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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Veröffentlicht in:Biomedical signal processing and control Jg. 62; S. 102054
Hauptverfasser: Porumb, Mihaela, Griffen, Corbin, Hattersley, John, Pecchia, Leandro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2020
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ISSN:1746-8094, 1746-8108
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Abstract 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.
AbstractList 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.
ArticleNumber 102054
Author Griffen, Corbin
Hattersley, John
Porumb, Mihaela
Pecchia, Leandro
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  givenname: Mihaela
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  givenname: Corbin
  surname: Griffen
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  givenname: John
  surname: Hattersley
  fullname: Hattersley, John
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  givenname: Leandro
  surname: Pecchia
  fullname: Pecchia, Leandro
  email: L.Pecchia@warwick.ac.uk
  organization: University of Warwick, Coventry CV4 7AL, UK
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Keywords Convolutional neural networks
Convolutional denoising autoencoder
Nocturnal hypoglycemia
Language English
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Snippet Hypoglycemia is a condition caused by low blood glucose levels, mainly affecting people with diabetes. Low levels of blood glucose can be dangerous, causing...
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SubjectTerms Convolutional denoising autoencoder
Convolutional neural networks
Nocturnal hypoglycemia
Title Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders
URI https://dx.doi.org/10.1016/j.bspc.2020.102054
Volume 62
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