Arrhythmias

Temporal and spatial self supervised learning methods for electrocardiograms.

The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, which limits their effectiveness. To address these limitations and provide novel insights, we propose a Temporal-Spatial Self-Supervised Learning (TSS...

Antifreezing Ultrathin Bioionic Gel-Based Wearable System for Artificial Intelligence-Assisted Arrhythmia Diagnosis in Hypothermia.

Cardiovascular disease (CAD) is a major global public health issue, with mortality rates being significantly impacted by cold temperatures. Stable and reliable electrocardiogram (ECG) monitoring in cold environments is crucial for early detection and treatment of CAD. However, existing skin sensor struggle to balance freeze resistance, breathability, flexibility, conductivity and adhesion at cold ...

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.

To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore t...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conv...

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.

Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for...

Transformer-based heart language model with electrocardiogram annotations.

This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, a...

An arrhythmia classification using a deep learning and optimisation-based methodology.

The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transfo...

Active learning and margin strategies for arrhythmia classification in implantable devices.

BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new...

Spherical lesion formation in HIFU using robotic assistance for controlled focal point manipulation.

We propose a robot-assisted method to generate spherical thermal lesions by high-intensity focused ultrasound (HIFU) ablation. Typically, HIFU-induced...

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartb...

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques a...

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity and mortality. Despite advancements in ablation t...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring.

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunatel...

AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the diagnostic process for cardiologists. The proposed...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human tech...

Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.

BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effec...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the st...

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may re...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. Howeve...

CaBind_MCNN: Identifying Potential Calcium Channel Blocker Targets by Predicting Calcium-Binding Sites in Ion Channels and Ion Transporters Using Protein Language Models and Multiscale Feature Extraction.

Calcium ions (Ca) are crucial for various physiological processes, including neurotransmission and cardiac function. Dysregulation of Ca homeostasis c...