generalized Discriminant Analysis (GDA)

feature reduction technique and the Multi Layer Perceptron (MLP) neural network

classifier. At first, nine linear and nonlinear features are extracted from the HRV signals

and then these features are reduced to only three by GDA. Finally, the MLP neural

network is used to classify the HRV signals. The proposed Arrhythmia classification

method is applied to input HRV signals, obtained from the MIT-BIH databases. Here,

four types of the most life threatening cardiac arrhythmias including left bundle branch

block, fist degree heart block, Supraventricular tachyarrhythmia and ventricular

trigeminy can be discriminated by MLP and reduced features with the accuracy of 100%.

Keywords: Heart Rate Variability (HRV), Generalized Discriminant Analysis (GDA),

Multi Layer Perceptron (MLP), Arrhythmia.

1. INTRODUCTION:

The last two decades have witnessed the recognition of a significant relationship between the

autonomic nervous system and cardiovascular mortality, including sudden cardiac death. HRV is

a nonlinear and non stationary signal that represents the autonomic activity and its influence on

the cardiovascular system. Hence, measurement of heart rate variations and computerized

analysis of it is a non-invasive tool for assessing the autonomic nervous system and

cardiovascular autonomic regulation. Furthermore, it could give us information about heart

deficiency at the present or in the future. Time and frequency domain measures in heart rate

variability analysis are less successful in the classification of multiple rhythm changes. With the

help of measures from non-linear dynamics we can quantify some of the complex structures in

heart rate time series. Therefore, we have used a combination of linear and non-linear parameters.

These features are used as input in an artificial neural network (ANN), which classifies each

segment into one of the arrhythmia classes. Hence, HRV analysis can be considered as an

important diagnostic tool in cardiology. Some examples of automatic arrhythmia detection and

classification methods which have been analyzed ECG signal in the literature are neural networks

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[1-4], wavelet transforms [5], support vector machines [6, 7], fuzzy logic [8] and the rule-based

algorithms [9]. On the other hand, since the RR time intervals are less affected by the noise,

analysis of HRV signal is a more robust method. Different HRV signal analysis methods for

arrhythmia classification were introduced in the past. Tsipouras and Fotiadis [10] proposed an

algorithm based on both time and time-frequency analysis of the HRV signal using neural

networks. Acharya et al. [11] could classify the input HRV segments into one of the four different

arrhythmia classes using multilayer perception (MLP) together with a fuzzy classifier. A

knowledge-based method for arrhythmia classification into four different categories was proposed

by Tsipouras et al. [9]. In this paper, an effective HRV-based arrhythmia classification method is

presented. Initially, nine features are extracted from HRV segments. In order to reduce the

learning time and also to improve the efficiency of the classifier, three reduced feature vectors

must be subsequently extracted from nine original ones using the GDA technique. Thereafter, the

MLP-based classifier method is to be used to classify the arrhythmias. The method is to be

analyzed for accuracy with various other arrhythmia conditions. The analysis of the heart rate

variability (HRV) and its relation to the other physiological signals is measured

2. HEART RATE VARIABILITY:

Nowadays the cardiac arrhythmias are the most famous causes of mortality. Hence, several

techniques have been proposed to identify and detect the different types of arrhythmia. These

techniques usually extract desired features from Electrocardiogram (ECG) or Heart Rate

Variability (HRV) arrhythmic signals to classify them. Since ECG signal processing is time

consuming and too sensitive to the amount of the noise, nowadays, analysis of HRV signal to

assess the heart activity and to discriminate different cardiac abnormalities is an alternative

approach. HRV signal, which is generated from ECG by calculating the inter-beat intervals, is a

non-stationary signal that represents the autonomic activity of the nervous system and the way it

influences the cardiovascular system. The RR time intervals are less affected by the noise,

analysis of HRV signal is a more robust method. Different HRV signal analysis methods for

arrhythmia classification were introduced in the past. This project presents an HRV-based

arrhythmia classification method which is able to effectively identify four different and more

frequently occurring types of cardiac arrhythmia. These arrhythmias are namely the left bundle

branch block (LBBB), the first degree heart block (BI), the Supraventricular tachyarrhythmia

(SVTA) and the ventricular trigeminy (T). This algorithm is based on the generalized

discriminant analysis (GDA) and MLP classifier. Dimensionality of the input feature space is

reduced by GDA. The reduced features are used in classifying the abnormal signals through

neural network. The signal processing steps are performed in LABVIEW.

3. METHODOLOGY:

The analysis of the heart rate variability (HRV) and its relation to the other physiological

signals can be measured. Methods for quantifying HRV are categorized as: time domain, spectral

or frequency domain, geometric and nonlinear.

