Death, Sudden, Cardiac

Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.

Biotechnology & genetic engineering reviews May 14, 2023

This study aimed to evaluate the potential of deep learning applied to the measurement of echocardiographic data in patients with sudden cardiac death (SCD). 320 SCD patients who met the inclusion and exclusion criteria underwent clinical evaluation,...

Death, Sudden, Cardiac Middle Aged Deep Learning ROC Curve Humans Female Risk Factors Male Echocardiography Aged Adult

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Correlation analysis of deep learning methods in S-ICD screening.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc Mar 15, 2023

BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of EC...

Retrospective Studies Female Aged Male Humans Heart Defibrillators, Implantable Death, Sudden, Cardiac Middle Aged Deep Learning Electrocardiography

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Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc Dec 16, 2022

INTRODUCTION: S-ICD eligibility is assessed at pre-implant screening where surface ECG traces are used as surrogates for S-ICD vectors. In heart failure (HF) patients undergoing diuresis, electrolytes and fluid shifts can cause changes in R and T wav...

Electrocardiography Heart Failure Arrhythmias, Cardiac Male Middle Aged Deep Learning Defibrillators, Implantable Death, Sudden, Cardiac Humans Aged Adult Prospective Studies Female

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Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.

Circulation journal : official journal of the Japanese Circulation Society Nov 12, 2022

BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model c...

Death, Sudden, Cardiac Brugada Syndrome Retrospective Studies Electrocardiography Humans Ventricular Fibrillation Artificial Intelligence

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Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Sensors (Basel, Switzerland) Jul 20, 2022

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to es...

Humans Heart Death, Sudden, Cardiac Arrhythmias, Cardiac Deep Learning Electrocardiography

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Update on risk factors and biomarkers of sudden unexplained cardiac death.

Journal of forensic and legal medicine Mar 6, 2022

Sudden cardiac death (SCD) accounts for approximately 15%-20% of all deaths worldwide, the causes of which are mainly structural heart diseases. However, SCD also occurs in patients without major cardiac structural abnormalities due to electrophysiol...

Arrhythmias, Cardiac Death, Sudden, Cardiac Humans Artificial Intelligence Biomarkers Risk Factors

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Deep learning methods for screening patients' S-ICD implantation eligibility.

Artificial intelligence in medicine Aug 9, 2021

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A m...

Electrocardiography Mass Screening Humans Deep Learning Arrhythmias, Cardiac Defibrillators, Implantable Death, Sudden, Cardiac

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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circulation research Nov 10, 2020

RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.

Ventricular Fibrillation Death, Sudden, Cardiac Electrophysiologic Techniques, Cardiac Female Myocardial Infarction Risk Factors Tachycardia, Ventricular Aged Humans Cardiomyopathies Action Potentials Phenotype Neural Networks, Computer Risk Assessment Prospective Studies Time Factors Aged, 80 and over Predictive Value of Tests Diagnosis, Computer-Assisted Support Vector Machine Male Prognosis Signal Processing, Computer-Assisted Middle Aged

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Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices.

Sensors (Basel, Switzerland) Sep 25, 2020

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to ide...

Middle Aged Male Heart Rate Humans Electrocardiography Ventricular Function, Left Death, Sudden, Cardiac Support Vector Machine Stroke Volume Female Aged

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[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Aug 25, 2024

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early ...

Humans Neural Networks, Computer Algorithms Signal Processing, Computer-Assisted Arrhythmias, Cardiac Sensitivity and Specificity Deep Learning Heart Rate Electrocardiography Death, Sudden, Cardiac

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Explore the latest research on artificial intelligence and machine learning in medicine.

Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.

Correlation analysis of deep learning methods in S-ICD screening.

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure.

Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Update on risk factors and biomarkers of sudden unexplained cardiac death.

Deep learning methods for screening patients' S-ICD implantation eligibility.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices.

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

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