Analysis of Acute Myocardial Infarction using ECG Signals and Machine Learning Algorithms (original) (raw)
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Background Electrocardiogram (ECG) interpretation is based on the understanding of cardiac electrical patterns. Machine learning (ML) techniques have been used to interpret ECGs, however, there is a lacuna in models able to identify the timing and affected cardiac territories with high accuracy. We aimed to utilize machine learning techniques coupled with relevant medical knowledge to create a machine learning model to detect MI with greater accuracy along with affected territory and timing. Methods A dataset containing 452 ECGs with 279 features from the University of California, Irvine, Machine Learning Repository was utilized. Three machine learning classification models namely Bootstrap Aggregation Decision Trees (BADT), Random Forest (RF) and Multi-layer Perceptron (MLP) were fed with ECG features selected based on the medical knowledge, categorized as normal, acute ischemic changes, old anterior MI and old inferior MI.
Detection of Myocardial Infarction in Electrocardiograms using Machine Learning
International Journal of Computer Applications, 2021
Currently, millions of people in the world have some type of deficiency in the cardiovascular system, more specifically anomalies in the heart and its heartbeat, most of these individuals end up not discovering these problems in advance, which would have a great impact on the chance of survival. In Brazil, the number of deaths caused by heart problems exceeds 350 thousand per year. The solution found to assist in the prevention and detection of pre-existing problems starts from the approach of analyzing electrocardiograms of people with already known conditions and anomalies, starting from the machine learning method for preventing conditions with only data input to a model. The proposal of this work designs in a prototype in which, in just a few moments, it generates a prediction with a considerable success rate, capable of assisting health professionals to make decisions regarding the patient's situation, based on the analysis of waves from an electrocardiogram (ECG). During this work, it is demonstrated the entire process of data acquisition and selection, treatment and filtering of wave signals until the development of an exam prediction. The results found were correct rates in the infarction class,
Engineering, Technology and Applied science research/Engineering, Technology and Applied Science Research, 2024
Myocardial Infarction (MI) is a condition often leading to death. It arises from inadequate blood flow to the heart, therefore, the classification of MI complications contributing to lethal outcomes is essential to save lives. Machine learning algorithms provide solutions to support the categorization of the MI complication attributes and predict lethal results. This paper compares various machine learning algorithms to classify myocardial infarction complications and to predict fatal consequences. The considered algorithms are Multilayer Perceptron (MLP), Naive Bayes (NB), and Decision Tree (DT). The main objective of this paper is to compare these algorithms in two scenarios: initially using the full dataset once and then using the dataset again, after implementing the WEKA attribute selection algorithm. To accomplish this goal, data from the Krasnoyarsk Interdistrict Clinical Hospital were employed. Results in general revealed that the MLP classifier demonstrated optimal performance regarding the full MI data, whereas the DT classifier emerged as more favorable when the dataset sample size was diminished through an attribute selection algorithm.
A Survey of Applications of Artificial Intelligence for Myocardial Infarction Disease Diagnosis
ArXiv, 2021
Myocardial infarction disease (MID) is caused to the rapid progress of undiagnosed coronary artery disease (CAD) that indicates the injury of a heart cell by decreasing the blood flow to the cardiac muscles. MID is the leading cause of death in middle-aged and elderly subjects all over the world. In general, raw Electrocardiogram (ECG) signals are tested for MID identification by clinicians that is exhausting, time-consuming, and expensive. Artificial intelligence-based methods are proposed to handle the problems to diagnose MID on the ECG signals automatically. Hence, in this survey paper, artificial intelligence-based methods, including machine learning and deep learning, are review for MID diagnosis on the ECG signals. Using the methods demonstrate that the feature extraction and selection of ECG signals required to be handcrafted in the ML methods. In contrast, these tasks are explored automatically in the DL methods. Based on our best knowledge, Deep Convolutional Neural Networ...
Detection of myocardial infarction on recent dataset using machine learning
International Journal of Informatics and Communication Technology (IJ-ICT)
In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and e...
2016
This paper proposes a classification technique using conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria which improves the accuracy of detecting Arrhythmia using Electrocardiogram (ECG) data. ECG is the most widely used first line clinical instrument to record the electrical activities of the heart. The data-set from UC Irvine (UCI) Machine Learning Repository was used to implement a multi-class classification for different types of heart abnormalities. After implementing rigorous data preprocessing and feature selection techniques,different machine learning algorithms such as Neural Networks, Decision trees, Random Forest, Gradient Boosting and Support Vector Machines were used. Maximum experimental accuracy of 84.82% was obtained via the conjunction of SVM and Gradient Boosting. A further improvement in accuracy was obtained by validating the factors which were important for doctors to decide between normal and abnormal heart conditions.The performance of class...
Physiological Measurement, 2022
Objective. 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 worldwide. 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. Approach. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some 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. Main results. 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. Significance. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
Classification and Prediction of Cardiac Arrhythmia using Machine Learning: A Survey
International Journal for Research in Applied Science and Engineering Technology, 2019
Heart disease is the most common cause of death globally. According to a recent study by the Indian Council of Medical Research (ICMR) near about 25% of deaths between the ages of 25-69 years cause due to different heart-related problems. The cardiovascular diseases are the highest increased diseases. So, we should also have jumped on techniques and methods used for alertness and care to avoid the sudden death of the people because of the heart attack. Heart disease prediction using data mining is one of the most imperative and challenging tasks. The shortage of specialists and high wrongly diagnosed cases has espoused the need to develop an efficient detection system. This paper provides a quick and easy review and understanding of available prediction models using different techniques.
Machine learning in electrocardiogram diagnosis
2009 International Multiconference on Computer Science and Information Technology, 2009
The electrocardiogram (ECG) is a measure of the electrical activity of the heart. Since its introduction in 1887 by Waller, it has been used as a clinical tool for evaluating heart function. A number of cardiovascular diseases (CVDs) (arrhythmia, atrial fibrillation, atrioventricular (AV) dysfunctions, and coronary arterial disease, etc.) can be detected non-invasively using ECG monitoring devices. With the advent of modern signal processing and machine learning techniques, the diagnostic power of the ECG has expanded exponentially. The principal reason for this is the expanded set of features that are typically extracted from the ECG time series. The enhanced feature space provides a wide range of attributes that can be employed in a variety of machine learning techniques, with the goal of providing tools to assist in CVD classification. This paper summarizes some of the principle machine learning approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and their classification accuracy.
IEEE access, 2024
Electrocardiogram is a heartbeat signal that can be used for the application of Humancomputer interaction. Electrocardiography (ECG) is a prominent way to analyze heart rate and to diagnose cardiovascular disease. However, its availability has been restricted, especially in contexts with limited resources, due to the cost associated with conventional ECG signal processing equipment. The importance of ECG signal processing classification for improving early diagnoses in clinical and remote monitoring contexts is highlighted here. The dataset considered for this work is MIT-BIH arrhythmia which has 15 categories and summarized in 5 classes Normal (N), Superventricular ectopic beats (SVEB), Ventricular ectopic beat (VEB), Fusion beats (F), and Unknown beats (Q). The work discusses the importance of automated classification techniques that make it possible to analyze vast amounts of ECG data effectively and objectively. This research presents an investigation into the classification of ECG signals using various Machine Learning (ML) methods. Specifically, the performance of Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms are examined. Among these classifiers, RF exhibits a remarkable accuracy of 98%. The results demonstrate the superior performance of the proposed approach for heartbeat classification systems.