Low power device with variable scheduling

Patent metadata

Field	Value
Publication number	US-9913593-B2
Application number	US-61679306-A
Country	US
Kind code	B2
Filing date	Dec 27, 2006
Priority date	Dec 27, 2006
Publication date	Mar 13, 2018
Grant date	Mar 13, 2018

Abstract

Medical device systems and methods for operating medical device systems conserve energy by efficiently managing computational demands of the systems. Signals from a subject are processed and analyzed and an estimate of a propensity for a subject to have a neurological event is determined. Based on the results of the analysis and the estimate, further analysis may be performed and the estimate may be refined. Succeeding cycles of signal measurement and analysis are scheduled depending on the results of the analysis and the estimate. The schedule may be varied temporally or with regard to the types and intensities of analyzes performed.

First claim

What is claimed:

1. A method of operating a medical device system, the method comprising:
receiving heart rate signals from a subject using a sensor coupled to the subject so as to detect the heart rate signals from the subject;
processing the heart rate signals received by the sensor using a programmed computer processing unit to detect at least one pre-onset characteristic of an epileptic event and determine a likelihood of the epileptic event based at least in part on the detected at least one pre-onset characteristic, wherein said processing the heart rate signals comprises performing a high-power processing of a first set of the heart rate signals received by the sensor; and
when the detected at least one pre-onset characteristic subsequently indicates a low likelihood of the epileptic event, performing a low-power processing of a second set of the heart rate signals received by the sensor subsequent to the first set of the heart rate signals.

2. The method as recited in claim 1 wherein said performing the low-power processing comprises determining a time to perform the low-power processing of the second set of the heart rate signals.

3. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on a charge condition of a battery associated with the medical device system.

4. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on a measurement indicative of sleep.

5. The method as recited in claim 2 wherein said determining the time to perform the low-power processing is based at least in part on the time of day.

6. The method as recited in claim 2 wherein said determining the time to perform the low-power processing further comprises selecting at least one analysis system for performing the low-power processing of additional heart rate signals based at least in part on the detection of pre-onset characteristics of the epileptic event.

7. The method as recited in claim 1 wherein processing the heart rate signals comprises performing an analysis on the heart rate signals to generate an estimated propensity for the subject to have the epileptic event within a specified time horizon.

8. The method as recited in claim 7 wherein said performing the analysis to generate the estimated propensity comprises generating an estimated time horizon during which an estimated likelihood that the subject will have the epileptic event is below a predetermined threshold.

9. The method as recited in claim 8 wherein said performing the low-power processing of the second set of the heart rate signals comprises determining a scheduled time to perform the low-power processing, the determination of the scheduled time being based on the estimated time horizon.

10. The method as recited in claim 9 wherein the scheduled time is between one half and one hundredth of the estimated time horizon.

11. The method as recited in claim 7 wherein the performing the analysis on the heart rate signals to generate the estimated propensity comprises:
performing a first analysis to estimate a first propensity for the subject to have the epileptic event; and
performing a second analysis to estimate a second propensity for the subject to have the epileptic event when the first estimate meets one or more specified criteria.

12. The method as recited in claim 1 wherein:
said low-power processing of the second set of the heart rate signals comprises processing the second set of the heart rate signals using a first analysis having a first computational demand level; and
said high-power processing of signals comprises processing the first set of the heart rate signals using a second analysis having a second computational demand level,
wherein the second computational demand level is greater than the first computational demand level.

13. The method as recited in claim 1 wherein:
said low-power processing the second set of the heart rate signals comprises processing the second set of the heart rate signals according to a first processing frequency; and
said high-power processing of the second set of the heart rate signals comprises processing the second set of the heart rate signals according to a second processing frequency,
wherein the second processing frequency is greater than the first processing frequency.

14. The method as recited in claim 1 wherein the likelihood of the epileptic event is a continuum extending from a minimum probability to a maximum probability, the low likelihood of the epileptic event corresponding to the minimum probability.

15. The method as recited in claim 1 further comprising:
performing a high-power processing of a third set of the heart rate signals received by the sensor subsequent to the second set of the heart rate signals when the detected at least one pre-onset characteristic subsequently indicates a high likelihood of the epileptic event.

16. The method as recited in claim 15 wherein the likelihood of the epileptic event is a continuum extending from a minimum probability to a maximum probability, the low likelihood of the epileptic event corresponding to the minimum probability, the high likelihood of the epileptic event corresponding to the maximum probability.

17. A method comprising:
receiving electrical signals from a subject using a heart rate sensor coupled to the subject so as to detect heart rate signals from the subject;
processing the electrical signals received by the heart rate sensor using a programmed computer processing unit to detect at least one pre-onset characteristic of an epileptic seizure and determine a likelihood of seizure based at least in part on the detected at least one pre-onset characteristic, wherein said processing the electrical signals comprises performing a high-power processing of a first set of the electrical signals received by the heart rate sensor; and
when the detected at least one pre-onset characteristic indicates a low likelihood of seizure, performing a low-power processing of a second set of the electrical signals received by the heart rate sensor subsequent to the first set of the electrical signals.

18. The method as recited in claim 17 wherein said processing the electrical signals comprises performing an analysis on the electrical signals to estimate a propensity for the subject to have an epileptic seizure within a specified time horizon.

19. The method as recited in claim 18 further comprising communicating information related to the estimate of a propensity to the subject.

20. The method as recited in claim 18 wherein performing the analysis to estimate the propensity comprises estimating a time horizon during which an estimated likelihood that the subject will have an epileptic seizure is below a predetermined threshold.

21. The method as recited in claim 17 wherein processing the electrical signals comprises applying at least one feature extractor to the electrical signals.

22. The method as recited in claim 17 wherein:
said low-power processing of the second set of the electrical signals comprises processing the second set of the electrical signals using a first analysis having a first computational demand level; and
said high-power processing of the first set of the electrical signals comprises processing the first set of the electrical signals using a second analysis having a second computational demand level,
wherein the second computational deman

Assignees

• Leyde Kent
• Harris John
• Cyberonics Inc

Inventors

• Leyde Kent
• Harris John

Classifications

• A61B5/4094 Primary

  Diagnosing or monitoring seizure diseases, e.g. epilepsy · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title

• A61B2560/0209

  adapted for power saving · CPC title

• G06F19/3406

  Physics · mapped topic

• A61B5/0006 Primary

  ECG or EEG signals · CPC title