4.7 Review

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Cardiac & Cardiovascular Systems

Incidence of Sudden Cardiac Death in the European Union

Jean-Philippe Empana et al.

Summary: This study aimed to determine the incidence of sudden cardiac death and out-of-hospital cardiac arrest in the European Union by utilizing data from four large population-based registries with at least 5 years of data collection.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2022)

Article Cardiac & Cardiovascular Systems

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Albert J. Rogers et al.

Summary: This study developed computational phenotypes of patients with ischemic cardiomyopathy by analyzing ventricular monophasic action potentials using machine learning. The findings revealed novel phenotypes for predicting long-term outcomes and may provide insights into cellular mechanisms for clinical outcomes in ischemic cardiomyopathy and potentially other conditions.

CIRCULATION RESEARCH (2021)

Article Engineering, Biomedical

Heart rate variability feature selection method for automated prediction of sudden cardiac death

Ashkan Parsi et al.

Summary: The proposed method achieves high sensitivity and specificity in predicting ventricular arrhythmias by analyzing cardiac data. It reduces the number of features and processes signals in different window lengths, outperforming related work in the literature.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Mathematical & Computational Biology

Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning

Da Un Jeong et al.

Summary: Ventricular fibrillation is a major cause of mortality worldwide, and heart rate variability is used as a biomarker for detecting life-threatening arrhythmias. Research has shown that features extracted from heart rate variability data can accurately predict the onset of ventricular fibrillation.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2021)

Article Biochemistry & Molecular Biology

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Xiaoxi Yao et al.

Summary: The study aimed to evaluate whether an AI-powered clinical decision support tool based on ECGs enables early diagnosis of low EF. Results showed that the intervention increased the diagnosis of low EF in primary care settings and among patients with a high likelihood of low EF, indicating the potential of using AI algorithms for early diagnosis in routine care.

NATURE MEDICINE (2021)

Article Cardiac & Cardiovascular Systems

Higher chances of survival to hospital admission after out-of-hospital cardiac arrest in patients with previously diagnosed heart disease

Laura Helena van Dongen et al.

Summary: This study found that patients with a pre-OHCA diagnosis of heart disease had higher chances of survival to hospital admission and were more likely to have a shockable initial rhythm, but their odds of survival to hospital discharge were not significantly different from patients without this diagnosis.

OPEN HEART (2021)

Article Multidisciplinary Sciences

Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features

M. Murugappan et al.

Summary: This study aims to predict SCA using the Rpeak to T-end (R-T-end) feature in ECG signals, extracting four nonlinear features and classifying them using three classifiers. The combination of sample entropy feature and support vector machine classifier can effectively predict the onset of SCA with the highest classification accuracy.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2021)

Review Cardiac & Cardiovascular Systems

Predicting sustained ventricular arrhythmias in dilated cardiomyopathy: a meta-analysis and systematic review

Arjan Sammani et al.

ESC HEART FAILURE (2020)

Article Emergency Medicine

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

Joon-myoung Kwon et al.

SCANDINAVIAN JOURNAL OF TRAUMA RESUSCITATION & EMERGENCY MEDICINE (2020)

Article Computer Science, Information Systems

Predicting Ventricular Fibrillation Through Deep Learning

Li-Ming Tseng et al.

IEEE ACCESS (2020)

Article Cardiac & Cardiovascular Systems

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Rutger R. van de Leur et al.

ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW (2020)

Article Computer Science, Interdisciplinary Applications

An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal

Elias Ebrahimzadeh et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2019)

Article Cardiac & Cardiovascular Systems

Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest

Duc H. Do et al.

AMERICAN JOURNAL OF CARDIOLOGY (2019)

Article Engineering, Biomedical

A novel multi-class approach for early-stage prediction of sudden cardiac death

Reeta Devi et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2019)

Article Physiology

Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape

Getu Tadele Taye et al.

FRONTIERS IN PHYSIOLOGY (2019)

Article Multidisciplinary Sciences

Machine learning algorithm validation with a limited sample size

Andrius Vabalas et al.

