Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks

Saifeddin Alghlayini; Asmaa Hosni; Mohamed Atef

doi:10.1109/ASET56582.2023.10180655

Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks

Saifeddin Alghlayini
, Asmaa Hosni
, Mohamed Atef

Department of Electrical and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

This paper introduces the design and measurements for non-invasive blood glucose level (BGL) estimation using a convolutional neural network (CNN) based on photoplethysmography (PPG). The prototype consists of a PPG sensor connected to a microcontroller (MCU) Arduino Nano 33 BLE Sense. The PPG-only based CNN model deployed on the MCU showed 89.28% of the predicted samples in zone A of a Clarke error grid (CEG). When the mean power spectrum feature from PPG signals was included, the results demonstrated an improvement in the accuracy to be 92.85%. The proposed system is real-time and non-invasive that can be used to replace the existing invasive glucometers.

Original language	English
Title of host publication	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454742
DOIs	https://doi.org/10.1109/ASET56582.2023.10180655
Publication status	Published - 2023
Event	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 - Dubai, United Arab Emirates Duration: Feb 20 2023 → Feb 23 2023

Publication series

Name	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023

Conference

Conference	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	2/20/23 → 2/23/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Blood Glucose Level (BGL)
Convolutional Neural Network (CNN)
Deep Learning
Machine Learning
Microcontroller (MCU)
Photoplethysmography (PPG)
Wearable Device

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Biomedical Engineering
Control and Optimization
Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Fuel Technology

Access to Document

10.1109/ASET56582.2023.10180655

Cite this

Alghlayini, S., Hosni, A., & Atef, M. (2023). Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks. In 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASET56582.2023.10180655

Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks. / Alghlayini, Saifeddin; Hosni, Asmaa; Atef, Mohamed.
2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Alghlayini, S, Hosni, A & Atef, M 2023, Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks. in 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, Institute of Electrical and Electronics Engineers Inc., 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, Dubai, United Arab Emirates, 2/20/23. https://doi.org/10.1109/ASET56582.2023.10180655

Alghlayini S, Hosni A, Atef M. Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks. In 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023). doi: 10.1109/ASET56582.2023.10180655

Alghlayini, Saifeddin ; Hosni, Asmaa ; Atef, Mohamed. / Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks. 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023).

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abstract = "This paper introduces the design and measurements for non-invasive blood glucose level (BGL) estimation using a convolutional neural network (CNN) based on photoplethysmography (PPG). The prototype consists of a PPG sensor connected to a microcontroller (MCU) Arduino Nano 33 BLE Sense. The PPG-only based CNN model deployed on the MCU showed 89.28\% of the predicted samples in zone A of a Clarke error grid (CEG). When the mean power spectrum feature from PPG signals was included, the results demonstrated an improvement in the accuracy to be 92.85\%. The proposed system is real-time and non-invasive that can be used to replace the existing invasive glucometers.",

keywords = "Blood Glucose Level (BGL), Convolutional Neural Network (CNN), Deep Learning, Machine Learning, Microcontroller (MCU), Photoplethysmography (PPG), Wearable Device",

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N2 - This paper introduces the design and measurements for non-invasive blood glucose level (BGL) estimation using a convolutional neural network (CNN) based on photoplethysmography (PPG). The prototype consists of a PPG sensor connected to a microcontroller (MCU) Arduino Nano 33 BLE Sense. The PPG-only based CNN model deployed on the MCU showed 89.28% of the predicted samples in zone A of a Clarke error grid (CEG). When the mean power spectrum feature from PPG signals was included, the results demonstrated an improvement in the accuracy to be 92.85%. The proposed system is real-time and non-invasive that can be used to replace the existing invasive glucometers.

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KW - Machine Learning

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Photoplethysmography Based Blood Glucose Estimation Using Convolutional Neural Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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