3.1 Extracting and Preprocessing the Signal:

The HRV data used in this project is generated from the ECG signals provided by the MIT-

BIH database. At first, it is necessary to extract the HRV signals from the ECG signals.

Generally, many interfering signals such as the mains 50 Hz, the electromyogram (EMG) signals

and also the baseline wandering can affect the extraction process. Hence, these interfering signals



are removed from the input ECG signal using a 5-15 Hz band pass filter. Then the signal is

processed using thresholding and the resultant signal is identified as HRV signal.

3.2 Feature Extraction:

The next step in the block diagram is the feature extraction. Generally, the cardiovascular system

demonstrates both linear and nonlinear behavior. Therefore, in this work we consider a

combination of linear and nonlinear features.

3.3. Linear analysis:

3.4. Time domain features

Four commonly used time domain parameters of the HRV signal which are directly extracted

from the RR interval time series are:

• Mean HR: The mean value of the heart rate within one minute in each segment.

Instantaneous heart rate (beat per minute) is equal to 60 divided by each R-R interval

(second).

• STD HR: The standard deviation of Instantaneous heart rate in each segment.

• pNN50: The number of successive difference of 64 R-R intervals that differs

more than 50 ms, respectively, divided by 64.

• HRV triangular index: This refers to the integral of the histogram (i.e. Total

number of RR intervals) divided by the height of the histogram. A bin width of

1/128 is selected.

3.5 Frequency domain features

Although the time domain parameters are effective, they do not have the ability of discrimination

between the sympathetic and parasympathetic contents of the HRV signal. High-frequency (HF)

band (0.2-0.5 Hz) of HRV signal shows the cardiac vagal activities such as Respiratory Sinus

Arrhythmia (RSA). In fact, HF components are considered as the origin of parasympathetic

activities of the cardiovascular system. On the other hand, the low-frequency (LF) band (0-0.2

Hz) is related to the baroreceptor control and is mediated by sympathetic systems. In this project,

the power spectral density (PSD) for the HF and LF bands are calculated and the ratio of the LF

and HF bands power (LF/HF) is considered as the Frequency domain feature of the HRV signal.

3.6 Nonlinear analysis

The HRV signal analysis by use of methods on nonlinear dynamics leads to very valuable

information for physiological interpretation of the heart. Hence, four different nonlinear

parameters of the HRV signal are used in this work.

• SD1/SD2: Poincare plot is a graphical representation of the correlation between

successive RR intervals. This is obtained plotting each RR interval (RR (n+1)) as a

function of the previous interval (RR (n)) in RR interval time series. This plot is

quantitively analyzed calculating the standard deviation of the distances of the time

series points from the lines y = x and y = x + 2RRm, in which RRm is the mean of all

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values of RR interval time series. These values are named SD1 and SD2 respectively.

In fact, SD1 represents the fast beat-to-beat variability, while SD2 describes the

relatively long-term variability in the HRV signal. In this work, SD1/SD2 is used as

the first nonlinear feature which is extracted from HRV segments.

• LLE: The Largest Lyapunov Exponent provides useful information about the

dependency of system on initial conditions and a positive Lyapunov exponent

confirms the existence of chaos in the system. For calculating LLE, a point is

selected in the reconstructed phase space of the system and all neighbor points

residing within a predefined radius e are determined. As the system evolves, the

mean distances between the trajectory of the initial point and the trajectories of the

neighbor points are calculated. Then the logarithm of these mean values plots against

the time and the slope of the resulting line are considered as LLE. The embedding

dimension and the lag are selected to be m = 10 and t = 1, respectively. The threshold

distance e is selected to be mSD, where SD is the standard deviation of the RR time

series.

• SpEn: The Spectral Entropy shows the complexity of the input time series (HRV

segment) in the frequency domain. Large values of SpEn show high irregularity and

smaller values of it indicate more regular time series. The Shannon’s channel entropy

is used to estimate the spectral entropy of the process as:

(1)

• D2: The Correlation Dimension is a measure of complexity of the time series and

determines the minimum number of dynamic variables which can model the system.

3.7 Feature Dimension Reduction:

Having above-defined features, due to the large variations in the HRV patterns of various

arrhythmias, there is usually a considerable overlap between some of these classes of arrhythmia

in the feature space.



Fig. 1 Block diagram of the project.

GDA is a feature transformation mechanism that can minimize the within-class scatter

and maximize the between-class scatter. Box -plots and the feature space plots of the

new three features for arrhythmia classes respectively, which are generated by GDA. As

seen, the samples relating to the different arrhythmia classes are located adjacent to each

other and relatively well separated from the other classes within the feature space.

Therefore, the new feature set not only reduces the number of the input features but also

increases the classification accuracy by selecting most discriminating features for a better

discrimination of the different arrhythmia classes.

