Abstract
Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.
Highlights
Diabetes is a medical emergency that can cause various complications or even death in adults
When a finger is inserted into the finger sensor, the raw PPG waveforms can be obobserved at Test Point 1 (TP1) using an oscilloscope
The output from the high-pass filter (HPF) goes to an active low-pass filter (LPF) where op-amp amplifies the PPG signal so that the rated PPG signal voltage is 0.8 V
Summary
Diabetes is a medical emergency that can cause various complications or even death in adults. It causes many people to ignore their diabetes and not realize its severity or presence until their blood sugar is above standard. The types of diabetes are distinguished by their cause. T1D is caused by a deficiency of insulin-secreting β-cells in the pancreas and cannot be cured, but the patient’s quality of life can be maintained. T2D is characterized by insulin resistance which develops throughout the patient’s life. As T2D is an acquired metabolic condition, people have a higher probability of having T2D than T1D [4]. Effective prevention by regularly monitoring blood glucose levels (BGL) is necessary for diabetes management [5,6,7]
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