AIMC Topic: Electrocardiography

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Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

Computer methods and programs in biomedicine
OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convolutional networks in previous studies, and to improve the performance of arrhythmia classification.

Evolution of single-lead ECG for STEMI detection using a deep learning approach.

International journal of cardiology
BACKGROUND: While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis.

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Biosensors
Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of...

Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a m...

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

Scientific reports
Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent re...

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

Circulation
BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predi...

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Medical & biological engineering & computing
Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple f...

Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence.

Journal of electrocardiology
BACKGROUND: Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnost...

Review of Deep Learning-Based Atrial Fibrillation Detection Studies.

International journal of environmental research and public health
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diag...

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic redundancy exists in the 12-lead ECG, which could impose a systematic overfitting on DL, causing poo...