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Common AI-Based Methods Used in Blood Glucose Estimation with PPG Signals

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Smart Applications with Advanced Machine Learning and Human-Centred Problem Design (ICAIAME 2021)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 1))

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Abstract

Abnormal blood glucose levels (BGL) cause diabetes mellitus, a chronic, painful, and costly disease. Thus, continuous glucose measurement (CGM) is essential for diabetic people to prevent fatal complications. The estimation of blood glucose level (BGL) with photoplethysmography (PPG) signals has become very popular since it is based on a non-invasive measurement. This study presents ordinary artificial intelligence (AI) methods used in BGL estimation employing PPG signals. The AI-based techniques studied in this paper are as follows: Cepstral coefficients, support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), artificial neural network (ANN), and naive bays (NB). The mentioned AI-based methods used in BGL estimation are investigated and compared in the aspect of sensitivity, accuracy and specificity, and the main techniques in BGL estimation are determined. It is seen that the Cepstral coefficients method has yielded a significant percentage of accuracy. However, the DT method has also given accurate results close to the Cepstral coefficients method. Also, we think that a more precise hybrid or gradual approach can be developed by considering the pros of DT and Cepstral coefficient methods together.

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Authors and Affiliations

  1. Department of Electrical and Energy, Vocational School of Technical Science, Karamanoglu Mehmetbey University, Karaman, Turkey

    Ömer Pektaş

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Inonu University, Malatya, Turkey

    Murat Köseoğlu

Authors
  1. Ömer Pektaş
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  2. Murat Köseoğlu
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Correspondence to Ömer Pektaş .

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Pektaş, Ö., Köseoğlu, M. (2023). Common AI-Based Methods Used in Blood Glucose Estimation with PPG Signals. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_44

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