A NOVEL SIGNAL PROCESSING METHOD BASED ON FREQUENCY MODALITY FOR INTRA-BODY MEDICAL INSTRUMENT TRACKING

Measuring the position of a medical instrument inside the human body can be performed with various methods. One option is to measure the phase shift of the signal originating from a transmitter embedded into the tip of the medical instrument, determining its displacement with respect to a set of stationary receivers. The phase shift is converted into a low frequency voltage with the use of a Phased Locked Loop (PLL). This voltage can subsequently be converted into displacement, providing the position of the medical instrument in one (1D), two (2D) and three (3D) dimensions using trilateration. The instrument’s displacement can be defined in either the time or frequency domain. This paper presents a novel method for constant velocity displacement of the transmitter, using either the Locally Weighted Scatter-Plot Smoothing (LOWESS) curve fitting method or a Lomb-Scargle periodogram. The Lomb-Scargle periodogram is based on the least-squares power spectrum and can be used instead of waveform smoothing and measurement into the time domain, providing more precise and accurate measurement results as compared to LOWESS curve fitting method.

Abstract MP039: Relationship between Nontraditional and Standard Markers of Glycemia

Background: Glycated albumin, fructosamine, and 1,5-anhydroglucitol (1,5-AG) may be utilized to monitor glycemic control when standard markers are unavailable. However, their fasting glucose- and HbA1c-equivalents have not been characterized in a general population. Methods: We compared glycated albumin, fructosamine, 1,5-AG, HbA1c, and fasting glucose in 1,719 participants (mean age = 71yr) in a cross-sectional subsample of the Atherosclerosis Risk in Communities (ARIC) Study. We modeled the relationship between the markers with generalized linear equations, using R2 and F-statistics. We also used the Area Under the Curve (AUC) to evaluate the ability of nontraditional markers to identify people with diabetes defined by HbA1c ≥6.5%, fasting glucose≥126 mg/dL, or self-report. Results: Median fasting glucose and HbA1c were 103 mg/dL and 5.6%, respectively; 25% (n=423) of the study population reported a diabetes diagnosis. Glycated albumin, fructosamine, and 1,5-AG were more strongly associated with HbA1c (r = 0.51, 0.43, and -0.25, respectively) vs. fasting glucose (r = 0.38, 0.34, and -0.15, respectively). Models generally performed better (higher R2) in diabetic subjects (Figure). Glycated albumin was superior to fasting glucose for identification of diabetes defined by HbA1c (AUC, 0.87 vs. 0.83) or by self-report (AUC, 0.84 vs. 0.81). Conclusion: Nontraditional glycemic markers can identify people with diabetes when whole blood required for HbA1c is not available. Glycated albumin was superior to fasting glucose in identifying persons with diabetes. The models identified in this study enable comparison of these markers to HbA1c and fasting glucose on a common scale, which could be useful clinically. Figure. Scatter plots of nontraditional serum glycemic markers vs. hemoglobin A1c (%) or fasting glucose (mg/dL) overlaid with a Lowess curve (dash) and best fit model (line). Also presented are formulae for the best model in a subset of the population with diagnosed diabetes (data points and fit not shown).