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A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals
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Haleem, Muhammad Salman, Cisuelo, Owain, Andellini, Martina, Castaldo, Rossana, Angelini, Massimiliano, Ritrovato, Matteo, Schiaffini, Riccardo, Franzese, Monica and Pecchia, Leandro (2024) A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals. Biomedical Signal Processing and Control, 92 . 106065. doi:10.1016/j.bspc.2024.106065 ISSN 1746-8094.
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Official URL: http://doi.org/10.1016/j.bspc.2024.106065
Abstract
With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management.
In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Journal or Publication Title: | Biomedical Signal Processing and Control | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 1746-8094 | ||||||||
Official Date: | June 2024 | ||||||||
Dates: |
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Volume: | 92 | ||||||||
Article Number: | 106065 | ||||||||
DOI: | 10.1016/j.bspc.2024.106065 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 19 March 2024 | ||||||||
Date of first compliant Open Access: | 19 March 2024 |
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