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

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/

Prediction of Sudden Cardiac Death Using Ensemble Classifiers

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this dissertation was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction points of 180 days and 365 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Early Detection and Continuous Monitoring of Atrial Fibrillation from ECG Signals with a Novel Beat-Wise Severity Ranking Approach

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) for user feedback. For this study, we used ECG recordings from MIT BIH Atrial Fibrillation, MIT BIH Long-term Atrial Fibrillation Database. All ECG signals are preprocessed for reducing noise using filter. Preprocessed signal is analyzed for extracting 39 features including 20 of amplitude type and 19 of interval type. The feature space for all ECG recordings is considered for Classification. Training and testing data include all classes of data i.e., beats to identify various episodes for severity. Feature space from the test data is fed to the classifier which determines the class label based on trained model. A class label is determined based on number of occurences of AF and other arrhythmia episodes such as AB(Atrial Bigeminy), SBR(Sinus Bradycardia), SVTA(Supra Ventricular Tacchyarrhythmia). Accuracy of 96.7764% is attained with Random Forest algorithm, Furthermore, precision and recall are determined based on correct and incorrect classifications for each class. Precision and recall on average of Random Forest Classifier are obtained as 0.968 and 0.968 respectievely. This work provides a novel approach to enhance existing method of AF detection by identifying heartbeat class and calculates a quantitative severity metric that might help in early detection and continuous monitoring of AF

Development of a Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 milliseconds, or 200 beats per minute. The design achieves stable performance metrics over the frequency range of 202Hz to 2.8kHz with an accuracy of 77.12%, positive predictive value (PPV) of 75.85%, and a negative predictive value (NPV) of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL (the largest EKG database available for research) validation set, and 84.17%, 78.37%, 87.55% over the PTB-XL test set. Major design contributions and findings of this work reveal (1) a method for the realtime detection of ventricular depolarization events in the PQRST complex from 12-lead electrocardiograms using Independent Component Analysis (ICA), with a slightly different use of ICA proposed for electrocardiogram analysis and R-peak detection/localization; (2) a multilayer Long-Short Term Memory (LSTM) neural network design that identifies infarcted patients from a single heartbeat of a single-lead (lead II) electrocardiogram; (3) and integrated LSTM neural network with an algorithm that detects the R-peaks in real time for instantaneous detection of myocardial infarctions and for effective monitoring of patients under cardiac stress and/or at risk of myocardial infarction; (4) a fully integrated 12-lead real-time classifier with even higher detection metrics and a deeper neural architecture, which could serve as a near real-time monitoring tool that could gauge disease progression and evaluate benefits gained from early intervention and treatment planning; (5) a real-time frequency-independent design based on a single-lead single-beat MI detector, which is of pivotal importance to deployment as there is no standard sampling frequency for EKGs, making them span a wider frequency spectrum. vi

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks

Heartbeat detection by Laser Doppler Vibrometry and Machine Learning

none6openAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, EmanueleAntognoli, Luca; Moccia, Sara; Migliorelli, Lucia; Casaccia, Sara; Scalise, Lorenzo; Frontoni, Emanuel

Heartbeat detection by laser doppler vibrometry and machine learning

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f 1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f 1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis

Primer on Machine Learning in Electrophysiology

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies