Icu Medical Inc.

Medtronic Minimed, Inc.

Cardionet, Inc.

Michael Kremliovsky

Anatoly S Belkin

William Kenneth Day

Steve Joseph Lindo

Robert P Cousineau

Gary Mills

Justin Joseph Schmidt

Paul John Foryt

Sumant Ramachandra

Rebecca K Gottlieb

Jefferson E Rose

Chia-Hung Chiu

Ying Luo

Meena Ramachandran

Nandita Dangui-Patel

Anirban Roy

Lev Korzinov

Barry Keenan

Nandita Dangui Patel

Chia Hung Chiu

Anatoly Belkin

William Day

Justin Schmidt

Steve Lindo

Paul Foryt

A patient care system is disclosed that includes a medical device such as an infusion pump. The medical device generates a data message containing information such as the status of the therapy being delivered, operating data or both. An alarm generating system assesses the data message from the pump and generates an alarm message if certain conditions established by a first set of rules are met. The alarm message is assessed according to a second set of rules as to whether to suppress the alarm message. The data message contains a required input for both the first and second algorithms. A dispatching system is adapted to forward the alarm message to an alarm destination according to a third set of rules. The alarm destination expresses an alarm upon receipt of the alarm message.

Patient care system with conditional alarm forwarding

Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, a blood-glucose concentration in a patient may be determined based, at least in part, on one or more probability models, one or more functions for estimating blood-glucose concentrations, and/or blood glucose reference sample-sensor measurement pairs.

Calibration of glucose monitoring sensor and/or insulin delivery system

Disclosed are methods, apparatuses, etc. for glucose sensor signal stability analysis. In certain example embodiments, a series of samples of at least one sensor signal that is responsive to a blood glucose level of a patient may be obtained. Based at least partly on the series of samples, at least one metric may be determined to assess an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time. A reliability of the at least one sensor signal to respond to the blood glucose level of the patient may be assessed based at least partly on the at least one metric assessing the underlying trend. Other example embodiments are disclosed herein.

Glucose sensor signal stability analysis

Disclosed are methods, apparatuses, etc. for glucose sensor signal stability analysis. In certain example embodiments, a series of samples of at least one sensor signal that is responsive to a blood glucose level of a patient may be obtained. Based at least partly on the series of samples, at least one metric may be determined to assess an underlying trend of a change in responsiveness of the at least one sensor signal to the blood glucose level of the patient over time. A reliability of the at least one sensor signal to respond to the blood glucose level of the patient may be assessed based at least partly on the at least one metric assessing an underlying trend. Other example embodiments are disclosed herein.

Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, one or more parameters of a probability model may be estimated based on blood glucose reference sample-sensor measurement pairs. Based on such information, function(s) for estimating a blood-glucose concentration in a patient may be determined.

Systems and techniques relating to monitoring physiological activity using partial state space reconstruction. In general, in one aspect, a partial state space is produced using an orthogonal, frequency-independent transform, such as Hilbert transform. The partial state space can then be analyzed using state space techniques to identify physiological information for the biological system. The described techniques can be implemented in a distributed cardiac activity monitoring system, including a cardiac monitoring apparatus, and a QRS detector thereof.

Monitoring physiological activity using partial state space reconstruction

Systems and techniques relating to locating cardiac wave forms in a cardiac signal, and to detecting a physiological condition, such as ventricular fibrillation. In general, in one aspect, a machine-implemented method includes obtaining a sensed cardiac signal of an organism, the sensed cardiac signal comprising a time series x(t); applying a Hilbert (H) transform to the time series x(t) to obtain H(x(t)), wherein x(t) and H(x(t)) together forming a partial state space trajectory; determining a speed of trajectory, for the sensed cardiac signal, from the partial state space trajectory; and identifying physiological information concerning the organism based on a combination of first and second signal elements, the first signal element including a phase property or an amplitude property of the speed of trajectory, and the second signal element including an amplitude property of the partial state space trajectory.

Automated analysis of a cardiac signal based on dynamical characteristics of the cardiac signal

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Poway, CA, United States of America

Michael Kremliovsky