Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications.

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone.

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

The Conceptual Design of a Novel Workstation for Seizure Prediction Using Machine Learning With Potential eHealth Applications.

Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research. On the other hand, building the optimal workstation is a complex task, and system instability can arise from the least obvious sources imaginable. Therefore, the absence of a template for a dedicated seizure prediction workstation in today's literature is a formidable obstacle to seizure prediction research. To increase the number of researchers working on this problem, a template for a dedicated seizure prediction workstation needs to become available. This paper proposes a novel dedicated system capable of machine learning-based seizure prediction and training for under U.S. $1000, which is significantly less expensive (U.S. $700 or more) than comparable commercial solutions. This powerful workstation will be capable of training sophisticated machine learning algorithms that can be deployed to lightweight wearable devices, which enables the creation of wearable EEG-based seizure early warning systems.