Predicting Arrhythmia Based on Machine Learning Using Improved Harris Hawk Algorithm

Arrhythmia disease is widely recognized as a prominent and lethal ailment on a global scale, resulting in a significant number of fatalities annually. The timely identification of this ailment is crucial for preserving individuals' lives. Machine Learning (ML), a branch of artificial intelligence (AI), has emerged as a highly efficient and cost-effective method for illness detection. The objective of this work is to develop a machine learning (ML) model capable of accurately predicting heart illness by using the Arrhythmia disease dataset, with the purpose of achieving optimal performance. The performance of the model is greatly influenced by the selection of the machine learning method and the features in the dataset for training purposes. In order to mitigate the issue of overfitting caused by the high dimensionality of the features in the Arrhythmia dataset, a reduction of the dataset to a lower dimensional subspace was performed via the improved Harris hawk optimization algorithm (iHHO). The Harris hawk algorithm exhibits a rapid convergence rate and possesses a notable degree of adaptability in its ability to identify optimal characteristics. The performance of the models created with the feature-selected dataset using various machine learning techniques was evaluated and compared. In this work, total seven classifiers like SVM, GB, GNB, RF, LR, DT, and KNN are used to classify the data produced by the iHHO algorithm. The results clearly show the improvement of 3%, 4%, 4%, 9%, 8%, 3%, and 9% with the classifiers KNN, RF, GB, SVM, LR, DT, and GNB respectively

ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using proposed method (SVM based fuzzy logic) gives better results than original SVM method. ECG arrhythmia classification using SVM method based fuzzy logic forms an average value of accuracy level, sensitivity level, and specificity level of 93.5%, 93.5%, and 98.7% respectively. ECG arrhythmia classification using only SVM method forms an average value accuracy level, sensitivity level, and specificity level of 91.83%, 91.83%, and 98.36% respectively

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning

A novel two-stage heart arrhythmia ensemble classifier

Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives

A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

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

Cardiomyopathy Detection from Electrocardiogram Features

Cardiomyopathy means heart (cardio) muscle (myo) disease (pathy) . Currently, cardiomyopathies are defined as myocardial disorders in which the heart muscle is structurally and/or functionally abnormal in the absence of a coronary artery disease, hypertension, valvular heart disease or congenital heart disease sufficient to cause the observed myocardial abnormalities. This book provides a comprehensive, state-of-the-art review of the current knowledge of cardiomyopathies. Instead of following the classic interdisciplinary division, the entire cardiovascular system is presented as a functional unity, and the contributors explore pathophysiological mechanisms from different perspectives, including genetics, molecular biology, electrophysiology, invasive and non-invasive cardiology, imaging methods and surgery. In order to provide a balanced medical view, this book was edited by a clinical cardiologist

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure