title: Hypoglycemia alarm enhancement using data fusion.
abstract: BACKGROUND
  The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features.
  
  
  METHODS
  Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions.
  
  
  RESULTS
  The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels.
  
  
  CONCLUSIONS
  Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.
authors.affiliation: AiMedics Pty. Ltd., Eveleigh, New South Wales, Australia. skladnev@aimedics.com
authors.forename: Victor N
authors.initials: VN
authors.lastname: Skladnev
authors.forename: Stanislav
authors.initials: S
authors.lastname: Tarnavskii
authors.forename: Thomas
authors.initials: T
authors.lastname: McGregor
authors.forename: Nejhdeh
authors.initials: N
authors.lastname: Ghevondian
authors.forename: Steve
authors.initials: S
authors.lastname: Gourlay
authors.forename: Timothy W
authors.initials: TW
authors.lastname: Jones
mesh_terms: D000293:Adolescent; D001341:Autonomic Nervous System / Q000502:physiology; D015374:Biosensing Techniques / Q000295:instrumentation; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation / Q000379:methods; D001800:Blood Specimen Collection / Q000295:instrumentation / Q000379:methods; D002648:Child; D056902:Clinical Alarms; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000150:complications; D004526:Efficiency; D001330:Electronic Data Processing / Q000295:instrumentation / Q000379:methods*; D004868:Equipment Failure; D005260:Female; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis* / Q000209:etiology; D008297:Male; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods; D055815:Young Adult
chemical_list: D001786:Blood Glucose