Classification and Detection of ECG-signals using Artificial Neural Networks

Classification of ECG-signals using Artificial Neural Networks

Prateek A. MadneS.B.Jain Institute of Technology Management and Research, Dept of ETC, Nagpur

Email: [email protected]

Gaurav D. UpadhyayS.B.Jain Institute of Technology Management and Research, Dept of ETC, Nagpur


Sumit M. PaliS.B.Jain Institute of Technology Management and Research, Dept of ETC, Nagpur


Akshay S. ThawareS.B.Jain Institute of Technology Management and Research, Dept of ETC, Nagpur


Abstract – An electrocardiogram (ECG) is a bio-electrical signal which is used to record 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. INTRODUCTIONElectrocardiography deals with the electrical activity of the heart beat. Bio-signals are a non-stationary signals, the reflection may occur at random in the time-scale. Therefore, for determining of disease, ECG signal pattern and heart rate variability may have to be observed for several hours. Thus the volume of the data being enormous, the study is tedious and time taking. Hence, computerized based analysis and classification of heart diseases can be very helpful in diagnosis process. The ECG may roughly be divided into the phases of repolarisation and depolarization of the muscle fibers of heart. The depolarization phases relates 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 and separate rules are applied to locate the onsets and offsets of the QRS complexes.

Fig.1. Normal ECG waveform

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II. LITERATURE SURVEYNazmy 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 obtained by the RR-interval of ECG. This paper proposed the classified ECG signals are normal sinus rhythm, premature ventricular contraction, atrial premature contraction, Ventricular Tachycardia, Ventricular Fibrillation and Supraventricular Tachycardia .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 consist of phase space and attractors, correlation dimension, spatial filling index and approximate entropy. The new program is developed for ECG classification which is based on the chaos method and has developed semi-automatic program for the feature extraction. The program is helpful to classify the ECG Signal and extract the feature of the signal.

Castro et al. in [3] describe the feature extraction with the help of wavelet transform technique and gives an algorithm which will utilize the wavelet transform for extracting the features of ECG signal . This proposed method first denoise by use of soft or hard threshold then the feature of ECG wave divided in to coefficient vector by optimal wavelet transformation. In this 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 as QRS complex, T wave, P wave then sum to obtain feature extraction.

Wisnu Jatmiko, et al. employed Back-Propagation Neural Network 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, Normal beat , Right Bundle Branch Block beat , Premature Ventricular Contraction . They used training classification methods namely Back propagation and FLVQ for their experiment. It provides an average accuracy 99.20% using Back- Propagation and 95.50% for FLVQ. The result shows that back-propagation leading than FLVQ and 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 based algorithm for classification of Paced Beat, Atrial Premature Beat arrhythmias as well as the normal beat signal. They applied Discrete Wavelet Transform for feature extraction and used it along with timing interval features to train the Neural Network. About 10 recording of the MIT/BIH arrhythmia database have been used for training and testing the neural network based classifiers.

The model result shows that the classification accuracy is 96.54%.

Karpagachelvi.S, [5], this paper describes an 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 feature exacting, the sensitivity of the RVM classifier is tested and that is compared with ELM. The obtained result confirms the superiority of the RVM approach when compared to traditional classifiers.

Ruchita Gautam and Anil Kumar Sharma [6] proposed a method based on the Dyadic wavelet transform technique this method is applied for finding the QRS complex. In this method focused on the interval of the two consecutive R wave and calculate the heartbeat. This method is on the ECG waveforms for detect the dieses Ventricular Late Potentials, and separate the wave P R & T which is associated with features of ECG waveforms. In this 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 techniques 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 this technique first phase give information about the point are resigned to the closest cluster around the centroid. The second phase gives information on line value where values are self-resigned. The data is taken from MIT-BIH for analysis. The success rate of classification for set 2, set 3, set 4, set 5 and set 7 is 99.98%, for set 1 it is 87.5% and for set 6 it is 75%.

III. Probabilistic Neural Network

An artificial neural network (ANN) has been used to solve a wide variety of tasks that are hard to solve using ordinary rule based programming. In this Probabilistic Neural Network was used for classification. A probabilistic neural network is a feed-forward neural network derived from the Bayesian-network and a statistical algorithm called Kernel-Fisher Discriminant analysis. In a PNN, the operation are organised into a multi-layered feed-forward neural network with three layers namely Input layer, hidden layer and Decision layer. There is only 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 Hidden layer. Hidden layer has one neuron for each case in the training data set. The neurons store the values of the predictor variable for the case beside with the target value.

Fig.2:Artifical Neural Network

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IV. Wavelet Transform

The wavelet transform is a convolution of the wavelet functions y(t) with the signal x(t). Discrete Wavelets are associated with scaling functions ϕ(t). Wavelet transform: For extracting parameters of ECG signal, we have used wavelet transform. Wavelet analysis breaks a signal into its constituent parts for analysis. The scaling function can be convolved with the signal to provide approximation coefficients. The discrete Wavelet Transform can be written as follows:

Tm,n =∫ x(t)*ym,n (t)dt

A. Performance MeasureWe have used three parameters for estimate the performance of our algorithm. Those are accuracy, sensitivity. These parameters are defined using 4 measures True Positive , True Negative , False Positive , and False Negative .True Positive (TP): It describes that arrhythmia detection coincides with decision of physician.True Negative (TN): In this both classifier and physician suggested absence of arrhythmia.False Positive (FP): The system labels a healthy case as an arrhythmia one.False Negative (FN): The system labels an arrhythmia as healthy.Accuracy: Accuracy is the ratio of number of correctly classified cases and is given as,Accuracy= (TP+TN) / NTotal number of cases are NSensitivity: Sensitivity refers to the rate of correctly classified positive. Sensitivity may be related as a True Positive Rate. Sensitivity should be high for a classifier.Sensitivity = TP / (TP+FN)

V. METHODOLOGY

Denoising and detection of the QRS complex in an ECG signal provides the information about various cardiac abnormalities. It provides validation for the diagnosis of cardiac diseases. For this important reason, it has earned a great respect in medical community. The presence of noise and time varying morphology makes the detection difficult.

Fig. 3: Block diagram of ECG classification

Preprocessing ECG signals helps us to remove contaminants from the ECG signal. ECG pollutants can be classified into the following categories: Power line interference, contact noise, Patient–electrode motion

artifacts, Muscle noise, Baseline wandering. Digital filtering methods and 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 since they define the cardiac beats. Heart rate is the very important parameter that is used to detected for analyzing the abnormality in the heart. Heart rate is calculated based on RR interval. The detection of the QRS complex is the most important job in automatic ECG signal analysis. Q and S point are detected after detecting the R peak by the slope inversion method. Wave shape and the signal are classified into different arrhythmia case.

VI. RESULT

Fig.4 : Extracted signal from database with Noise

Fig.5: Correlating the signal with Symlet4

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Acquire

d ECG

Signal

Pre-processing

R peak Detection

Classification

of ECG Signal

s

Fig.6 Detection of R-peaks

Fig.7: GUI of Disease Detection

VII. 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.

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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.

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