Deep Learning Method to Estimate Glucose Level from Heart Rate Variability
Journal
2020 28th Telecommunications Forum (TELFOR)
Date Issued
2020-11-24
Author(s)
Shaqiri, Ervin
DOI
10.1109/telfor51502.2020.9306609
Abstract
Recently, there have been efforts by different researchers in implementing some Machine Learning (ML) and Deep Learning (DL) techniques in designing a model that will predict Glucose based solely on Heart Rate Variability parameters. However, each study uses an in-house dataset and thus the results differ from the rest. The aim of this paper is to explore the predictive capabilities of DL techniques in designing a model to predict glucose regulation from Heart Rate Variability. The clinical study was conducted on a dataset of 155 patients with long-term Electrocardiogram measurements. The best results are achieved with an architecture of three hidden layers (32, 256, and 64 neurons, respectively) with an Adam optimizer alongside a learning rate of 0.001 coupled by a Binary Cross entropy loss function. Furthermore, the Z score outlier removal method proved to lead to a higher accuracy value, whilst the IQR outlier removal method proved to lead to a higher Fl score value.