Papers Arrhythmia Detection
“Arrhythmia Detection” 태그가 달린 논문 103편 · 필터 해제
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 detection and diagnosis. However, current wearable monitoring devices a…
Arrhythmia DetectionSL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data. While …
Self-Supervised LearningDomain GeneralizationArrhythmia DetectionContrastive LearningArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized …
Arrhythmia DetectionHeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework comb…
Domain GeneralizationArrhythmia DetectionECG ClassificationDeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems treat beats as isolated local instances. This is limiting because beat l…
Arrhythmia DetectionDecision MakingTowards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
Cardiovascular disease remains the leading cause of death worldwide, and early detection of arrhythmias through continuous ECG monitoring on wearable devices can prevent life-threatening events. Federated Learning (FL) e…
Arrhythmia DetectionFederated LearningSampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the specific consequences of these discrepanci…
Atrial Fibrillation DetectionArrhythmia DetectionDeriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG
Photoplethysmography (PPG) is one of the most widely captured biosignals for clinical prediction tasks, yet PPG-based algorithms are typically trained on small-scale datasets of uncertain quality, which hinders meaningfu…
Arrhythmia DetectionUCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper proposes UCTECG-Net, an uncertainty-awa…
Arrhythmia DetectionDeep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation
With the rising prevalence of cardiovascular diseases, electrocardiograms (ECG) remain essential for the non-invasive detection of cardiac abnormalities. This study presents a comprehensive evaluation of deep neural netw…
Arrhythmia DetectionOptimized Hybrid Feature Engineering for Resource-Efficient Arrhythmia Detection in ECG Signals: An Optimization Framework
Cardiovascular diseases, particularly arrhythmias, remain a leading global cause of mortality, necessitating continuous monitoring via the Internet of Medical Things (IoMT). However, state-of-the-art deep learning approa…
Arrhythmia DetectionFeature EngineeringA novel approach to classification of ECG arrhythmia types with latent ODEs
12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but s…
Arrhythmia DetectionInterpretable temporal fusion network of multi- and multi-class arrhythmia classification
Clinical decision support systems (CDSSs) have been widely utilized to support the decisions made by cardiologists when detecting and classifying arrhythmia from electrocardiograms. However, forming a CDSS for the arrhyt…
Arrhythmia DetectionH-Infinity Filter Enhanced CNN-LSTM for Arrhythmia Detection from Heart Sound Recordings
Early detection of heart arrhythmia can prevent severe future complications in cardiac patients. While manual diagnosis still remains the clinical standard, it relies heavily on visual interpretation and is inherently su…
Arrhythmia DetectionSpeech Foundation Models Generalize to Time Series Tasks from Wearable Sensor Data
Both speech and sensor time series data encode information in both the time- and frequency- domains, like spectral powers and waveform shapelets. We show that speech foundation models learn representations that generaliz…
Arrhythmia DetectionExplainable AI (XAI) for Arrhythmia detection from electrocardiograms
Advancements in deep learning have enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption. This study investigates the appli…
Arrhythmia DetectionMasked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
Background: Electrocardiograms are indispensable for diagnosing cardiovascular diseases, yet in many settings they exist only as paper printouts stored in multiple recording layouts. Converting these images into digital …
Atrial Fibrillation DetectionArrhythmia DetectionIntegrating 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 interference overlaps with vital ECG signal features, affecting the accuracy o…
Arrhythmia DetectionClassificationECG ClassificationEnhanced 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 sources and different equipments is critical for effective AI computation…
Arrhythmia DetectionBinary ClassificationDensity EstimationConvolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification
Machine learning has revolutionized biomedical signal analysis, particularly in electrocardiogram (ECG) classification. While convolutional neural networks (CNNs) excel at automatic feature extraction, the optimal integr…
Arrhythmia DetectionClassificationdomain classificationECG Classification