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Spectrogram-driven convolutional neural network for real-time non-invasive hyperglycaemia detection in paediatric Type-1 diabetes via wearable sensors
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Cisuelo, Owain, Haleem, Muhammad Salman, Hattersley, John and Pecchia, Leandro (2024) Spectrogram-driven convolutional neural network for real-time non-invasive hyperglycaemia detection in paediatric Type-1 diabetes via wearable sensors. In: Badnjević, A. and Gurbeta Pokvić, L., (eds.) MEDICON’23 and CMBEBIH’23. IFMBE Proceedings, 94 . Cham: Springer, pp. 376-386. ISBN 9783031490675
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Official URL: http://dx.doi.org/10.1007/978-3-031-49068-2_39
Abstract
Real-time detection of glycaemic events is crucial in the effective management of type 1 diabetes, particularly in paediatric patients. Recent advances in wearable sensors and machine learning have allowed for the inference of glycaemic events based on non-invasive physiological signals such as electrocardiogram (ECG). However, existing approaches have limitations due to the limited number of ECG features analysed and their applicability to real-life conditions. To overcome these limitations, we propose a spectrogram-driven deep learning methodology for real-time glycaemic event detection. We calculated beat-level spectrograms using Short Time Fourier Transform (STFT) on ECG beats extracted from continuous signals using our deep learning ECG segmentation tool. Subject-specific multi-layer 2D convolutional neural networks were trained on the spectrograms. We evaluated our methodology on an original dataset comprising continuous ECG and interstitial glucose data collected from children with type-1 diabetes over several days in real-life conditions. Our approach achieved an average personalised hyperglycaemia detection accuracy of 96.9%.
Item Type: | Book Item | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||
Series Name: | IFMBE Proceedings | ||||
Publisher: | Springer | ||||
Place of Publication: | Cham | ||||
ISBN: | 9783031490675 | ||||
ISSN: | 1680-0737 | ||||
Book Title: | MEDICON’23 and CMBEBIH’23 | ||||
Editor: | Badnjević, A. and Gurbeta Pokvić, L. | ||||
Official Date: | 4 January 2024 | ||||
Dates: |
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Volume: | 94 | ||||
Page Range: | pp. 376-386 | ||||
DOI: | 10.1007/978-3-031-49068-2_39 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published |
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