Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review

Y Hagiwara, H Fujita, SL Oh, JH Tan, R San Tan… - Information …, 2018 - Elsevier
Arrhythmia is a type of disorder that affects the pattern and rate of the heartbeat. Among the
various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated …

Smart wearables for cardiac monitoring—real-world use beyond atrial fibrillation

D Duncker, WY Ding, S Etheridge, PA Noseworthy… - Sensors, 2021 - mdpi.com
The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation
are increasing continuously. This review focuses on the real-world use and evolution of …

Passive detection of atrial fibrillation using a commercially available smartwatch

GH Tison, JM Sanchez, B Ballinger, A Singh… - JAMA …, 2018 - jamanetwork.com
Importance Atrial fibrillation (AF) affects 34 million people worldwide and is a leading cause
of stroke. A readily accessible means to continuously monitor for AF could prevent large …

A deep learning approach for real-time detection of atrial fibrillation

RS Andersen, A Peimankar… - Expert Systems with …, 2019 - Elsevier
Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation
(AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An …

Detecting atrial fibrillation by deep convolutional neural networks

Y Xia, N Wulan, K Wang, H Zhang - Computers in biology and medicine, 2018 - Elsevier
Background Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of
AF increases with age, causing high risks of stroke and increased morbidity and mortality …

Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings

X Fan, Q Yao, Y Cai, F Miao, F Sun… - IEEE journal of …, 2018 - ieeexplore.ieee.org
Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in
elderly population, associated with a high mortality and morbidity in stroke, heart failure …

Physiological parameter monitoring from optical recordings with a mobile phone

CG Scully, J Lee, J Meyer, AM Gorbach… - IEEE Transactions …, 2011 - ieeexplore.ieee.org
We show that a mobile phone can serve as an accurate monitor for several physiological
variables, based on its ability to record and analyze the varying color signals of a fingertip …

Subject-aware contrastive learning for biosignals

JY Cheng, H Goh, K Dogrusoz, O Tuzel… - arXiv preprint arXiv …, 2020 - arxiv.org
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG),
often have noisy labels and have limited number of subjects (< 100). To handle these …

Nonlinear methods most applied to heart-rate time series: a review

T Henriques, M Ribeiro, A Teixeira, L Castro, L Antunes… - Entropy, 2020 - mdpi.com
The heart-rate dynamics are one of the most analyzed physiological interactions. Many
mathematical methods were proposed to evaluate heart-rate variability. These methods …

Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine

S Asgari, A Mehrnia, M Moussavi - Computers in biology and medicine, 2015 - Elsevier
Background Atrial fibrillation (AF) is the most common cardiac arrhythmia, and a major
public health burden associated with significant morbidity and mortality. Automatic detection …