A Deep Learning Approach For Non-Invasive Continuous Blood Glucose Monitoring With HRV for Diabetes Management

Diabetes is an inveterate metabolic disease which is affecting approximately 463 million individuals around the world. For the enhancement of the medication of individuals affected under diabetes, eHealth has been extensively endorsed in the past few years and produced a significant number of data t

Project Title

A Deep Learning Approach For Non-Invasive Continuous Blood Glucose Monitoring With HRV for Diabetes Management

Project Area of Specialization

Wearables and Implantable

Project Summary

Diabetes is an inveterate metabolic disease which is affecting approximately 463 million individuals around the world. For the enhancement of the medication of individuals affected under diabetes, eHealth has been extensively endorsed in the past few years and produced a significant number of data that can be utilized for advanced operations of this inveterate disorder. By obtaining the greatest possible benefit from the situation, methods that employ artificial intelligence and deep learning were broadly endorsed along with encouraging outcomes. In the intended proposal, we introduced a thorough study of deep learning implementations in the account of diabetes. We carried out efficient documented research and determined three major sectors that use these methods: diabetes examination, glucose handling, and analysis of diabetes-affiliated problems. HRV relates to the measurement of the variance in time during every heartbeat and the variance in time is managed by the significant part of the nervous system, known as the autonomic nervous system. HRV testing is usually performed to evaluate the independent operations in diabetic studies. It is an observable event that comprises cycles in chronological heartbeat spans managed by the self-governing nervous system and it is induced by the heart's ability to deal with constant stress and points of relaxation on the body. Diabetic patients are instructed to measure their glucose level daily especially Type I and II diabetic patients need to measure their glucose levels up to 10 times a day, the most used method is an invasive blood test that provokes distress, uneasiness, and cost consequences. The modern continuous glucose monitoring (CGM) methodology enables individuals to detect glucose concurrently and the latest study exhibits a relationship between HRV and glucose level, hypoglycemia (when the level of glucose in your blood drops below the 70mg/dl), and hyperglycemia (when the level of glucose is high than the normal). The main aim of this research is to improve the deep learning algorithm for continuous glucose monitoring with the help of HRV traits.

Project Objectives

As Diabetic patients are instructed to check their glucose level daily this method should be as convenient as possible for the patient but the only invasive technique available known as finger pricking is quite painful for them. We are providing the method that will be using a non-invasive technique to check glucose levels using HRV. It is an evaluation of the time difference during every heartbeat. It is controlled by the autonomic nervous system (ANS) and this system is responsible to control compulsory body work, such as it control the beats of the heart from speeding up and slowing down. The presented non-invasive method will use heart rate variability to check the glucose level of the patient using a wearable device.

The main objectives include:

• To overcome the unease the diabetic patient might have while using the invasive method of finger pricking.
• To check the BGL of the diabetic patient continuously.
• Emphasis on the regular monitoring and measurement of BGL.
• Checks the BGL without penetrating the skin of the patient.

Project Implementation Method

Benefits of the Project

The main benefits include:

• To overcome the unease the diabetic patient might have while using the invasive method of finger pricking.
• To check the BGL of the diabetic patient continuously.
• Emphasis on the regular monitoring and measurement of BGL.
• Checks the BGL without penetrating the skin of the patient.

Technical Details of Final Deliverable

We are working on it.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Health

Other Industries

Medical

Core Technology

Wearables and Implantables

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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