ArticleSensors (Basel, Switzerland)2021
Hyperglycemia Identification Using ECG in Deep Learning Era.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled 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
- Development and Pilot Evaluation of a Wearable 12-Lead ECG System for Multilead Feature Analysis in Individuals with Different Glycemic Status.Sensors (Basel, Switzerland) · 2026Article
- Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction.medRxiv : the preprint server for health sciences · 2025Article
- Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions.Sensors (Basel, Switzerland) · 2025Article
- Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.NPJ digital medicine · 2025Article
- Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals.Bioengineering (Basel, Switzerland) · 2024Article
- Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.JACC. Clinical electrophysiology · 2024Article
- Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram.Diagnostics (Basel, Switzerland) · 2024Article
- State of the art of mobile health technologies use in clinical arrhythmia care.Communications medicine · 2024Review
- Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study.EClinicalMedicine · 2024Article
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Article
- Assessment of Electrocardiographic Response to Fluctuating Blood Glucose Levels in People Without Diabetes.Journal of diabetes science and technology · 2023Article
- Blood glucose estimation based on ECG signal.Physical and engineering sciences in medicine · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring.Biosensors · 2022Article
- The emerging roles of machine learning in cardiovascular diseases: a narrative review.Annals of translational medicine · 2022Review
- Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.International journal of arrhythmia · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.
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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.