Dianosing Heart Diseases Using ANN and GA

The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body.The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing electrical current in the heart muscle the electrical current can be captured and analyzed to diagnose the heart state. This operation is done by using electrocardiograph (ECG) device; this device captures the electrical signal, filters it from noise signals, and amplifies it. Then it displays the signal on the screen or prints it on the trace paper then the doctor interprets the ECG signal to diagnose the disease.This project discusses using artificial intelligent (AI) to process and analyze the ECG signal to diagnose the heart disease directly and display detailed report about the heart state by using the artificial neural network (ANN) after training it and finding the values of the connection weights using the genetic algorithm (GA) to choose the best values to the weights.The GA is qualified in enhancing the weights of the ANN since the ANN is trained using the classical algorithm (back-propagation), the genetic algorithm is used as a co-training algorithm for enhancing the connection weights values and minimizing the error value

Pre-Diagnosis of Hypertension Using Artificial Neural Network

This paper aims to use artificial neural network ANN to contribute to pre-diagnosis of hypertension prediction, In this paper used the MATLAB to building ANN model in this model a number of people were tested to predict whether they had blood pressure disease or whether they were not infected this paper found the performance of pre-diagnosis of hypertension using artificial neural network is good method for healthcare based on results the accuracy of model reaches 81 % the proposed neural network is back propagation neural network incudes of seven input neurons in the input layer which are the factors of hypertension, four hidden neurons in the hidden layer The node of the output layer is the one that gives the classification for the data. It classifies that are having hypertension or not having hypertension.&nbsp

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

Wavelet based QRS detection in ECG using MATLAB

In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survivalanalysis of heart diseases. Electrocardiography has had a profound influence on the practice of medicine.This paper deals with the detection of QRS complexes of ECG signals using derivativebased/Pan-Tompkins/wavelet transform based algorithms. The electrocardiogram signal contains animportant amount of information that can be exploited in different manners. The ECG signal allows for theanalysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals fromMIT/BIH Arrhythmia data base are used to verify the various algorithms using MATLAB software.Wavelet based algorithm presented in this paper is compared with the AF2 algorithm/Pan-Tompkinsalgorithms for signal denoising and detection of QRS complexes meanwhile better results are obtained forECG signals by the wavelet based algorithm. In the wavelet based algorithm, the ECG signal has beendenoised by removing the corresponding wavelet coefficients at higher scales. Then QRS complexes aredetected and each complex is used to find the peaks of the individual waves like P and T, and also theirdeviations.Keywords: Electrocardiogram (ECG), AF2 Algorithm, MATLAB, Pan-Tompkins algorithm, WaveletTransform, Denoisin

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

An algorithm for evaluating the performance of adaptive filters for the removal of artifacts in ECG signals

Filtering electrocardiogram (ECG) signals calls for a filter whose impulse response can be automatically adjusted according to the varying characteristics of the signal and artifacts. In order to eliminate effectively the artifacts in ECG signals, we propose the unbiased linear artificial neural network (ULANN) as a new type of adaptive filter. This paper compares the performance of the ULANN filter with the prevailing least-mean-squares (LMS) and recursive-least-squares (RLS) adaptive filters, for the removal of artifacts in noisy ECG signals. The measures of performance include the root-mean-squared error, a normalized correlation coefficient (NCC), and entropy. A template derived from each ECG signal is used as a reference to derive the measures. The NCC values for the ULANN, LMS, and RLS filter, averaged over 22 ECG signals, are 0.9956 +/- 0.0022, 0.9948 +/- 0.0020, and 0.9940 +/- 0.0026, respectively. The results indicate that the ULANN filter provides filtered signals with the highest waveshape fidelity among the three filters studied

Cancellation of artifacts in ECG signals using a normalized adaptive neural filter

Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter