# Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning

arXiv: https://arxiv.org/abs/2303.17616 · HTML: https://mlsanity.com/paper/2303.17616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [Signal Classification using Smooth Coefficients of Multiple wavelets](https://mlsanity.com/paper/2109.09988/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia](https://mlsanity.com/paper/2601.11615/) (2026)

## Abstract (arXiv)

People with diabetes have to manage their blood glucose level to keep it within an appropriate range. Predicting whether future glucose values will be outside the healthy threshold is of vital importance in order to take corrective actions to avoid potential health damage. In this paper we describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated. The approach of this work is the application of transformation functions on glucose time series, and their use in convolutional neural networks. We have tested our proposed method using real data from 4 different diabetes patients with promising results.