Title:	Predicting glucose from peripheral nerve signals
DNr:	Berzelius-2024-164
Project Type:	LiU Berzelius
Principal Investigator:	Henrik Hult <hult@kth.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2024-04-23 – 2024-11-01
Classification:	10106
Keywords:

Abstract

This is a WASP-DDLS project. Bioelectronic medicine is an emerging discipline combining neuroscience, immunology, and electrical engineering, that develops new methods capable of monitoring and treating diseases by electrical intervention in the peripheral nervous system. The aim of this project is to create data-driven statistical and machine learning algorithms that can analyze electrical signals from the peripheral nervous system to predict the level of glucose in the blood, and to provide proof-of-principle that an autonomous machine can replace the sensory detection by the central nervous system of bodily functions, with potential applications that will range far beyond predicting glucose levels. The data is collected through an implanted electrode on the vagus nerve while blood glucose levels will be varied. Using a conventional device to measure blood glucose as a reference, training data from the electrode is generated as high-frequency multi-channel signals that can be analyzed using a combination of statistical signal processing techniques and machine learning algorithms. By utilizing artificial intelligence and machine learning to interpret the recorded nerve signals from the peripheral nervous system, autonomous adaptation of treatment could potentially be achieved. This will help tackle the well-known dosage and timing problems, which are responsible for many unwanted side effects of currently available pharmaceutical drugs. This will be an important step towards truly personalized medicine.