Hemoglobin and glucose level estimation from PPG characteristics features of fingertip video using MGGP-based model
| Autor: | S. M. Taslim Uddin Raju, Mohamed Hashem, Md. Asaf-uddowla Golap, Md. Rezwanul Haque |
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| Rok vydání: | 2021 |
| Předmět: |
business.industry
0206 medical engineering Biomedical Engineering Health Informatics Genetic programming Regression analysis Feature selection Pattern recognition 02 engineering and technology 020601 biomedical engineering Correlation 03 medical and health sciences 0302 clinical medicine Photoplethysmogram Signal Processing Genetic algorithm Artificial intelligence Symbolic regression business 030217 neurology & neurosurgery Second derivative Mathematics |
| Zdroj: | Biomedical Signal Processing and Control. 67:102478 |
| ISSN: | 1746-8094 |
| Popis: | Hemoglobin and the glucose level can be measured after taking a blood sample using a needle from the human body and analyzing the sample, the result can be observed. This type of invasive measurement is very painful and uncomfortable for the patient who is required to measure hemoglobin or glucose regularly. However, the non-invasive method only needed a bio-signal (image or spectra) to estimate blood components with the advantages of being painless, cheap, and user-friendliness. In this work, a non-invasive hemoglobin and glucose level estimation model have been developed based on multigene genetic programming (MGGP) using photoplethysmogram (PPG) characteristic features extracted from fingertip video captured by a smartphone. The videos are processed to generate the PPG signal. Analyzing the PPG signal, its first and second derivative, and applying Fourier analysis total of 46 features have been extracted. Additionally, age and gender are also included in the feature set. Then, a correlation-based feature selection method using a genetic algorithm is applied to select the best features. Finally, an MGGP based symbolic regression model has been developed to estimate hemoglobin and glucose level. To compare the performance of the MGGP model, several classical regression models are also developed using the same input condition as the MGGP model. A comparison between MGGP based model and classical regression models have been done by estimating different error measurement indexes. Among these regression models, the best results ( ± 0.304 for hemoglobin and ± 0.324 for glucose) are found using selected features and symbolic regression based on MGGP. |
| Databáze: | OpenAIRE |
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