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Arrhythmias, Cardiac

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Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The automatic arrhythmia classification system has made a significant contribution to reducing the mortality rate of cardiovascular diseases. Although the current deep-learning-based models have achieved ideal effects in arrhythmia classification, th...

An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease (CVD) is a serial of diseases with global leading causes of death. Electrocardiogram (ECG) is the most commonly used basis for CVD diagnosis due to its low cost and no injury. Due to the great performance shown in classificatio...

More than meets the eye: Using AI to identify reduced heart function by electrocardiograms.

Med (New York, N.Y.)
Electrocardiographic (ECG) assessment of patients with suspected heart disease is a bedrock of cardiology for diagnosing conduction system disease, arrhythmias, and heart attack. Now, using AI-assisted interpretation of ECGs, the signals within these...

Deformable US/CT Image Registration with a Convolutional Neural Network for Cardiac Arrhythmia Therapy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Image registration represents one of the fundamental techniques in medical imaging and image-guided interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for t...

Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrhythmias classification models with high accuracy using artificial neural networks. To solve this problem, we propose a novel data augmentation method ...

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When perfo...

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in tur...

Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Discovery medicine
Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, problems with c...