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

Ernia Susana, Kalamullah Ramli, Hendri Murfi, Nursama Heru Apriantoro

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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%.

Original languageEnglish
Article number59
JournalInformation (Switzerland)
Volume13
Issue number2
DOIs
Publication statusPublished - Feb 2022

Keywords

  • Blood glucose levels
  • Diabetes
  • Ensemble bagged trees
  • Machine learning
  • Photoplethysmography

Fingerprint

Dive into the research topics of 'Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal'. Together they form a unique fingerprint.

Cite this