AIMC Topic: Electrocardiography

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Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks.

IEEE transactions on neural networks and learning systems
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monit...

Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals.

BMC medical informatics and decision making
BACKGROUND: Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances in the heartbeat caused by electrical conduction anomalies in cardiac muscle. Clinically, ECG machines are utilized to diagnose and monitor cardiac arrhythmia n...

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

Cardiovascular engineering and technology
PROPOSE: An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a rest...

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment.

Sensors (Basel, Switzerland)
In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive me...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Computers in biology and medicine
Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillat...

Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment.

Journal of the American Heart Association
BACKGROUND: Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform ...

SelANet: decision-assisting selective sleep apnea detection based on confidence score.

BMC medical informatics and decision making
BACKGROUND: One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to devel...

As artificial intelligence goes multimodal, medical applications multiply.

Science (New York, N.Y.)
Machines don't have eyes, but you wouldn't know that if you followed the progression of deep learning models for accurate interpretation of medical images, such as x-rays, computed tomography (CT) and magnetic resonance imaging (MRI) scans, pathology...

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity.

Scientific reports
Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus disease 2019 (COVID-19) severity prediction. Between March 2020 and June 2022, we enrolled 1453 COVID-19 p...

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in lit...