Noninvasive Blood Glucose Estimation Using Pulse Based Cepstral Coefficients

Habbu, Shraddha; Joshi, Shrikant S.; Dale, Manisha; Ghongade, Rajesh

doi:10.1109/icspis48135.2019.9045897

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Photoplethysmography (PPG) technique is used for measurement of blood glucose concentration. (24,25) Different time and frequency domain features like auto regression coefficient, peak distance, power spectral density, pulse transit time etc. are extracted from PPG signal recorded from these patients.…”

Section: Modified Approachmentioning

confidence: 99%

“…More detailed information can be obtained by analysis of the PPG signal sequences recorded over some time interval of about few minutes. (25)(26)(27) A functional relationship exists between pulse signal and blood glucose concentration. (25) For measuring blood glucose concentration, time and frequency domain features are extracted from PPG waveform.…”

Section: Photoplethysmographymentioning

confidence: 99%

“…(25)(26)(27) A functional relationship exists between pulse signal and blood glucose concentration. (25) For measuring blood glucose concentration, time and frequency domain features are extracted from PPG waveform. We have done single pulse wave analysis for the purpose of concentration measurement.…”

Section: Photoplethysmographymentioning

confidence: 99%

“…These coefficients are also used to models the spectral envelope of PPG signals. (25) • Kaiser Teager Energy It is well-known method used for finding energy profiles of signals with periodic signal components. This property indicates whether the signal is clean or noisy signal.…”

Section: • Auto Regression Coefficientsmentioning

confidence: 99%

“…This property indicates whether the signal is clean or noisy signal. (25) • Power Spectral Density It measures damping of pulses, the spectral harmonic components and presence of noise. (25) These features are given as input to neural network.…”

Section: • Auto Regression Coefficientsmentioning

confidence: 99%

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Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement

Bavkar¹,

Shinde²

2021

IJST

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Objectives: To facilitate painless and easy method for prediction of diabetes with high accuracy and to measure blood glucose by noninvasive method using Photoplethysmography (PPG). Method: In this study, diabetes prediction is done using different machine learning algorithms on a dataset created by using samples from PIMA Indian Diabetes dataset and in vivo diabetes dataset. Machine learning algorithms used are Support Vector Machine (SVM), Decision Tree, Naïve Bayes Classifier and K Nearest Neighbor (KNN). A PPG data of 182 individuals is recorded over 1 minute duration each. Various frequency and time domain features of PPG single are extracted using single pulse wave analysis. Neural network is trained using extracted features and glucose measurement is performed. Findings: With decision tree algorithm we got highest accuracy of 89.97% for diabetes prediction and it proves to be good algorithm for consideration for measurement in diabetes treatment.Clarke Error Grid analysis for blood glucose prediction is clinically accepted, so we performed similar analysis. Using time and frequency domain features, we got 94.27 % data points in clinically accepted regions (Region A and Region B). Novelty: Based on the data collected or samples analyzed, and accuracy of our results, it is encouraging to see that further research may lead to affordable noninvasive method for detection of diabetes at early stage.

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Section: Modified Approachmentioning

confidence: 99%