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US-2024428568-A1 · Dec 26, 2024 · US
Near-infrared spectroscopy (nir) based glucose prediction using deep learning
US2020293882A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2020293882-A1 |
| Application number | US-201916402204-A |
| Country | US |
| Kind code | A1 |
| Filing date | May 2, 2019 |
| Priority date | Mar 15, 2019 |
| Publication date | Sep 17, 2020 |
| Grant date | — |
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Abstract
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A recurrent neural network that predicts blood glucose level includes a first long short-term memory (LSTM) network and a second LSTM network. The first LSTM network may include an input to receive near-infrared (NIR) radiation data and includes an output. The second LSTM network may include an input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network.
First claim
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What is claimed is: 1 . A recurrent neural network to predict blood glucose level, the recurrent neural network comprising: a first long short-term memory (LSTM) network comprising an input to receive near-infrared (NIR) radiation data and an output; and a second LSTM network comprising an input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network. 2 . The recurrent neural network of claim 1 , further comprising a denoiser filter coupled to the input of the first LSTM network, the denoiser filter receiving the NIR radiation data and outputting denoised NIR radiation data to the input of the first LSTM network. 3 . The recurrent neural network of claim 2 , wherein the denoising filter comprises a Savitzky-Golay filter. 4 . The recurrent neural network of claim 2 , further comprising an autoencoder coupled to the input of the denoiser filter, the autoencoder receiving the NIR radiation data and outputting data codings corresponding to the NIR radiation data, wherein the denoiser filter receives the data codings corresponding to the NIR radiation data. 5 . The recurrent neural network of claim 1 , further comprising an autoencoder coupled to the input of the first LSTM network, the autoencoder receiving the NIR radiation data and outputting denoised NIR radiation data to the input of the first LSTM network. 6 . The recurrent neural network of claim 1 , wherein the input NIR radiation data comprises multichannel NIR radiation data. 7 . The recurrent neural network of claim 6 , wherein the input NIR radiation data comprises 131 channels. 8 . The recurrent neural network of claim 1 , wherein the input NIR radiation data comprises first overtone NIR radiation data. 9 . A system to predict blood glucose level, the system comprising: an input interface to receive near-infrared (NIR) radiation data; and a recurrent neural network coupled to the input interface, the recurrent neural network comprising: a first long short-term memory (LSTM) network comprising an input to receive the NIR radiation data and an output; and a second LSTM network comprising an input to receive the output of the first LSTM network and an output to output blood glucose level data based on the NIR radiation data input to the first LSTM network. 10 . The system of claim 9 , further comprising a denoiser filter coupled to the input of the first LSTM network, the denoiser filter receiving the NIR radiation data and outputting denoised NIR radiation data to the input of the first LSTM network. 11 . The system of claim 10 , wherein the denoising filter comprises a Savitzky-Golay filter. 12 . The system of claim 10 , further comprising an autoencoder coupled to the input of the denoiser filter, the autoencoder receiving the NIR radiation data and outputting data codings corresponding to the NIR radiation data, wherein the denoiser filter receives the data codings corresponding to the NIR radiation data. 13 . The system of claim 9 , further comprising an autoencoder coupled to the input of the first LSTM network, the autoencoder receiving the NIR radiation data and outputting denoised NIR radiation data to the input of the first LSTM network. 14 . The system of claim 9 , wherein the input NIR radiation data comprises multichannel NIR radiation data. 15 . The system of claim 14 , wherein the input NIR radiation data comprises 131 channels. 16 . The system of claim 9 , wherein the input NIR radiation data comprises first overtone NIR radiation data.
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Classifications
Recurrent networks, e.g. Hopfield networks · CPC title
- G06N3/045Primary
Combinations of networks · CPC title
Supervised learning · CPC title
characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title
Auto-encoder networks; Encoder-decoder networks · CPC title
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- What does patent US2020293882A1 cover?
- A recurrent neural network that predicts blood glucose level includes a first long short-term memory (LSTM) network and a second LSTM network. The first LSTM network may include an input to receive near-infrared (NIR) radiation data and includes an output. The second LSTM network may include an input to receive the output of the first LSTM network and an output to output blood glucose level dat…
- Who is the assignee on this patent?
- Samsung Electronics Co Ltd
- What technology area does this patent fall under?
- Primary CPC classification G06N3/045. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Thu Sep 17 2020 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).