ArticleScientific reports2020
Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 63 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
Development and Validation of Artificial Intelligence Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics
Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 195 citations in OpenAlex.
- Pooled it
- Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.Diabetes, obesity & metabolism · 2026Review
- Noninvasive Detection of Acute Hyperglycemia Using Signal from Wearable ECG Sensors Considering Individual HRV Response Delays to Glucose.Biosensors · 2026Article
- Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation.BMC medical informatics and decision making · 2026Article
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- AI-enhanced non-invasive monitoring of critical biochemical analytes in perioperative care: a review of signal processing and clinical applications.Frontiers in physiology · 2026Review
- Time below range alone is insufficient to identify severe hypoglycaemia risk in type 1 diabetes-the critical role of hypoglycaemia awareness: results from the SFDT1 study.Diabetologia · 2025Observational
- A multimodal physiological dataset for non-invasive blood glucose estimation.Scientific data · 2025Article
- Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.Journal of diabetes and metabolic disorders · 2025Review
- Review
- Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram.NPJ digital medicine · 2025Article
- Advances in Electrocardiogram-Based Artificial Intelligence Reveal Multisystem Biomarkers.Journal of clinical & experimental cardiology · 2025Article
- Applied body-fluid analysis by wearable devices.Nature · 2024Review
- Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.Cell reports. Medicine · 2024Article
- Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques.Bone reports · 2024Article
- Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry.Journal of diabetes science and technology · 2024Article
- Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.Sensors (Basel, Switzerland) · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.