Method of enabling feature extraction for glucose monitoring using near-infrared (NIR) spectroscopy

1. A method of extracting glucose feature, the method comprising:

obtaining, by a spectrometer, a near-infrared (NIR) data;

filtering the NIR data by removing noise from the NIR data;

obtaining orthogonal components of a reference glucose spectrum;

applying an Extended Multiplicative Scatter Correction (EMSC) regression to the reference glucose spectrum to resolve the reference glucose spectrum into orthogonal components;

extracting a glucose signal from the filtered NIR data by removing the orthogonal components from the filtered NIR data; and

removing, from the extracted glucose signal, temporal drift components that are caused due to a drift of the spectrometer.

2. The method of claim 1 , wherein the filtering the NIR data comprises removing the noise from the NIR data using a Savitzky-Golay (SG) filter.

3. The method of claim 1 , wherein the extracting the glucose signal comprises removing, from the filtered NIR data, a signal component corresponding to a blood component other than a glucose.

4. The method of claim 3 , wherein the blood component other than the glucose comprises at least one of water, fat and protein.

5. The method of claim 1 , wherein the removing temporal drift components comprises differentiating and smoothing the extracted glucose signal, and removing the temporal drift components by applying a drift removal technique to the differentiated and smoothed glucose signal.

6. The method of claim 5 , wherein the removing temporal drift components by applying the drift removal technique comprises calculating the temporal drift components by differentiating and averaging the differentiated and smoothed glucose signal, and removing the calculated temporal drift components from the differentiated and smoothed glucose signal.

7. A method of monitoring glucose, the method comprising:

filtering a near-infrared (NIR) data, which is obtained by a spectrometer from a NIR light that is emitted to and returns from a user, by removing noise from the NIR data;

obtaining orthogonal components of a reference glucose spectrum;

applying an Extended Multiplicative Scatter Correction (EMSC) regression to the reference glucose spectrum to resolve the reference glucose spectrum into orthogonal components;

extracting a glucose signal from the filtered NIR data by removing the orthogonal components from the filtered NIR data;

removing, from the extracted glucose signal, temporal drift components that are caused due to a drift of the spectrometer;

generating a data model for glucose prediction by regressing the extracted glucose signal from which the temporal drift components have been removed, based on an expected glucose signal; and

predicting a glucose level of the user based on the generated data model.

8. The method of claim 7 , wherein the filtering is performed using an Savitzky-Golay (SG) filter.

9. The method of claim 7 , wherein the filtering is performed using a Fourier domain filtering.

10. The method of claim 7 , wherein the removing the temporal drift components further comprises smoothing a differential of the extracted glucose signal by coarsely removing the temporal drift components from the extracted glucose signal.

11. The method of claim 10 , wherein the removing the temporal drift components further comprises applying a fine drift removal method to the smoothed differential of the extracted glucose signal to remove a residual drift from the extracted glucose signal.

12. A glucose monitoring device, comprising:

a filtering module configured to filter a near-infrared (NIR) data generated from a NIR spectrometer by removing noise from the NIR data;

an interferents removal module configured to:

obtain orthogonal components of a reference glucose spectrum;

apply an Extended Multiplicative Scatter Correction (EMSC) regression to the reference glucose spectrum to resolve the reference glucose spectrum into orthogonal components;

extract a glucose signal from the filed NIR data by removing the orthogonal components from the filtered NIR data; and

a feature extraction module configured to remove, from the extracted glucose signal, drift components caused due to a drift of the NIR spectrometer;

wherein the glucose monitoring device is configured to generate a data model for glucose prediction by regressing the extracted glucose signal from which the drift components have been removed, based on an expected glucose signal; and predict a glucose level of a user based on the generated data model.

13. The glucose monitoring device of claim 12 , wherein the filtering module comprises a Savitzky-Golay (SG) filter and a Fourier domain filter to remove the noise from the NIR data.

14. The glucose monitoring device of claim 12 , wherein the feature extraction module comprises:

a SG filter with differential module configured to smooth a differential of the extracted glucose signal; and

a drift removal module configured to remove the drift components from the extracted glucose signal.