Arrhythmias

Towards federated transfer learning in electrocardiogram signal analysis.

Modern methods in artificial intelligence perform very well on many healthcare datasets, at times outperforming trained doctors. However, many assumptions made in model training are not justifiable in clinical settings. In this work, we propose a method to train classifiers for electrocardiograms, able to deal with data of disparate input dimensions, distributed across different institutions, and ...

Long-term results of ablation index guided atrial fibrillation ablation: insights after 5+ years of follow-up from the MPH AF Ablation Registry.

BACKGROUND: Catheter ablation (CA) for symptomatic atrial fibrillation (AF) offers the best outcomes for patients. Despite the benefits of CA, a significant proportion of patients suffer a recurrence; hence, there is scope to potentially improve outcomes through technical innovations such as ablation index (AI) guidance during AF ablation. We present real-world 5-year follow-up data of AI-guided p...

Heart failure classification using deep learning to extract spatiotemporal features from ECG.

BACKGROUND: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major med...

Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.

This paper reviews the literature on assessing electrical dyssynchrony for patient selection in cardiac resynchronization therapy (CRT). The guideline...

Model-based estimation of AV-nodal refractory period and conduction delay trends from ECG.

Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricul...

Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis.

Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The ap...

Advancing Cardiovascular Risk Assessment with Artificial Intelligence: Opportunities and Implications in North Carolina.

Cardiovascular disease mortality is increasing in North Carolina with persistent inequality by race, income, and location. Artificial intelligence (AI...

ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and dete...

Multichannel high noise level ECG denoising based on adversarial deep learning.

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram...

Survival Analysis for Multimode Ablation Using Self-Adapted Deep Learning Network Based on Multisource Features.

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with ...

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of card...

Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm.

BACKGROUND: Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are pr...

Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias.

BACKGROUND: T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess...

Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.

The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardiovascular health, yet traditional ECG analysis can...

Validation of an automated artificial intelligence system for 12‑lead ECG interpretation.

BACKGROUND: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first ...

Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation.

OBJECTIVE: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhy...

Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data.

Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive ...

SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection.

Accurate detection of electrocardiogram (ECG) waveforms is crucial for computer-aided diagnosis of cardiac abnormalities. This study introduces SEResU...

Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach.

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting i...

Personalized ECG monitoring and adaptive machine learning.

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and t...