AIMC Topic: Electrocardiography

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On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.

IEEE transactions on bio-medical engineering
OBJECTIVE: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical fea...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

IEEE transactions on cybernetics
Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, th...

CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography.

PloS one
Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and morbidity, and an accurate diagnosis at an early stage of heart change is clinically significant. Electrocardiography is the most convenient, economical...

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BMC cardiovascular disorders
BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). ...

Transformer-based temporal sequence learners for arrhythmia classification.

Medical & biological engineering & computing
An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features, and more recently, deep learning methods use convolution and recursive structures to classify heart si...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

Circulation. Genomic and precision medicine
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well u...

Erroneous electrocardiographic interpretations and its clinical implications.

Journal of cardiovascular electrophysiology
INTRODUCTION: The advancement of artificial intelligence (AI) has aided clinicians in the interpretation of electrocardiograms (ECGs) serving as an essential tool to provide rapid triage and care. However, in some cases, AI can misinterpret an ECG an...

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.

Sensors (Basel, Switzerland)
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performanc...

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching.

Sensors (Basel, Switzerland)
Cardiac monitoring can be performed by means of an accelerometer attached to a subject's chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardi...

In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

IEEE transactions on biomedical circuits and systems
This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog ...