Developing A Deep Learning Solution to Estimate Instantaneous Glucose Level from Heart Rate Variability
Date Issued
2021
Author(s)
Shaqiri, Ervin
Gusev, Marjan
DOI
10.23919/MIPRO52101.2021.9597191
Abstract
Abstract—A great deal of studies address the use IoT devices
coupled by machine learning in order to predict and better detect
health problems. Diabetes is an issue that society is struggling
for a very long time. The ease with which ECG signals can be
recorded and interpreted provides an opportunity to use Deep
Learning techniques to predict the estimated Sugar Levels of
a patient. This research aims at describing a Deep Learning
approach to provide models for different short term heart rate
variability measurements.
Our approach is based on a special method to calculate heart
rate variability with identification of segments, then averaging
and concatenating them to exploit better feature engineering
results.The short-term measurements are used for determination
of instantaneous plasma glucose levels. Deep Learning method
is based on Autokeras, the neural architectural search provided
the best results for the 15 minute measurements.
Our research question is to develop a solution to estimate
the Instantaneous glucose value from heart rate variability with
sufficient quality. The evaluated test set gave the following results:
RMSE(0.368), MSE(0.193), R square(51.281), and R squared
loss(54.128).
coupled by machine learning in order to predict and better detect
health problems. Diabetes is an issue that society is struggling
for a very long time. The ease with which ECG signals can be
recorded and interpreted provides an opportunity to use Deep
Learning techniques to predict the estimated Sugar Levels of
a patient. This research aims at describing a Deep Learning
approach to provide models for different short term heart rate
variability measurements.
Our approach is based on a special method to calculate heart
rate variability with identification of segments, then averaging
and concatenating them to exploit better feature engineering
results.The short-term measurements are used for determination
of instantaneous plasma glucose levels. Deep Learning method
is based on Autokeras, the neural architectural search provided
the best results for the 15 minute measurements.
Our research question is to develop a solution to estimate
the Instantaneous glucose value from heart rate variability with
sufficient quality. The evaluated test set gave the following results:
RMSE(0.368), MSE(0.193), R square(51.281), and R squared
loss(54.128).
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