WAVELET BASED FEATURE EXTRACTOR AND ANN BASED CLASSIFIER FOR OPTIMAL ECG INTERPRETATION

The heart plays the most vital role of supplying nutrients and oxygen in any organism. Any abnormality in its function renders the body to many complications which may sometimes even lead to death. Hence, timely and early diagnosis of any abnormality is extremely important. Another requirement of the hour is the Automatic detection. Several techniques have been developed till date, but efficiency achieved so far leaves room for improvement. This paper also, presents a technique that aims at automatic detection of cardiac abnormality using an Artificial Neural Network. The detection is done on the basis of the wave shapes of different QRS complexes for different arrhythmias which are extracted from the ECG beats using Wavelet Transform. As the Daubechies wavelets are similar in shape to the QRS complex of the ECG, db4 has been used in the above context. The performance accuracies achieved for training, testing known data and unknown data have been found to be 99.7%, 99.2% and 96.2% respectively. The MIT-BIH database has been used for the present study and an altogether of seven different beats have been used for classification

COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%

DETECTION OF ARTIAL FIBRILLATION DISORDER BY ECG USING DISCRETE WAVELET TRANSFORMS

Atrial fibrillation (A-fib) is the most common cardiac disorder. To efficiently treat or inhibit, an automatic detection based on electrocardiograph (ECG)monitoring is significantly required. ECG is a key function in the analysis of the heart functioning and diagnostic of diseases. Currently, a computer basedsystem is used to analyze the ECG signal. The main aim of this project is to analyze a heart malfunctions named as A-fib, using discrete wavelet transforms(DWT). The ECG signals were decomposed into time-frequency representations using DWT, and the statistical features were calculated to describe theirdistribution. The DWT detailed coefficients are used to obtain various parameters of the ECG signal such as the mean, variance, standard deviation, andentropy of the signal. An analysis had been made with these parameters of various patients with normal heart functioning and A-fib to identify the disorder.Keywords: Atrial fibrillation, Electrocardiogram, Discrete wavelet transforms

Wavelet diagnosis of ECG signals with kaiser based noise diminution

The evaluation of distortion diagnosis using Wavelet function for Electrocardiogram (ECG), Electroen- cephalogram (EEG) and Phonocardiography (PCG) is not novel. However, some of the technological and economic issues remain challenging. The work in this paper is focusing on the reduction of the noise inter- ferences and analyzes different kinds of ECG signals. Furthermore, a physiological monitoring system with a programming model for the filtration of ECG is presented. Kaiser based Finite Impulse Response (FIR) filter is used for noise reduction and identifica- tion of R peaks based on Peak Detection Algorithm (PDA). Two approaches are implemented for detect- ing the R peaks; Amplitude Threshold Value (ATV) and Peak Prediction Technique (PPT). Daubechies wavelet transform is applied to analyze the ECG of driver under stress, arrhythmia and sudden cardiac arrest signals. From the obtained results, it was found that the PPT is an effective and efficient technique in detecting the R peaks compared to ATV

IDENTIFICATION OF TACHYCARDIA AND BRADYCARDIA HEART DISORDERS USING WAVELET TRANSFORM BASED QRS DETECTION

In the recent years Cardiac disorder is a very common problem faced by the people. The ECG is the most important test for the interpretation of cardiac abnormalities. The ECG gives the electrical activity of the human heart and by analyzing the deviation in these electrical activities, conclusion can be drawn. The study is divided into two parts. In the first part it deals with the detection of real time ECG waveform from the MIT-BIT Arrhythmia database and then these signals is further diagnosed by applying Wavelet Transform for R-peak detection. The second part of the study deals with the calculation of heart rate with the help of R-peaks detected and accordingly the cardiac arrhythmia can be analyzed. The study has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and takes less computation time

Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease