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Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG
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Porumb, Mihaela, Stranges, Saverio, Pescapè, Antonio and Pecchia, Leandro (2020) Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG. Scientific Reports, 10 (1). 170. doi:10.1038/s41598-019-56927-5 ISSN 2045-2322.
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WRAP-precision-medicine-artificial-intelligence-events-ECG-2-Pecchia-020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1763Kb) | Preview |
Official URL: http://dx.doi.org/10.1038/s41598-019-56927-5
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | R Medicine > RA Public aspects of medicine | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Blood sugar monitoring, Diabetes, Hypoglycemia | ||||||
| Journal or Publication Title: | Scientific Reports | ||||||
| Publisher: | Nature Publishing Group | ||||||
| ISSN: | 2045-2322 | ||||||
| Official Date: | 13 January 2020 | ||||||
| Dates: |
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| Volume: | 10 | ||||||
| Number: | 1 | ||||||
| Article Number: | 170 | ||||||
| DOI: | 10.1038/s41598-019-56927-5 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 21 January 2020 | ||||||
| Date of first compliant Open Access: | 30 January 2020 | ||||||
| RIOXX Funder/Project Grant: |
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