3.4 Classification:

In this project, the MLP neural network is used to classify the HRV segments. A

three-layer feed-forward network, with sigmoid hidden and output neurons was

developed to classify input vectors. This optimal network has 15 inputs (5 inputs for the

case of the reduced feature vectors), one hidden layer with 20 neurons and 4 outputs for

the four arrhythmia classes each with a real value in the interval [0, 1]. The MLP is

trained using the training data set and employing the Levenberg–Marquardt learning rule

ECG

SIGNAL

HRV

SIGNAL

CLASS-INDEX

NOISE REMOVAL

RR INTERVAL TIME

SERIES

LINEAR

FEATURES

NON-LINEAR

FEATURES

DIMENSION REDUCTION BY

GDA

NEURAL

NETWORK

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backpropogation strategy. For each input feature vector, the output with the largest value

demonstrates the appropriate class that the input vector belongs to.

4. LEVENBERG–MARQUARDT LEARNING RULE

PROPAGATION

Learning occur in the insight by altering association weig

is processed, based on the quantity of fault in the output compared to the predictable effect. This

is an instance of managed learning, and is approved out from side to side

simplification of the smallest amount square algorithm in the linear awareness.

We symbolized the fault in output node

, where is the aim value and

to the weights of the nodes based on

output, given by

Using slope fall, we find our transform in every weight to be

Where is the output of the preceding neuron and

cautiously chosen to make sure that the weights meet to a reply quick adequate, with no

producing oscillations. In indoctrination applications, this stricture typically ranges from

0.2 to 0.8

The imitative to be intended depends on the induced local field

is simple to show that for an output knob this imitative can be simplified to

. For each input feature vector, the output with the largest value

demonstrates the appropriate class that the input vector belongs to.

MARQUARDT LEARNING RULE WITH BACK

Learning occur in the insight by altering association weights following every part of information


managed learning, and is approved out from side to side back propagation, a

smallest amount square algorithm in the linear awareness.

We symbolized the fault in output node in the th data point by

is the value produced by the insight. We then make corrections

to the weights of the nodes based on those corrections which diminish the mistake in the whole

slope fall, we find our transform in every weight to be

is the output of the preceding neuron and is the learning rate


ns. In indoctrination applications, this stricture typically ranges from

The imitative to be intended depends on the induced local field , which itself varies. It

is simple to show that for an output knob this imitative can be simplified to

. For each input feature vector, the output with the largest value

hts following every part of information


back propagation, a

is the value produced by the insight. We then make corrections

those corrections which diminish the mistake in the whole

(2)

(3)

learning rate, which is


ns. In indoctrination applications, this stricture typically ranges from

, which itself varies. It

(4)

International journal of Digital Signal and Image Processing (IJDSIP)

Where is the derivative of the commencement purpose described above, which itself does not

vary. The study is harder for the vary in weights to a concealed node, but it can be shown that the

relevant derivative is

This depends on the transform in weights of the

vary the hidden layer weights, we have to first alt

derivative of the activation function, and so this algorithm represents a

activation function.

5. SIMULATION RESULTS:

The database is taken from Physiobank and the project is done with v

signals. The signal is processed in Lab VIEW. The signal is noise removed and the results are

obtained. R peaks are obtained for the ECG signal and thus the HRV signal is extracted for

further processing. Figure 2 is the Input HRV signal

shows the Noise removed from the input signal.

after removing the noise from the HRV signal.

signal. The output of the proposed work is done by the Lab View/Simulink tool and the output is

obtained.

International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013)


is the derivative of the commencement purpose described above, which itself does not

arder for the vary in weights to a concealed node, but it can be shown that the

This depends on the transform in weights of the th nodes, which signify the output layer. So to

vary the hidden layer weights, we have to first alter the output layer weights according to the

derivative of the activation function, and so this algorithm represents a back propagation of the

RESULTS:

The database is taken from Physiobank and the project is done with various arrhythmia



Figure 2 is the Input HRV signal Extracted from the ECG signal.

shows the Noise removed from the input signal. Figure 4 illustrates the threshold window signal

after removing the noise from the HRV signal. Figure 5 is Extracted R peaks from the ECG

sed work is done by the Lab View/Simulink tool and the output is

Fig2. Input ECG signal.

Fig3. Noise removed input signal.

013) 17


is the derivative of the commencement purpose described above, which itself does not

arder for the vary in weights to a concealed node, but it can be shown that the

(5)

th nodes, which signify the output layer. So to

er the output layer weights according to the

back propagation of the

arious arrhythmia



Extracted from the ECG signal. Figure 3

Figure 4 illustrates the threshold window signal

Figure 5 is Extracted R peaks from the ECG

sed work is done by the Lab View/Simulink tool and the output is

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Fig4. Threshold window signal

Fig5. Extracted R peaks from the ECG signal.

