![“Classification of ECG-signals using Artificial Neural
Networks”
Gaurav D.Upadhyay1
Akshay S. Thaware2
Sumit M. Pali3
Prateek A. Madne4
Abstract – An electrocardiogram (ECG) is a bioelectrical
signal which records the heart's electrical activity with
respect to time. Early and accurate detection is important
in detecting heart diseases and choosing appropriate
treatment for a patient. ECG signals are used as the
parameter for detection of Cardiac diseases and most of
the data comes from PhysioDataNet and MIT-BIH
database .The pre-processing of ECG signal is performed
with help of Wavelet toolbox and also used for feature
extraction of ECG signal. The complete project is
implemented on MATLAB platform. The performance of
the algorithm is evaluated on MIT–BIH Database. This
paper presents the application of Probabilistic Neural
Networks (PNN) for the classification and detection of
Electrocardiogram (ECG).
Keywords: Electrocardiogram (ECG), MIT-BIH database,
Probabilistic Neural Networks (PNN), Wavelet toolbox.
I. INTRODUCTION
Electrocardiography deals with the electrical activity of the
heart. Bio-signals being non-stationary signals, the
reflection may occur at random in the time-scale.
Therefore, for effective diagnostic, ECG signal pattern and
heart rate variability may have to be observed over several
hours. Thus the volume of the data being enormous, the
study is tedious and time consuming. Therefore, computer-
based analysis and classification of cardiac diseases can be
very helpful in diagnostic. The ECG may roughly be
divided into the phases of depolarization and repolarisation
of the muscle fibers making up the heart. The
depolarization phases correspond to the P-wave (atrial
depolarization) and QRS-wave (ventricles depolarization).
The re-polarization phases correspond to the T-wave.
Arrhythmia is a heart disorder representing itself as an
irregular heartbeat due to malfunction in the electrical
system cells in the heart. It causes the heart to pump blood
less effectively and causing disorders in the heart
conduction process. Early detection of heart disease is very
helpful for living a long life and increase the improvement
of our technique detection of arrhythmias. The technique
used in ECG pattern recognition comprises: ECG signal
pre-processing, QRS detection, feature extraction and
neural network for signal classification. Probabilistic
Neural Network (PNN) is used as a classifier to detect QRS
and non-QRS regions. Most of the QRS detection
algorithms reported in literature detects R-peak locations
and separate rules are applied for the delineation of QRS
i.e. to locate the onsets and offsets of the QRS complexes.
Fig. 1. Normal ECG waveform
II. LITERATURE SURVEY
Nazmy et al [1] described adaptive neuro-fuzzy inference
system (ANFIS) algorithm for classification of ECG wave
.The feature extraction is done with the help of
Independent Component Analysis (ICA) and Power
spectrum and input is provided by the RR interval of ECG.
In this paper the classified ECG signals are normal sinus
rhythm (NSR), premature ventricular contraction (PVC),
atrial premature contraction (APC), Ventricular
Tachycardia (VT), Ventricular Fibrillation (VF) and
Supraventricular Tachycardia (SVT).using ANFIS
approach the classification accuracy is also obtained.
Alan and Nikola in [2] presented that use chaos theory for
classification of ECG signal and feature extraction. In this
paper also consist of including phase space and attractors,
correlation dimension, spatial filling index, central
tendency measure and approximate entropy. A new
program is developed for ECG classification which is
based on the chaos method and also developed semi-
automatic program for feature extraction. The program is
helpful to classify the ECG wave and extract the features of
the signal successfully.
