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Artificial Intelligence for Glucose Modelling and Prediction in Diabetes Care
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Artificial Intelligence for Glucose Modelling and Prediction in Diabetes Care

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 10198
Please feel free to contact Guest Editors or Special Issue Editor (charlie.cai@mdpi.com) for any queries.

Special Issue Editors

Prof. Mehdi Ammi

E-Mail Website
Guest Editor
Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France
Interests: human activity recognition; modeling physiological functions; emotion recognition; affective and social interaction; human–computer interaction; pervasive and ubiquitous environments; Internet of Things; e-health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mounim A. El Yacoubi

E-Mail Website
Guest Editor
Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France
Interests: machine learning; deep learning; pattern recognition; modeling behavioral and physiological human data; human activity and gesture recognition; handwriting and voice analysis; human mobility analysis; biometrics; human–computer interaction; detection and assessment of neurodegenerative diseases from biometric signals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diabetes has in just a few decades become one of the major diseases around the world. The number of people with diabetes quadrupled from 1980 to 2014 (108 to 422 million) with a death toll of 4.2 million in 2019. Today, diabetes is the major cause of stroke, heart attacks, blindness, kidney failure, and lower limb amputation.

Numerous efforts have been made to provide advanced solutions to measure, analyze, and predict glycemic and related factors to coach patients but also to provide medical practitioners with platforms to monitor this type of chronic disease.

In recent years, smart sensors, the Internet of Things, and Artificial Intelligence have triggered a revolution in this field. In particular, advances in deep learning models, transfer learning, as well the acceptability and interpretability of these models, have allowed them to achieve an impressive performance. These technological advances are transforming diabetes care from early diagnosis to risk modeling and complication monitoring.

This Special Issue is intended to provide an up-to-date and comprehensive view of recent progress in Artificial Intelligence applied to diabetes care.

Potential topics include but are not limited to:

  • Diabetes diagnosis
  • Blood sugar modeling and prediction
  • Physical activity and emotion analysis for diabetes
  • Nutritional analysis
  • Patients’ self-management and coaching
  • Risk modelling
  • Complication monitoring
  • Genomics for diabetes
  • Emerging trends in deep learning techniques
  • Physiological models for diabetes assessment
  • Smart sensing and IoT
  • Multisensor data fusion
  • Intelligent data acquisition and measurement systems
  • Cloud and embedded computing
  • Human/machine smart interfaces

Prof. Mehdi Ammi
Prof. Mounim A. El Yacoubi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Review

28 pages, 5276 KB  
Review
Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review
by Serena Zanelli, Mehdi Ammi, Magid Hallab and Mounim A. El Yacoubi
Sensors 2022, 22(13), 4890; https://doi.org/10.3390/s22134890 - 29 Jun 2022
Cited by 26 | Viewed by 8636
Abstract
(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 [...] Read more.
(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. Full article
(This article belongs to the Special Issue Artificial Intelligence for Glucose Modelling and Prediction in Diabetes Care)
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