5. CONCLUSION AND FUTURE WORK

In this paper, an effective HRV-based arrhythmia classification method is presented.

Initially, nine features are extracted from HRV segments. In order to reduce the learning time and

also to improve the efficiency of the classifier, three reduced feature vectors must be

subsequently extracted from nine original ones using the GDA technique. Thereafter, the MLP-

based classifier method is to be used to classify the arrhythmias. The method is to be analyzed for

accuracy with various other arrhythmia conditions. The analysis of the heart rate variability

(HRV) and its relation to the other physiological signals is measured; at first we extract the HRV

signals from the ECG signals and interfering signals such as the mains 50 Hz, the

electromyogram (EMG) signals are removed by 5-15 Hz band pass filter to avoid the noise that

affected the extraction process. Hence, these interfering signals are removed from the input ECG

signal using a band pass filter. Then the signal is processed using thresholding and the resultant

signal is identified as HRV signal. The power spectral density (PSD) for the HF and LF bands are

calculated and the ratio of the LF and HF bands power (LF/HF) is considered as the Frequency

domain feature of the HRV signal.



REFERENCES

[1] Clayton, R., A. Murray and R. Campbell. ‘Recognition of ventricular fibrillation using neural

networks’ Medical and Biological Engineering and Computing, 32 (2): 217-220, 1994.

[2] Minami, K. H. Nakajima and T. Toyoshima. ‘Real -time discrimination of ventricular

tachyarrhythmia with Fourier-transform neural network’ Biomedical Engineering, IEEE

Transactions, 46 (2): 179-185, 1999.

[3] Chary, R. et al.,. ‘Classification of cardiac abnormalities using heart rate signals’. Medical

and Biological Engineering and Computing, 42 (3): 288-293, 2004.

[4] Yang, T., B. Devine and P. Macfarlane. ‘Artificial neural networks for the diagnosis of atria

fibrillation’ Medical and Biological Engineering and Computing, 32 (6): 615-619, 1994.

[5] Al-Fahoum, A. and I. Howitt. ‘Combined wavelet transformation and radial basis neural

networks for classifying life-threatening cardiac arrhythmias’ Medical and Biological

Engineering and Computing, 37 (1): 566-573, 1999.

[6] Song, M.H. et al.,. ‘Support vector machine based arrhythmia classification using reduced

features’. International Journal of Control Automation and Systems, 3 (4): 571, 2005.

[7] Asl, Babak Mohammadzadeh, Seyed Kamaledin Setarehdan and Maryam Mohebbi. ‘Support

vector machine-based arrhythmia classification using reduced features of heart rate variability

signal’. Artificial Intelligence in Medicine, 44 (1): 51-64, 2008.

[8] Tsipouras, M.G., Y. Goletsis and D.I. Fotiadis. ‘A method for arrhythmic episode classification

in ECGs using fuzzy logic and Markov models’ In Computers in Cardiology, 2004, pp: 361-364,

2004.

[9] Tsipouras, M.G., D.I. Fotiadis and D. Sideris. ‘An arrhythmia classification system based on the

RR-interval signal’ Artificial Intelligence in Medicine, 33 (3): 237-250, 2005.

[10] Tsipouras, Markos G. and Dimitrios I. Fotiadis. ‘Automatic arrhythmia detection based on time

and time-frequency analysis of heart rate variability’. Computer Methods and Programs in

Biomedicine, 74 (2): 95-108, 2004.

[11] Rajendra Acharya U. Et al. ‘Classification of heart rate data using artificial neural network

and fuzzy equivalence relation’ Pattern Recognition 36 (1): 61-68, 2003.

Authors

Prof.V.Karthikeyan has received his Bachelor’s Degree in Electronics and

Communication Engineering from PGP college of Engineering and Technology in

2003, Namakkal, India, He received Masters Degree in Applied Electronics from KSR

college of Technology, Erode in 2006 He is currently working as Assistant Professor

in SVS College of Engineering and Technology, Coimbatore. She has about 8 years of

Teaching Experience

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Prof.V.J.Vijayalakshmi has completed her Bachelor’s Degree in Electrical &

Electronics Engineering from Sri Ramakrishna Engineering College, Coimbatore,

India She finished her Masters Degree in Power Systems Engineering from Anna

University of Technology, Coimbatore, She is currently working as Assistant

Professor in Sri Krishna College of Engineering and Technology, Coimbatore She has

about 5 years of teaching Experience.

Mr P.Jeyakumar Currently pursuing his Bachelor’s Degree in Electronics

Engineering in Karpagam University, Coimbatore, Tamil Nadu, India.

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