Castro et al. in [3] describe the feature extraction with the
help of wavelet transform technique and also present an
algorithm which will utilize the wavelet transform for
extracting the feature of ECG wave. Their proposed
method first denoised by use of soft or hard threshold then
the feature of ECG wave divided in to coefficient vector by
optimal wavelet transformation. In the proposed method
choose the mother wavelet transform set of orthogonal and
biorthogonal wavelet filter bank by means of the best
correlation with the ECG signal was developed. After the
analysis of ECG signal coefficient are divided QRS
complex, T wave, P wave then sum to obtain feature
extraction.](https://image.slidesharecdn.com/paperi148853927331614914016556371-170405150659/85/Classification-of-ecg-signal-using-artificial-neural-network-1-320.jpg)
![Wisnu Jatmiko, et al. employed Back-Propagation Neural
Networks and Fuzzy Neuro Learning Vector Quantization
(FLVQ) as classifier in ECG classification [3]. In their
work, they used only the MLII lead as source data. The
classes that are considered are Left Bundle Branch Block
beat (LBBB), Normal beat (NORMAL), Right Bundle
Branch Block beat (RBBB), Premature Ventricular
Contraction (PVC). They used training classification
methods namely Back-propagation and FLVQ for their
experiment. It produces an average accuracy 99.20% using
Back- Propagation and 95.50% for FLVQ. The result
shows that back-propagation leading than FLVQ but, back-
propagation has disadvantages to classified unknown
category beat but not for FLVQ. FLVQ has stable accuracy
although contain unknown category beat.
Maedeh Kiani Sarkaleh, [4], proposed a Neural Network
(NN) based algorithm for classification of Paced Beat (PB),
Atrial Premature Beat (APB) arrhythmias as well as the
normal beat signal. They applied Discrete Wavelet
Transform (DWT) for feature extraction and used it along
with timing interval features to train the Neural Network.
About 10 recordings of the MIT-BIH arrhythmias database
have been used for training and testing the neural network
based classifier. The model results show that the
classification accuracy is 96.5%.
Karpagachelvi.S, [5], a novel ECG beat classification
system using RVM is proposed and applied to MIT/BIH
arrhythmia database to classify five kinds of abnormal
waveforms and normal beats. In exacting, the sensitivity of
the RVM classifier is tested and that is compared with
ELM. The obtained results clearly confirm the superiority
of the RVM approach when compared to traditional
classifiers.
Ruchita Gautam and Anil Kumar Sharma [6] proposed a
method is based on the Dyadic wavelet transform (DyWT)
technique this method is applied for finding the QRS
complex. In these method focused on the interval of the
two consecutive R wave and calculate the heartbeat. This
method is applied on the ECG waveforms for detect the
dieses Ventricular Late Potentials (VLP’s), and separate
the wave P R & T which is associated with features of
ECG waveforms, In theses method the main consideration
is to find out the R waves and threshold is set to 75% of the
maximum peak.
Manpreet Kaur, A.S.Arora [7] shows with the help of K
clustering technique the output signal is analyzed, the
parameter is wave shape, duration and amplitude. With the
help of K clustering technique minimize the sum of point
to centroid distance, this clustered K summed. In these
technique first phase give information about the points are
resigned to the closest cluster around the centroid. The
second phase gives information on line value where values
are self-resigned. The data comes from MIT-BIH for
analysis. The success rate of classification for set 2, set 3,
set 4, set 5 and set 7 is 100%, for set 1 it is 87.5% and for
set 6 it is 75%.
III. Probabilistic Neural Network
Artificial neural networks have been used to solve a wide
variety of tasks that are hard to solve using ordinary rule-
based programming. In this work, Probabilistic Neural
Network (PNN) was used for classification. A probabilistic
neural network (PNN) is a feed-forward neural network,
derived from the Bayesian network and a statistical
algorithm called Kernel Fisher Discriminant analysis. In a
PNN, the operations are organized into a multilayered feed-
forward network with four layers namely Input layer,
Pattern layer and Decision layer as shown in figure 13.
There is one neuron in the input layer for each predictor
variable value. The input neurons then supply the values to
each of the neurons in the pattern layer. Pattern layer has
one neuron for each case in the training data set. The
neuron stores the values of the predictor variables for the
case beside with the target value.