PLOS ONE (2019)

Article Medicine, General & Internal

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Robert F. Wolff et al.

ANNALS OF INTERNAL MEDICINE (2019)

Article Computer Science, Hardware & Architecture

A novel algorithm for the prediction and detection of ventricular arrhythmia

Nourhan Bayasi et al.

ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING (2019)

Article Psychiatry

How to perform a meta-analysis with R: a practical tutorial

Sara Balduzzi et al.

EVIDENCE-BASED MENTAL HEALTH (2019)

Article Cardiac & Cardiovascular Systems

Associations between common ECG abnormalities and out-of-hospital cardiac arrest

Marc Meller Sondergaard et al.

OPEN HEART (2019)

Article Computer Science, Interdisciplinary Applications

A time local subset feature selection for prediction of sudden cardiac death from ECG signal

Elias Ebrahimzadeh et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2018)

Article Engineering, Biomedical

Early detection of sudden cardiac death using nonlinear analysis of heart rate variability

Mohammad Khazaei et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2018)

Article Cardiac & Cardiovascular Systems

Prospective Countywide Surveillance and Autopsy Characterization of Sudden Cardiac Death POST SCD Study

Zian H. Tseng et al.

CIRCULATION (2018)

Article Computer Science, Artificial Intelligence

Early detection of sudden cardiac death using Poincare plots and recurrence plot-based features from HRV signals

Vahid Houshyarifar et al.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES (2017)

Article Medicine, General & Internal

A guide to systematic review and meta-analysis of prediction model performance

Thomas P. A. Debray et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Article Medicine, General & Internal

A guide to systematic review and meta-analysis of prediction model performance

Thomas P. A. Debray et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Article Urology & Nephrology

Medical big data: promise and challenges

Choong Ho Lee et al.

KIDNEY RESEARCH AND CLINICAL PRACTICE (2017)

Article Computer Science, Artificial Intelligence

Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index

Hamida Fujita et al.

APPLIED SOFT COMPUTING (2016)

Article Engineering, Biomedical

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal

Vahid Houshyarifar et al.

BIO-MEDICAL MATERIALS AND ENGINEERING (2016)

Editorial Material Cardiac & Cardiovascular Systems

Powerlessness of a Number: Why Left Ventricular Ejection Fraction Matters Less for Sudden Cardiac Death Risk Assessment

Katherine C. Wu et al.

CIRCULATION-CARDIOVASCULAR IMAGING (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

SVM classification of CWT signal features for predicting sudden cardiac death

Towfeeq Fairooi et al.

BIOMEDICAL PHYSICS & ENGINEERING EXPRESS (2016)

Review Physiology

Mechanisms of Ventricular Arrhythmias: From Molecular Fluctuations to Electrical Turbulence

Zhilin Qu et al.

ANNUAL REVIEW OF PHYSIOLOGY, VOL 77 (2015)

Article Cardiac & Cardiovascular Systems

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers

Julia Ramirez et al.

JOURNAL OF ELECTROCARDIOLOGY (2015)

Article Computer Science, Artificial Intelligence

An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

U. Rajendra Acharya et al.

KNOWLEDGE-BASED SYSTEMS (2015)

Review Cardiac & Cardiovascular Systems

Risk stratification for sudden cardiac death: current status and challenges for the future

Hein J. J. Wellens et al.

EUROPEAN HEART JOURNAL (2014)

Article Cardiac & Cardiovascular Systems

Systematic Review of the Incidence of Sudden Cardiac Death in the United States

Melissa H. Kong et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2011)

Review Medicine, General & Internal

Systematic Reviews of Diagnostic Test Accuracy

Mariska M. G. Leeflang et al.

ANNALS OF INTERNAL MEDICINE (2008)

Article Health Care Sciences & Services

Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews

JB Reitsma et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2005)

Article Medicine, General & Internal

Accuracy of Noninvasive Ejection Fraction Measurement in a Large Community-Based Clinic

Dana E. Habash-Bseiso et al.

CLINICAL MEDICINE & RESEARCH (2005)