Transformers in biosignal analysis: A review
Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …
Through outstanding performance in natural language processing and superior capability to …
A review of arrhythmia detection based on electrocardiogram with artificial intelligence
Introduction With the widespread availability of portable electrocardiogram (ECG) devices,
there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia …
there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia …
An end-to-end deep learning framework for real-time denoising of heart sounds for cardiac disease detection in unseen noise
The heart sound signals captured via a digital stethoscope are often distorted by
environmental and physiological noise, altering their salient and critical properties. The …
environmental and physiological noise, altering their salient and critical properties. The …
Pilot stress detection through physiological signals using a transformer-based deep learning model
Y Li, K Li, J Chen, S Wang, H Lu… - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Pilot stress detection is a challenging task and it plays a vital role in improving flight
performance and avoiding catastrophic accidents. Many deep learning models have been …
performance and avoiding catastrophic accidents. Many deep learning models have been …
Trahgr: Transformer for hand gesture recognition via electromyography
S Zabihi, E Rahimian, A Asif… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG)
signals have recently shown considerable potential for development of advanced …
signals have recently shown considerable potential for development of advanced …
[HTML][HTML] DCETEN: A lightweight ECG automatic classification network based on Transformer model
F Jiang, J Xiao, L Liu, C Wang - Digital Communications and Networks, 2024 - Elsevier
Abstract Currently, Cardiovascular Disease (CVD) remains a significant contributor to
premature mortality and escalating health care expenses. Early and accurate detection is …
premature mortality and escalating health care expenses. Early and accurate detection is …
HCTNet: An experience-guided deep learning network for inter-patient arrhythmia classification on imbalanced dataset
C Han, P Wang, R Huang, L Cui - Biomedical Signal Processing and …, 2022 - Elsevier
The automatic diagnosis of arrhythmia using machine learning has been a hot topic and
extensively researched recently. A common problem is class imbalance that could make the …
extensively researched recently. A common problem is class imbalance that could make the …
MS-MLP: Multi-scale sampling MLP for ECG classification
W Wang, J Guan, X Che… - 2022 30th European Signal …, 2022 - ieeexplore.ieee.org
Transformer-based models (ie, Fusing-Tf and LDTF) have achieved state-of-the-art
performance for electro-cardiogram (ECG) classification. However, these models may suffer …
performance for electro-cardiogram (ECG) classification. However, these models may suffer …
A deep learning framework for ECG denoising and classification
H Peng, X Chang, Z Yao, D Shi, Y Chen - Biomedical Signal Processing …, 2024 - Elsevier
Cardiovascular disease (CVD) is a major cause of mortality worldwide. To facilitate early
prevention and timely diagnosis of CVD, daily electrocardiogram (ECG) monitoring has …
prevention and timely diagnosis of CVD, daily electrocardiogram (ECG) monitoring has …
Heartbeat classification method combining multi-branch convolutional neural networks and transformer
F Zhou, J Wang - Iscience, 2024 - cell.com
The detection and classification of arrhythmias are crucial steps in diagnosing
cardiovascular diseases. However, current deep learning-based classification methods …
cardiovascular diseases. However, current deep learning-based classification methods …