Jayasanthi, M and Vismitha, V and Eunice Joshi, A P (2024) A Non-Invasive Approach for Hemoglobin Estimation. In: 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), Coimbatore, India.
Full text not available from this repository.Abstract
To facilitate hemoglobin value estimation noninvasively, a machine learning model is proposed in this paper. Features extracted from the acquired Photo-plethysmography (PPG) signals are used in the estimation of haemoglobin value using a Machine Learning model. Hemoglobin estimation is also done using modified Beer-Lambert's law along with a Random Forest Regression model. This model is chosen since it has the lowest mean absolute error and highest accuracy when compared to other Machine Learning models like LASSO Regression, Support Vector Regression, Ridge Regression, and ADA-BOOST Regression. The PPG dataset was obtained from 3000 subjects. The signals were recorded from a fingertip at 125Hz and pre-processed for baseline wander minimization, noise, and motion artifact removal. The PPG signal database consists of a cell array of matrices. Matlab helped in extracting 21 features from the PPG signal. Accuracy is enhanced by identifying how each feature is related to each other using a Correlation matrix and Heatmap, including derivatives and Spectral features of PPG signal. The dataset is divided into a training and testing set to train the Machine Learning model. After training with the training data set, the Hb values are estimated with test data as input. The random forest regression ML model used here provides an accuracy of 81% with a Mean Absolute Error of 0.88, Mean Squared Error of 1.76, variance score of 0.61, and root MSE of 0.43.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | Beer Lambert law; Forest regression; Haemoglobins; Machine learning models; Machine-learning; Mean absolute error; Photo plethysmography; Photo-plethysmograph; Random; Random forests |
Subjects: | C Computer Science and Engineering > Network Security C Computer Science and Engineering > Machine Learning |
Divisions: | Electronics and Communication Engineering |
Depositing User: | Dr Krishnamurthy V |
Date Deposited: | 27 Sep 2024 06:32 |
Last Modified: | 27 Sep 2024 06:33 |
URI: | https://ir.psgitech.ac.in/id/eprint/1228 |