AIMC Topic: Electrocardiography
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A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX).
Scientific reports
Jul 2, 2025
Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiograms (ECGs). Traditional explanation methods focus on highlighting important ECG segments but often fai...
Machine learning evaluation model of pilot workload in a low-visibility environment.
Scientific reports
Jul 1, 2025
To analyze the variation trend of pilots' workload in a low-visibility flight environment and then put forward a scientific evaluation method, this study set up an experimental platform using an E01-pro simulated flight platform and a PhysioPlux mult...
DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.
Scientific reports
Jul 1, 2025
Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely intervention in cardiac disorders. Existing models, such as CNNs and LSTMs, often struggle with long-range dependencies, generalization across multiple ...
Analyzing mental stress in Indian students through advanced machine learning and wearable technologies.
Scientific reports
Jul 1, 2025
Mental stress is a prevalent issue in modern society, and detecting and classifying it accurately is crucial for effective interventions and treatment plans. This study aims to compare various machine learning (ML) algorithms for detecting mental str...
Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.
PloS one
Jun 17, 2025
Arrhythmia is a prevalent cardiac disorder that can lead to severe complications such as stroke and cardiac arrest. While deep learning has advanced automated ECG analysis, challenges remain in accurately classifying arrhythmias due to signal variabi...
A multimodal dataset for coronary microvascular disease biomarker discovery.
Scientific data
Jun 12, 2025
Coronary microvascular disease (CMD), particularly prevalent among women, is associated with increased morbidity and mortality, making clinical screening vital for effective management. However, limited publicly available screening-level data hinders...
Artificial intelligence-assisted diagnosis and prognostication in low ejection fraction using electrocardiograms in inpatient department: a pragmatic randomized controlled trial.
BMC medicine
Jun 9, 2025
BACKGROUND: Early diagnosis of low ejection fraction (EF) remains challenging despite being a treatable condition. This study aimed to evaluate the effectiveness of an electrocardiogram (ECG)-based artificial intelligence (AI)-assisted clinical decis...
Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.
BMJ health & care informatics
Jun 6, 2025
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the EC...
Brugada syndrome.
Nature reviews. Disease primers
May 29, 2025
Brugada syndrome (BrS) is a cardiac channelopathy associated with an elevated risk of arrhythmias and sudden cardiac death compared with the general population. Since its initial description in 1992 by Pedro and Josep Brugada, there has been tremendo...
A pediatric ECG database with disease diagnosis covering 11643 children.
Scientific data
May 26, 2025
Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to achieve intelligent diagnosis of ECG. The existing ECG datasets almost only focus on adults and most ...
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