[HTML][HTML] A deep multi-task learning approach for bioelectrical signal analysis
Deep learning is a promising technique for bioelectrical signal analysis, as it can
automatically discover hidden features from raw data without substantial domain knowledge …
automatically discover hidden features from raw data without substantial domain knowledge …
Deep learning-based multi-channel bio-electrical signal processing: Algorithms, models, and applications
Q Li - 2023 - search.proquest.com
Bio-electrical signals are electrical potentials generated by living beings. These signals
carry important information about various physiological processes in the body. For instance …
carry important information about various physiological processes in the body. For instance …
A deep multi-task learning approach for ECG data analysis
Deep learning is an advanced representation learning method and can automatically
discover hidden features from raw data. Researchers have attempted to adopt it for ECG …
discover hidden features from raw data. Researchers have attempted to adopt it for ECG …
Cross-database generalization of deep learning models for arrhythmia classification
E Merdjanovska, A Rashkovska - 2021 44th International …, 2021 - ieeexplore.ieee.org
Arrhythmias are a wide-spread group of heart abnormalities. In the area of computational
methods for ECG analysis, much research has been done on automated arrhythmia …
methods for ECG analysis, much research has been done on automated arrhythmia …
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing
W Hao, K Jingsu - arXiv preprint arXiv:2204.04420, 2022 - arxiv.org
In recent years, deep learning has witnessed its blossom in the field of Electrocardiography
(ECG) processing, outperforming traditional signal processing methods in various tasks, for …
(ECG) processing, outperforming traditional signal processing methods in various tasks, for …
Diagnosis of arrhythmia based on multi-scale feature fusion and imbalanced data
Z Cheng, Z Liu, G Yang - Proceedings of the 2022 7th International …, 2022 - dl.acm.org
Evidence suggests that Electrocardiogram (ECG) analysis plays an important role in the
diagnosis of arrhythmia and the prevention of cardiovascular diseases. Extracting disease …
diagnosis of arrhythmia and the prevention of cardiovascular diseases. Extracting disease …
Detecting heart arrhythmias using deep learning algorithms
An electrocardiogram measures the electrical activity of the heart and has been widely used
for detecting heart diseases due to its simplicity and non‐invasive nature. It is possible to …
for detecting heart diseases due to its simplicity and non‐invasive nature. It is possible to …
Arrhythmias classification by integrating stacked bidirectional LSTM and two-dimensional CNN
Classifying different types of arrhythmias based on ECG signal is an important research
topic in healthcare. Traditional methods focus on extracting varieties of features from ECG …
topic in healthcare. Traditional methods focus on extracting varieties of features from ECG …
[HTML][HTML] Improving the efficacy of deep-learning models for heart beat detection on heterogeneous datasets
Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to
extract physiological markers. However, the efficacy and applicability of the results proposed …
extract physiological markers. However, the efficacy and applicability of the results proposed …
[HTML][HTML] Arrhythmia classification and diagnosis based on ECG signal: A multi-domain collaborative analysis and decision approach
H Ruan, X Dai, S Chen, X Qiu - Electronics, 2022 - mdpi.com
Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will
pose a great threat to human health. As an effective feature extraction method, deep …
pose a great threat to human health. As an effective feature extraction method, deep …