IV. Wavelet Transform
The wavelet transform is a convolution of the wavelet
function ψ (t) with the signal x (t). Orthonormal dyadic
discrete Wavelets are associated with scaling functions
ϕ(t). Wavelet transform: For extracting parameters of ECG
we use wavelet transform, wavelet analysis breaks a signal
down into its constituent parts for analysis. The scaling
function can be convolved with the signal to produce
approximation coefficients. The discrete wavelet
transform (DWT) can be written as:
Tm,n =∫ x(t)*ψ m,n (t)dt
A. Performance Measure
We have used three parameters for evaluating performance
of our algorithm. Those are accuracy, sensitivity.These
parameters are defined using 4 measures True Positive
(TP), True Negative (TN), False Positive (FP), and False
Negative (FN).
True Positive: arrhythmia detection coincides with
decision of physician
True Negative: both classifier and physician suggested
absence of arrhythmia
False Positive: system labels a healthy case as an
arrhythmia one
False Negative: system labels an arrhythmia as healthy
Accuracy: Accuracy is the ratio of number of correctly
classified cases, and is given by,
Accuracy= (TP+TN) / N
Total number of cases are N
Sensitivity: Sensitivity refers to the rate of correctly
classified positive. Sensitivity may be referred as a True
Positive Rate. Sensitivity should be high for a classifier.
Sensitivity = TP / (TP+FN).](https://image.slidesharecdn.com/paperi148853927331614914016556371-170405150659/85/Classification-of-ecg-signal-using-artificial-neural-network-2-320.jpg)
Classification of ecg signal using artificial neural network
- 1. “Classification of ECG-signals using Artificial Neural Networks” Gaurav D.Upadhyay1 Akshay S. Thaware2 Sumit M. Pali3 Prateek A. Madne4 Abstract – An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG). Keywords: Electrocardiogram (ECG), MIT-BIH database, Probabilistic Neural Networks (PNN), Wavelet toolbox. I. INTRODUCTION Electrocardiography deals with the electrical activity of the heart. Bio-signals being non-stationary signals, the reflection may occur at random in the time-scale. Therefore, for effective diagnostic, ECG signal pattern and heart rate variability may have to be observed over several hours. Thus the volume of the data being enormous, the study is tedious and time consuming. Therefore, computer- based analysis and classification of cardiac diseases can be very helpful in diagnostic. The ECG may roughly be divided into the phases of depolarization and repolarisation of the muscle fibers making up the heart. The depolarization phases correspond to the P-wave (atrial depolarization) and QRS-wave (ventricles depolarization). The re-polarization phases correspond to the T-wave. Arrhythmia is a heart disorder representing itself as an irregular heartbeat due to malfunction in the electrical system cells in the heart. It causes the heart to pump blood less effectively and causing disorders in the heart conduction process. Early detection of heart disease is very helpful for living a long life and increase the improvement of our technique detection of arrhythmias. The technique used in ECG pattern recognition comprises: ECG signal pre-processing, QRS detection, feature extraction and neural network for signal classification. Probabilistic Neural Network (PNN) is used as a classifier to detect QRS and non-QRS regions. Most of the QRS detection algorithms reported in literature detects R-peak locations and separate rules are applied for the delineation of QRS i.e. to locate the onsets and offsets of the QRS complexes. Fig. 1. Normal ECG waveform II. LITERATURE SURVEY Nazmy et al [1] described adaptive neuro-fuzzy inference system (ANFIS) algorithm for classification of ECG wave .The feature extraction is done with the help of Independent Component Analysis (ICA) and Power spectrum and input is provided by the RR interval of ECG. In this paper the classified ECG signals are normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF) and Supraventricular Tachycardia (SVT).using ANFIS approach the classification accuracy is also obtained. Alan and Nikola in [2] presented that use chaos theory for classification of ECG signal and feature extraction. In this paper also consist of including phase space and attractors, correlation dimension, spatial filling index, central tendency measure and approximate entropy. A new program is developed for ECG classification which is based on the chaos method and also developed semi- automatic program for feature extraction. The program is helpful to classify the ECG wave and extract the features of the signal successfully. Castro et al. in [3] describe the feature extraction with the help of wavelet transform technique and also present an algorithm which will utilize the wavelet transform for extracting the feature of ECG wave. Their proposed method first denoised by use of soft or hard threshold then the feature of ECG wave divided in to coefficient vector by optimal wavelet transformation. In the proposed method choose the mother wavelet transform set of orthogonal and biorthogonal wavelet filter bank by means of the best correlation with the ECG signal was developed. After the analysis of ECG signal coefficient are divided QRS complex, T wave, P wave then sum to obtain feature extraction.
