AIMC Topic: Electrocardiography

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SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network.

Computers in biology and medicine
Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these chal...

Artificial Intelligence for Identification of Patients with Increased Risk of Severe Cancer Therapy-Related Cardiac Dysfunction Following Anthracycline Therapy.

The American journal of medicine
BACKGROUND: Early detection of cancer therapy-related cardiac dysfunction (CTRCD) after anthracycline exposure is critically important in minimizing morbidity and mortality. Artificial intelligence models applied to electrocardiograms (ECG-AI) may al...

Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification.

Computers in biology and medicine
Cardiac arrhythmias, characterized by irregular heart function, disrupt normal blood circulation and are commonly detected using electrocardiograms (ECGs). ECG is widely preferred due to its cost-effectiveness, ease of application, and high reliabili...

Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.

American journal of human genetics
Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to multimodal health data provides a unique opportunity for genetic studies of complex traits because diffe...

Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study.

Computer methods and programs in biomedicine
BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise class...

Deep Learning for Cardiac Overload Estimation - Predicting B-Type Natriuretic Peptide (BNP) Levels From Heart Sounds and Electrocardiogram.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro-BNP) are key biomarkers used for heart failure (HF) management. Although traditional auscultation lacks objective evaluation, the SSS01-series phonocardiogram enables rapid r...

Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach.

Computers in biology and medicine
Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable de...

Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.

PloS one
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
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
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...