AiMedics Pty. Ltd.

Eveleigh, Australia

AiMedics Pty. Ltd.

Eveleigh, Australia

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Skladnev V.N.,AiMedics Pty. Ltd. | Ghevondian N.,AiMedics Pty. Ltd. | Tarnavskii S.,AiMedics Pty. Ltd. | Paramalingam N.,University of Western Australia | And 3 more authors.
Journal of Diabetes Science and Technology | Year: 2010

Background: The aim of this study was to evaluate the performance of a prototype noninvasive alarm system (HypoMon® ) for the detection of nocturnal hypoglycemia. A prospective cohort study evaluated an alarm system that included a sensor belt, a radio frequency transmitter for chest belt signals, and a receiver. The receiver incorporated integrated "real-time" algorithms designed to recognize hypoglycemia "signatures" in the physiological parameters monitored by the sensor belt. Methods: Fifty-two children and young adults with type 1 diabetes mellitus (T1DM) participated in this blinded, prospective, in-clinic, overnight study. Participants had a mean age of 16 years (standard deviation 2.1, range 12-20 years) and were asked to follow their normal meal and insulin routines for the day of the study. Participants had physiological parameters monitored overnight by a single HypoMon system. Their BG levels were also monitored overnight at regular intervals via an intravenous cannula and read on two independent Yellow Springs Instruments analyzers. Hypoglycemia was not induced by any manipulations of diabetes management, rather the subjects were monitored overnight for "natural" occurrences of hypoglycemia. Performance analyses included comparing HypoMon system alarm times with allowed time windows associated with each hypoglycemic event. Results: The primary recognition algorithm in the prototype alarm system performed at a level consistent with expectations based on prior user surveys. The HypoMon system correctly recognized 8 out of the 11 naturally occurring overnight hypoglycemic events and falsely alarmed on 13 out of the remaining 41 normal nights [sensitivity 73% (8/11), specificity 68% (28/41), positive predictive value 38%,negative predictive value 90%]. Conclusion: The prototype HypoMon shows potential as an adjunct method for noninvasive overnight monitoring for hypoglycemia events in young people with T1DM. © Diabetes Technology Society.


Skladnev V.N.,AiMedics Pty. Ltd. | Tarnavskii S.,AiMedics Pty. Ltd. | McGregor T.,AiMedics Pty. Ltd. | Ghevondian N.,AiMedics Pty. Ltd. | And 3 more authors.
Journal of Diabetes Science and Technology | Year: 2010

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. Conclusion: Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms. © Diabetes Technology Society.


Methods and systems are described for controlling a flowrate of insulin infused into the body of a patient. An insulin infusion device infuses insulin into the body of the patient. A first sensor generates blood glucose level (BGL) data indicative of a blood glucose level of the patient. A second sensor generates autonomic nervous system (ANS) data such as heart rate data dependent on at least one parameter of the patients autonomic nervous system. A data fusion processor receives the BGL data and the ANS data and generates an output alarm signal if a hypoglycaemic event is inferred. A flowrate of insulin of the insulin infusion device may be modified dependent on the output alarm signal.


A method and system are described for detecting a hypoglycaemic state in a patient. The patients heart rate is monitored to provide a heart-rate signal. A time-lagged signal is determined as the difference between the heart-rate signal and a time-lagged version of the heart rate-signal. The heart-rate signal is filtered with a low-pass filter to provide a heart-rate trend. An absolute difference between the heart-rate signal and the heart-rate trend is determined to provide an absolute-difference signal. A second time-lagged signal is determined as a difference between the absolute-difference signal and a time-lagged version of the absolute-difference signal. The occurrence of a hypoglycaemic condition is inferred dependent on the time-lagged signal and the second time-lagged signal.


PubMed | AiMedics Pty. Ltd.
Type: Evaluation Studies | Journal: Journal of diabetes science and technology | Year: 2010

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.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.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.Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.


PubMed | AiMedics Pty. Ltd.
Type: Clinical Trial | Journal: Journal of diabetes science and technology | Year: 2010

The aim of this study was to evaluate the performance of a prototype noninvasive alarm system (HypoMon) for the detection of nocturnal hypoglycemia. A prospective cohort study evaluated an alarm system that included a sensor belt, a radio frequency transmitter for chest belt signals, and a receiver. The receiver incorporated integrated real-time algorithms designed to recognize hypoglycemia signatures in the physiological parameters monitored by the sensor belt.Fifty-two children and young adults with type 1 diabetes mellitus (T1DM) participated in this blinded, prospective, in-clinic, overnight study. Participants had a mean age of 16 years (standard deviation 2.1, range 12-20 years) and were asked to follow their normal meal and insulin routines for the day of the study. Participants had physiological parameters monitored overnight by a single HypoMon system. Their BG levels were also monitored overnight at regular intervals via an intravenous cannula and read on two independent Yellow Springs Instruments analyzers. Hypoglycemia was not induced by any manipulations of diabetes management, rather the subjects were monitored overnight for natural occurrences of hypoglycemia. Performance analyses included comparing HypoMon system alarm times with allowed time windows associated with each hypoglycemic event.The primary recognition algorithm in the prototype alarm system performed at a level consistent with expectations based on prior user surveys. The HypoMon system correctly recognized 8 out of the 11 naturally occurring overnight hypoglycemic events and falsely alarmed on 13 out of the remaining 41 normal nights [sensitivity 73% (8/11), specificity 68% (28/41), positive predictive value 38%,negative predictive value 90%].The prototype HypoMon shows potential as an adjunct method for noninvasive overnight monitoring for hypoglycemia events in young people with T1DM.

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