- 2. Wisnu Jatmiko, et al. employed Back-Propagation Neural Networks and Fuzzy Neuro Learning Vector Quantization (FLVQ) as classifier in ECG classification [3]. In their work, they used only the MLII lead as source data. The classes that are considered are Left Bundle Branch Block beat (LBBB), Normal beat (NORMAL), Right Bundle Branch Block beat (RBBB), Premature Ventricular Contraction (PVC). They used training classification methods namely Back-propagation and FLVQ for their experiment. It produces an average accuracy 99.20% using Back- Propagation and 95.50% for FLVQ. The result shows that back-propagation leading than FLVQ but, back- propagation has disadvantages to classified unknown category beat but not for FLVQ. FLVQ has stable accuracy although contain unknown category beat. Maedeh Kiani Sarkaleh, [4], proposed a Neural Network (NN) based algorithm for classification of Paced Beat (PB), Atrial Premature Beat (APB) arrhythmias as well as the normal beat signal. They applied Discrete Wavelet Transform (DWT) for feature extraction and used it along with timing interval features to train the Neural Network. About 10 recordings of the MIT-BIH arrhythmias database have been used for training and testing the neural network based classifier. The model results show that the classification accuracy is 96.5%. Karpagachelvi.S, [5], a novel ECG beat classification system using RVM is proposed and applied to MIT/BIH arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In exacting, the sensitivity of the RVM classifier is tested and that is compared with ELM. The obtained results clearly confirm the superiority of the RVM approach when compared to traditional classifiers. Ruchita Gautam and Anil Kumar Sharma [6] proposed a method is based on the Dyadic wavelet transform (DyWT) technique this method is applied for finding the QRS complex. In these method focused on the interval of the two consecutive R wave and calculate the heartbeat. This method is applied on the ECG waveforms for detect the dieses Ventricular Late Potentials (VLP’s), and separate the wave P R & T which is associated with features of ECG waveforms, In theses method the main consideration is to find out the R waves and threshold is set to 75% of the maximum peak. Manpreet Kaur, A.S.Arora [7] shows with the help of K clustering technique the output signal is analyzed, the parameter is wave shape, duration and amplitude. With the help of K clustering technique minimize the sum of point to centroid distance, this clustered K summed. In these technique first phase give information about the points are resigned to the closest cluster around the centroid. The second phase gives information on line value where values are self-resigned. The data comes from MIT-BIH for analysis. The success rate of classification for set 2, set 3, set 4, set 5 and set 7 is 100%, for set 1 it is 87.5% and for set 6 it is 75%. III. Probabilistic Neural Network Artificial neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule- based programming. In this work, Probabilistic Neural Network (PNN) was used for classification. A probabilistic neural network (PNN) is a feed-forward neural network, derived from the Bayesian network and a statistical algorithm called Kernel Fisher Discriminant analysis. In a PNN, the operations are organized into a multilayered feed- forward network with four layers namely Input layer, Pattern layer and Decision layer as shown in figure 13. There is one neuron in the input layer for each predictor variable value. The input neurons then supply the values to each of the neurons in the pattern layer. Pattern layer has one neuron for each case in the training data set. The neuron stores the values of the predictor variables for the case beside with the target value. IV. Wavelet Transform The wavelet transform is a convolution of the wavelet function ψ (t) with the signal x (t). Orthonormal dyadic discrete Wavelets are associated with scaling functions ϕ(t). Wavelet transform: For extracting parameters of ECG we use wavelet transform, wavelet analysis breaks a signal down into its constituent parts for analysis. The scaling function can be convolved with the signal to produce approximation coefficients. The discrete wavelet transform (DWT) can be written as: Tm,n =∫ x(t)*ψ m,n (t)dt A. Performance Measure We have used three parameters for evaluating performance of our algorithm. Those are accuracy, sensitivity.These parameters are defined using 4 measures True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). True Positive: arrhythmia detection coincides with decision of physician True Negative: both classifier and physician suggested absence of arrhythmia False Positive: system labels a healthy case as an arrhythmia one False Negative: system labels an arrhythmia as healthy Accuracy: Accuracy is the ratio of number of correctly classified cases, and is given by, Accuracy= (TP+TN) / N Total number of cases are N Sensitivity: Sensitivity refers to the rate of correctly classified positive. Sensitivity may be referred as a True Positive Rate. Sensitivity should be high for a classifier. Sensitivity = TP / (TP+FN).
