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Background: Prior investigations regarding glucose variability in nondiabetic subjects measured by continuous glucose monitoring (CGM) have been limited to only few weeks. Little is known about what defines healthy or pathologic glycemic variability. This study investigated “normal glycemia” under daily conditions using a long-term implantable CGM system in nondiabetic adults for 90 days. Methods: 25 adult nondiabetic participants (10 Male, 15 Female, 17 participants >45 years old) were inserted with the Eversense® CGM System (Senseonics Inc, MD). Participants were instructed to continuously wear the CGM system during the 90 day period and calibrate the system when prompted. Analysis was performed to estimate standardized measures of euglycemia (70-180 mg/dL), hypoglycemia (<54, <70 mg/dL), hyperglycemia (>180, >200 mg/dL) and glucose variability (Mean, SD, CV). Results: Out of 25 participants, 5 had inadequate data for inclusion. Analysis showed the average glucose value of the nondiabetic participants was 102 mg/dL (SD=18 and CV=0.17). Average percent time spent in hypoglycemia <54mg/dL was 0.14% and <70mg/dL was 1.5%. Time spent in euglycemia 70-180 mg/dL was 98%. Time spent in hyperglycemia >180 and >200 mg/dL was 0.17% and 0.05%, respectively. 17 participants had at least one value < 54mg/dL and 18 participants had glucose values >180mg/dL with 11 subjects with glucose values > 200mg/dL. Conclusions: Glycemic variability recorded over extended period of time in participants without a diagnosis of diabetes showed similar results as observed by previous researchers over short duration with tight glycemic control. However, hypo- and hyperglycemic excursions are in some normoglycemic regarded individuals more frequent than suggested without CGM. Further analysis including meal challenge and fasting levels may provide guidance regarding expected values for persons without a diagnosis of diabetes and to set realistic target ranges for those with diabetes. Disclosure D. Deiss: Consultant; Self; Senseonics. Advisory Panel; Self; Abbott. Consultant; Self; Roche Diabetes Care Health and Digital Solutions. Advisory Panel; Self; Becton, Dickinson and Company. T. Abtahi: Other Relationship; Self; Senseonics. R. Rastogi: Employee; Self; Senseonics. E.L. Kelley: Employee; Self; Senseonics.

Almost 90% of the data available today was created within the last couple of years, thus Big Data set processing is of utmost importance. Many solutions have been investigated to increase processing speed and memory capacity, however I/O bottleneck is still a critical issue. To tackle this issue we adopt Sketching technique to reduce data communications. Reconstruction of the sketched matrix is performed using Orthogonal Matching Pursuit (OMP). Additionally we propose Gradient Descent OMP (GD-OMP) algorithm to reduce hardware complexity. Big data processing at real-time imposes rigid constraints on sketching kernel, hence to further reduce hardware overhead both algorithms are implemented on a low power domain specific many-core platform called Power Efficient Nano Clusters (PENC). GD-OMP algorithm is evaluated for image reconstruction accuracy and the PENC many-core architecture. Implementation results show that for large matrix sizes GD-OMP algorithm is 1.3× faster and consumes 1.4× less energy than OMP algorithm implementations. Compared to GPU and Quad-Core CPU implementations the PENC many-core reconstructs 5.4× and 9.8× faster respectively for large signal sizes with higher sparsity.

Big data processing on hardware gained immense interest among the hardware research community to take advantage of fast processing and reconfigurability. Though the computation latency can be reduced using hardware, big data processing cost is dominated by data transfers. In this article, we propose a low overhead framework based on compressive sensing (CS) to reduce data transfers up to 67% without affecting signal quality. CS has two important kernels: “sensing” and “reconstruction.” In this article, we focus on CS reconstruction is using orthogonal matching pursuit (OMP) algorithm. We implement the OMP CS reconstruction algorithm on a domain-specific PENC many-core platform and a low-power Jetson TK1 platform consisting of an ARM CPU and a K1 GPU. Detailed performance analysis of OMP algorithm on each platform suggests that the PENC many-core platform has 15× and 18× less energy consumption and 16× and 8× faster reconstruction time as compared to the low-power ARM CPU and K1 GPU, respectively. Furthermore, we implement the proposed CS-based framework on heterogeneous architecture, in which the PENC many-core architecture is used as an “accelerator” and processing is performed on the ARM CPU platform. For demonstration, we integrate the proposed CS-based framework with a hadoop MapReduce platform for a face detection application. The results show that the proposed CS-based framework with the PENC many-core as an accelerator achieves a 26.15% data storage/transfer reduction, with an execution time and energy consumption overhead of 3.7% and 0.002%, respectively, for 5,000 image transfers. Compared to the CS-based framework implementation on the low-power Jetson TK1 ARM CPU+GPU platform, the PENC many-core implementation is 2.3× faster for the image reconstruction part, while achieving 29% higher performance and 34% better energy efficiency for the complete face detection application on the Hadoop MapReduce platform.

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