Arrhythmias

A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis

Cardiovascular diseases remain the leading cause of global mortality, emphasizing the critical need for efficient diagnostic tools such as electrocardiograms (ECGs). Recent advancements in deep learning, particularly transformers, have revolutionized ECG analysis by capturing detailed waveform features as well as global rhythm patterns. However, traditional transformers struggle to effectively c...

Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels

The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerful and non-invasive nature. One of the most critical usages is to determine whether more detailed examinations are necessary, with users ranging across various levels of expertise. Given this diversity in expertise, it is essential to assist users to avoid critical errors. Recent studies in machine l...

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of tra...

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal activity in the brain, which is often associated ...

ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation

Camouflaged object segmentation presents unique challenges compared to traditional segmentation tasks, primarily due to the high similarity in patte...

A Systematic Literature Review of Unmanned Aerial Vehicles for Healthcare and Emergency Services

Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in various fields. As UAVs become more common across mu...

Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors

Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents...

Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller

The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the treatment and diagnosis of cardiovascular diseases (CV...

Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, w...

Susceptibility of Large Language Models to User-Driven Factors in Medical Queries

Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as questio...

The Role of Computational Modeling in Enhancing Thermal Safety During Cardiac Ablation

Objective: In this review, we aim to provide an analysis of current cardiac ablation techniques, such as radiofrequency ablation (RF), cryoablation,...

Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB

A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially powerline interference at 50 Hz or 60 Hz. This interf...

Enhanced ECG Arrhythmia Detection Accuracy by Optimizing Divergence-Based Data Fusion

AI computation in healthcare faces significant challenges when clinical datasets are limited and heterogeneous. Integrating datasets from multiple s...

Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach

False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patie...

Predicting Cardiopulmonary Exercise Testing Outcomes in Congenital Heart Disease Through Multi-modal Data Integration and Geometric Learning

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables includin...

BioMamba: Leveraging Spectro-Temporal Embedding in Bidirectional Mamba for Enhanced Biosignal Classification

Biological signals, such as electroencephalograms (EEGs) and electrocardiograms (ECGs), play a pivotal role in numerous clinical practices, such as ...

Neuro-Informed Adaptive Learning (NIAL) Algorithm: A Hybrid Deep Learning Approach for ECG Signal Classification

The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early diagnosis and intervention in cardiovascular disea...

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. Unfortunat...

TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis

Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagnosis, health monitoring, and biometric authenticati...

Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiograms

Electrocardiography (ECG) is an essential tool for diagnosing multiple cardiac anomalies: it provides valuable clinical insights, while being afford...