- 3. V. METHODOLOGY Denoising and detection of the QRS complexes in an ECG signal provides information about various cardiac abnormalities. It supplies evidence for the diagnosis of cardiac diseases. For this very important reason, it has earned a great respect in medical community. Unfortunately, the presence of noise and time-varying morphology makes the detection difficult. Fig. 3 Block diagram of ECG classification Preprocessing ECG signals helps us remove contaminants from the ECG signals. ECG contaminants can be classified into the following categories: Power line interference, contact noise, Patient–electrode motion artifacts, Electromyography (EMG) noise, Baseline wandering. Digital filtering methods as well as wavelet based methods are used to remove baseline wandering and the other wideband noise. The baseline wandering and the above noises are removed by taking two approximation level coefficients. Detection of R peaks is very important because they define the cardiac beats. Heart rate is the important parameter that is detected for analyzing the abnormality in the heart. Heart rate is calculated based on R-R interval. The detection of the QRS-complex is the most important task in automatic ECG signal analysis. Q and S points are detected after detecting the R peak by the slope inversion method. Wave shape and the signal are classified into various arrhythmia cases. VI. CONCLUSION This study is on detection and classification of arrhythmia beats. The heart beats are different for different person and all these beats are having different variations with nonlinear nature. Thus the proposed computerized system will be helpful for early detection of heart status and to decrease the death percentage of human which occurs due to the heart disease. REFERENCE T. M. Nazmy, H. El-Messiry and B. Al-bokhity. 2009. Adaptive Neuro- Fuzzy Inference System for Classification of ECG Signals, Journal of Theoretical and Applied Information Technology. Alan Jovic, and Nikola Bogunovic, 2007.Feature Extraction for ECG Time-Series Mining based on Chaos Theory, Proceedings of 29th International Conference on Information Technology Interfaces. B. Castro, D. Kogan, and A. B. Geva, 2000. ECG feature extraction using optimal mother wavelet, The 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, pp. 346-350. Wisnu Jatmiko, Nulad W. P., Elly Matul I.,I Made Agus Setiawan, P. Mursanto,” Heart Beat Classification Using Wavelet Feature Based on Neural Network ,” Wseas Transactions on Systems, ISSN: 1109-2777 Issue 1, Volume 10, January 2011. Maedeh Kiani Sarkaleh and Asadollah Shahbahrami, “Classification of ECG Arrhythmias using Discrete Wavelet Transform and Neural Networks”, International Journal of Computer Science, Engineering and Applications (IJCSEA) Volume 2, Issue 1, February 2012. Karpagachelvi.S, Dr.M.Arthanari and Sivakumar M, “Classification of Electrocardiogram Signals with Extreme Learning Machine and Relevance Vector Machine”, International Journal of Computer Science Issues, Volume 8, Issue 1, January 2011 ISSN (Online): 1694-0814. V. Vijaya, K. Kishan Rao, V. Rama, “Arrhythmia Detection through ECG Feature Extraction using Wavelet Analysis”, European Journal of Scientific Research, Vol. 66, pp. 441-448, 2011.