Estimation of Blood Glucose from PPG Signal Using Convolutional Neural Network

Hossain, Shamima; Debnath, Bidya; Biswas, Sabyasachi; Al-Hossain, Md. Junaed; Anika, Adrita; Navid, Syed Khaled Zaman

doi:10.1109/becithcon48839.2019.9063187

Cited by 18 publications

(3 citation statements)

References 11 publications

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“…In comparison, heart rate and RWAT prediction tasks from PPG PWA have much lower multi-valued mapping factors and much higher mutual information indicating that the task is relatively well conditioned compared to PPG PWA to BP. We believe that these tools are relevant for feasibilty analysis in similar tasks involving wearable data, such as predicting stress levels from PPG [65][66][67] and estimating blood glucose levels from PPG [68][69][70] .…”

Section: Discussionmentioning

confidence: 99%

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

Mehta,

Jain,

Kwatra

et al. 2024

Preprint

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The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis

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Section: Discussionmentioning

confidence: 99%

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

Mehta,

Jain,

Kwatra

et al. 2024

Preprint

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“…Photoplethysmography. This technique involves predicting blood glucose levels from the PPG signal [ 135 , 136 , 137 , 138 , 139 , 140 ]. It has attracted increasing attention in recent years due to its low cost and wide adoption in smart devices [ 141 ].…”

Section: Noninvasive Sensing Techniquesmentioning

confidence: 99%

Noninvasive Glucose Sensing In Vivo

Leung

Forlenza

Prioleau

et al. 2023

Sensors

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Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

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“…Our proposed method is very relevant to obtaining a BGL classification in real-time. Most of the previous studies of PPG focused on estimating the BGL value [18,45,46]. The drawback of this estimation method is that clinical supervision is required.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

et al. 2022

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Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.

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Estimation of Blood Glucose from PPG Signal Using Convolutional Neural Network

Cited by 18 publications

References 11 publications

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

Examining the Challenges of Blood Pressure Estimation via Photoplethysmogram

Noninvasive Glucose Sensing In Vivo

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

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