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This paper proposes a non invasive blood glucose sensing system using photoplethysmography (PPG). Neural network based adaptive noise cancellation (adaline) is employed to reduce the motion artifact. Also artificial neural network is used to create the predictive model which estimates the glucose levels based on PPG signals. Error in estimating glucose levels came out to be 5.48 mg/dl using ANN on MATLAB. This predictive model created by ANN has been implemented on FPGA. Error in estimating glucose levels by the ANN model implemented on FPGA, came out to be 7.23mg/dl. The results have been validated by performing Clarke error grid analysis.

This paper presents an approach of apt prognostic diagnostics of cardiac health by using Artificial Intelligence (AI) in safety-related based non-invasive bio-medical systems. This approach addresses the existing challenge in identification of the actual abnormality of the vital cardiac signal from the various interrupting factors like bio-signal faulted due to high noise signal interference, electronic and software fault, mechanical fault like sensor contacts failures, wear and tear of equipment. Presently, most of the medical systems use a 1oo1(one-out-of-one) system architectures, and there exists a safety procedure to raise a particular defined type of standard alarm for a specific failure to detect an abnormality. These existing approaches may incur high maintenance costs and subject to random failures with long downtimes of the system and where it affects operational safety to a certain extent. However, there is a scope to improve in the segregation of the actual fault-free signal and extract the abnormality of the vital feature for prognostic diagnostics. With advancements in systems engineering and usage of safety-related design architectures in medical systems, we used an Artificial Intelligence (AI) based approach in performing the data analytics on the selected correct vital signal for prognostic analysis. As a case study, we evaluated by configuring the system with the 2oo2 fault-tolerant safety-related design architecture and implemented the diagnostic function using the AI-based method on the apt logged data during system operation. The results show a substantial improvement in the accuracy of the cardiac health findings.

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