Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders

Hypoglycemia is a condition caused by low blood glucose levels, mainly affecting people with diabetes. Low levels of blood glucose can be dangerous, causing multiple complications over time. Hypoglycemia affects the repolarization characteristics of the heart. This study presents a personalized, dee...

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Vydáno v:Biomedical signal processing and control Ročník 62; s. 102054
Hlavní autoři: Porumb, Mihaela, Griffen, Corbin, Hattersley, John, Pecchia, Leandro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2020
Témata:
Convolutional denoising autoencoder
Convolutional neural networks
Nocturnal hypoglycemia
Convolutional neural networks
Convolutional denoising autoencoder
Nocturnal hypoglycemia
ISSN:1746-8094, 1746-8108
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Shrnutí:Hypoglycemia is a condition caused by low blood glucose levels, mainly affecting people with diabetes. Low levels of blood glucose can be dangerous, causing multiple complications over time. Hypoglycemia affects the repolarization characteristics of the heart. This study presents a personalized, deep learning-based approach that enables nocturnal hypoglycemia detection using raw electrocardiogram (ECG) signals, recorded with non-invasive, wearable devices. The study was carried out in a calorimeter room during 24–36 h monitoring on twenty-five, healthy, elderly participants. The results of this study (accuracy of ≈90%) provide evidence for the feasibility of a non-invasive, ECG-based hypoglycemia alarming system. Moreover, leveraging an unsupervised method (i.e. convolutional denoising autoencoder) and visualizing the ECG heartbeats in the embeddings space, clear heartbeats clusters grouped by glucose levels could be determined, showing that specific patterns in the input heartbeats are indeed glucose discriminative and the autoencoder could successfully capture those patterns.
ISSN:1746-8094
1746-8108
DOI:10.1016/j.bspc.2020.102054