Hyperglycemia Identification Using ECG in Deep Learning Era

Cordeiro, Renato; Karimian, Nima; Park, Younghee

doi:10.3390/s21186263

Cited by 35 publications

(23 citation statements)

References 41 publications

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“…However, this approach does not meet the requirements from the Clarke error grid analysis. Cordeiro et al [120] trained several machine and deep learning models to detect hypoglycemia. He computed innovative HRV features from ECG signals belonging to 1119 subjects.…”

Section: Blood Glucose Estimation With Deep Learningmentioning

confidence: 99%

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Zanelli

Ammi

Hallab³

et al. 2022

Sensors

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(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review’s aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as “Diabetes”, “ECG”, “PPG”, “Machine Learning”, etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review’s aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.

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Section: Blood Glucose Estimation With Deep Learningmentioning

confidence: 99%

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Zanelli

Ammi

Hallab³

et al. 2022

Sensors

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“…8,9,12,13 In taking a more global analytic approach, we have identified a potential role for ECG-based classification of non-cardiac disease. We additionally highlight conditions for which further study may be particularly high yield, including diseases not classically associated with ECG findings but each independently supported by prior studies (e.g., type 2 diabetes, 36,37 sleep apnea, [38][39][40][41] chronic liver disease/cirrhosis, 15,42 and renal failure 43 ), as well as diseases with previously undescribed associations.…”

Section: Discussionmentioning

confidence: 72%

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

Venn

Wang

Friedman

et al. 2022

Preprint

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The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance of the ECG, though the extent to which diseases across the human phenotypic landscape can be detected on the ECG remains unclear. We developed a deep learning variational autoencoder model that encodes and reconstructs ECG waveform data within a multidimensional latent space. We then systematically evaluated whether associations between ECG encodings and a broad range of disease phenotypes could be detected using the latent space model by deriving disease vectors and projecting individual ECG encodings onto the vectors. We developed models for both 12- and single-lead ECGs, akin to those used in wearable ECG technology. We leveraged phecodes to generate disease labels using International Classification of Disease (ICD) codes for about 1,600 phenotypes in three different datasets linked to electronic health record data. We tested associations between ECG encodings and disease phenotypes using a phenome-wide association study approach in each dataset, and meta-analyzed the results. We observed that the latent space ECG model identified associations for 645 (40%) diseases tested in the 12-lead model. Associations were enriched for diseases of the circulatory (n=140, 82% of category-specific diseases), respiratory (n=53, 62%), and endocrine/metabolic (n=73, 45%) systems, with additional associations evident across the human phenome; results were similar for the single-lead models. The top ECG latent space association was with hypertension in the 12-lead ECG model, and cardiomyopathy in the single-lead ECG model (p<2.2x10-308 for each). The ECG latent space model demonstrated a greater number of associations than ECG models using standard ECG intervals alone, and generally resulted in improvements in discrimination of diseases compared to models comprising only age, sex, and race. We further demonstrate how a latent space model can be used to generate disease-specific ECG waveforms and facilitate disease profiling for individual patients.

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“…Using a deep belief network for the detection of hypoglycemic episodes in diabetes patients, San et al achieved sensitivity and specificity values of 80.00 and 50.00 %, respectively [23]. Cordeiro et al evaluated ECG data from 1,119 patients and found that a 10layer deep neural network was effective in detecting hyperglycemia, with a value of 94.53 % for the area under the curve (AUC), 87.57 % sensitivity, and 85.04 % specificity [24].…”

Section: Discussionmentioning

confidence: 99%

One-Class Machine-Learning Model to Screen for Dysglycemia Using Single Lead ECG in ICU, toward Noninvasive Blood Glucose Monitoring

Chiu¹,

Cheng²,

Chang³

et al. 2022

Preprint

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Blood glucose (BG) monitoring is an important issue for critically ill patients. Previous studies reported that poor sugar control was associated with increased mortality in admitted patients. However, repeated blood glucose monitoring can be resource-consuming and cause a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia based on electrocardiogram (ECG) findings. The study included patients with more than 20 ECG records during single hospital admission in the Medical Information Mart for Intensive Care III database, focusing on the lead II recordings, along with the corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine (SVM) algorithm to predict dysglycemia. The model prediction for dysglycemia using a single heartbeat had an AUC level of 0.92 ± 0.09, with a sensitivity of 0.92 ± 0.10 and specificity of 0.84 ± 0.04. Based on 10 s majority voting, the model prediction for dysglycemia improved to an AUC of 0.97 ± 0.06. In this study, we found that a personalized machine-learning algorithm could accurately detect dysglycemia using a single-lead ECG.

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Hyperglycemia Identification Using ECG in Deep Learning Era

Cited by 35 publications

References 41 publications

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling

One-Class Machine-Learning Model to Screen for Dysglycemia Using Single Lead ECG in ICU, toward Noninvasive Blood Glucose Monitoring

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