Arrhythmias

Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks.

GOAL: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system.

An Automatic Subject-Adaptable Heartbeat Classifier Based on Multiview Learning.

In this paper, a novel subject-adaptable heartbeat classification model is presented, in order to address the significant interperson variations in ECG signals. A multiview learning approach is proposed to automate subject adaptation using a small amount of unlabeled personal data, without requiring manual labeling. The designed subject-customized models consist of two models, namely, general clas...

Impact of catheter ablation with remote magnetic navigation on procedural outcomes in patients with persistent and long-standing persistent atrial fibrillation.

BACKGROUND: The objectives of this study were to assess the procedural outcomes of persistent and long-standing persistent atrial fibrillation (PsAF a...

Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.

Premature ventricular contraction (PVC) is a common type of abnormal heartbeat. Without early diagnosis and proper treatment, PVC may result in seriou...

ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is t...

Robotic ablation of atrial fibrillation.

BACKGROUND: Pulmonary vein isolation (PVI) is an established treatment for atrial fibrillation (AF). During PVI an electrical conduction block between...

Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators.

Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the ana...

A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG.

GOAL: This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG.

Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and...

Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters.

Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue ...

Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes.

Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its rec...

Robotic-assisted thermal ablation of liver tumours.

OBJECTIVE: This study aimed to assess the technical success, radiation dose, safety and performance level of liver thermal ablation using a computed t...

An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry.

The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing. This study introduces a device annotation method...

Composite Artificial Intelligence-Enabled Electrocardiogram for Detection and Prediction of Structural Heart Disease

Background Structural heart disease (SHD) drives heart failure and cardiovascular mortality but remains underdiagnosed, and echocardiography is limite...

Weakly Supervised Pathology-Informed Representation Learning for PET-Based Content Retrieval of Intra-Tumour Heterogeneity

We propose a weakly supervised 18FFDG PET representation-learning framework for content based medical image retrieval, using H&E derived information d...

Knowledge-Guided Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Label-Conditioned Contrastive Alignment

Adult and pediatric electrocardiogram (ECG) interpretation relies on age-sensitive criteria, and models pretrained mainly on adult ECGs often transfer...

Scaling ECG Foundation Models and Identifying a Threshold for Effective Representation Learning

We conducted a scaling evaluation of unlabeled pretraining for electrocardiogram foundation model performance. One-dimensional vision transformer mask...

Angular Gaussian Supervised Contrastive Learning for Long-Tailed Electrocardiogram Arrhythmia Diagnosis

Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) arrhythmia diagnosis, particularly for clinically ...

Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices

Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective d...

Artificial intelligence-based ECG reconstruction error as a continuous predictor of all-cause mortality: a multi-cohort retrospective validation study

Background Recent artificial intelligence (AI) models applied to the electrocardiogram (ECG) for risk stratification typically rely on supervised lear...