Machine learning-based blood glucose measurement system based on Raman spectroscopy
Blood glucose level measurement includes a light source configured to irradiate light to a subject; a monochrome part configured to separate wavelength components of the light that is reflected and scattered from the subject; a light receiver configured to receive the light transmitted via the monochrome part and to generate electrical signals based on the received light; and a processor configured to extract information on the blood glucose level of the subject based on a frequency shift of the light due to the Raman effect.
1 . A non-transitory computer readable medium containing program instructions executed by a processor, the program instructions when executed by the processor configured to:
cause a light source to irradiate light to a subject;
receive, from a light receiver, electrical signals representative of a Raman spectrum of the light, which is reflected and scattered from the subject, and wavelength components of which are separated by using a monochrome part; and
determine a blood glucose level of the subject based on the Raman spectrum,
wherein the blood glucose level is determined based on a machine learning model that has been trained to correlate the blood glucose level with areas of (a) a peak at about 1450 cm −1 , (b) a peak at about 1660 cm −1 , and (c) a peak at about 1125 cm −1 in the Raman spectrum, and
wherein, for (c) the peak at about 1125 cm −1 , the glucose level is correlated based on each of at least three ranges (c-i) between 1089 cm −1 and 1160 cm −1 , (c-ii) between 1115 cm −1 and 1140 cm −1 , and (c-iii) between 1120 cm −1 and 1130 cm −1 , which correspond to glucose.
2 . The non-transitory computer readable medium of claim 1 , wherein the machine learning model utilizes partial least squares (PLS), support vector machine (SVM), autoencoder, ResNet, or any combination thereof.
3 . The non-transitory computer readable medium of claim 2 , wherein the machine learning model comprises a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, a generative adversarial network (GAN), or any combination thereof.
4 . The non-transitory computer readable medium of claim 1 , wherein, for (a) the peak at about 1450 cm −1 , the area is obtained in a range between 1415 cm −1 and 1480 cm −1 , which corresponds to proteins.
5 . The non-transitory computer readable medium of claim 1 , wherein, for (b) the peak at about 1660 cm −1 , the area is obtained in a range between 1630 cm −1 to and 1685 cm −1 , which corresponds to fats.
6 . A non-transitory computer readable medium containing program instructions executed by a processor, the program instructions when executed by the processor configured to:
cause a light source to irradiate light to a subject;
receive, from a light receiver, electrical signals representative of a Raman spectrum of the light, which is reflected and scattered from the subject, and wavelength components of which are separated by using a monochrome part;
obtain areas of (a) a peak at about 1450 cm −1 , (b) a peak at about 1660 cm −1 , and (c) a peak at about 1125 cm −1 in the Raman spectrum;
receive a measured blood glucose level of the subject; and
update a machine learning model based on a correlation of the measured blood glucose level of the subject with the areas obtained using the Raman spectrum,
wherein, for (c) the peak at about 1125 cm −1 , the correlation is based on each of at least three ranges (c-i) between 1089 cm −1 and 1160 cm −1 , (c-ii) between 1115 cm −1 and 1140 cm −1 , and (c-iii) between 1120 cm −1 and 1130 cm −1 , which correspond to glucose.
7 . The non-transitory computer readable medium of claim 6 , wherein the measured blood glucose level of the subject is measured using at least one of finger blood sampling, vein blood sampling, or continuous CGM.
8 . The non-transitory computer readable medium of claim 6 wherein the machine learning model utilizes partial least squares (PLS), support vector machine (SVM), autoencoder, ResNet, or any combination thereof.
9 . The non-transitory computer readable medium of claim 8 , wherein the machine learning model comprises a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, a generative adversarial network (GAN), or any combination thereof.
10 . The non-transitory computer readable medium of claim 6 , wherein, for (a) the peak at about 1450 cm −1 , the area is obtained in a range between 1415 cm −1 and 1480 cm −1 .
11 . The non-transitory computer readable medium of claim 6 , wherein, for (b) the peak at about 1660 cm −1 , the area is obtained in a range between 1630 cm −1 to and 1685 cm −1 .