Prediction and monitoring of clinical episodes

• US 8,403,865 B2
• Filed: 07/25/2007
• Issued: 03/26/2013
• Est. Priority Date: 02/05/2004
• Status: Active Grant

• Current Assignee: Hill-Rom Services Incorporated (Baxter International Inc.)
• Original Assignee: EarlySense Ltd.
• Inventors: Halperin, Avner, Lange, Daniel H., Gross, Yosef, Pinhas, Itzhak
• Primary Examiner(s): Lavert, Nicole F
• Application Number:
  US11/782,750
• Publication Number:
  US 20080269625A1
• Time in Patent Office: 2,071 Days
• Field of Search: 600/484, 600/529, 600/534
• US Class Current: 600/584
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/704 Tables
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61N 1/3956 Implantable devices for applying electric shocks to the heart, e.g. for cardioversion
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method is provided for predicting an onset of an asthma attack. The method includes sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing, and predicting the onset of the asthma attack at least in part responsively to the sensed parameter. Also provided is a method for predicting an onset of an episode associated with congestive heart failure (CHF), including sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing, and predicting the onset of the episode at least in part responsively to the sensed parameter. Other embodiments are also described.

Claims

1. A method for predicting an onset of an asthma attack, comprising:

sensing, using a single sensor, at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

using a pattern-analysis module, predicting the onset of the asthma attack at least in part responsively to the sensed parameter.

2. The method according to claim 1, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein sensing the at least one parameter comprises sensing the at least one motion-related parameter.

3. The method according to claim 1, wherein predicting the onset comprises predicting the onset before the subject or a caretaker of the subject becomes aware of the onset.

4. The method according to claim 1, comprising determining a measure of coughing of the subject, wherein predicting the onset comprises predicting the onset at least in part responsively to the sensed parameter and the measure of coughing.

5. The method according to claim 1, wherein sensing the at least one parameter comprises sensing the at least one parameter without requiring human compliance.

6. The method according to claim 1, wherein sensing the at least one parameter comprises sensing the at least one parameter while the subject is sleeping.

7. The method according to claim 1, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and wherein sensing the at least one parameter comprises sensing the at least one breathing-related parameter.

8. The method according to claim 7, wherein predicting the onset comprises:

determining at least one breathing pattern of the subject responsively to the sensed breathing-related parameter;

comparing the breathing pattern with a baseline breathing pattern; and

predicting the onset at least in part responsively to the comparison.

9. The method according to claim 1, wherein sensing the at least one parameter comprises measuring a pressure in, on, or under a reclining surface upon which the subject lies.

10. The method according to claim 9, wherein sensing the at least one parameter comprises measuring the pressure using exactly one sensor placed in, on, or under the reclining surface.

11. A method for predicting an onset of an episode associated with congestive heart failure (CHF), comprising:

sensing, using a single sensor, at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

using a pattern-analysis module, predicting the onset of the episode at least in part responsively to the sensed parameter.

12. The method according to claim 11, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein sensing the at least one parameter comprises sensing the at least one motion-related parameter.

13. The method according to claim 11, wherein sensing the at least one parameter comprises measuring a pressure in, on, or under a reclining surface upon which the subject lies.

14. The method according to claim 11, wherein sensing the at least one parameter comprises sensing the at least one parameter while the subject is sleeping.

15. The method according to claim 11, wherein sensing the at least one parameter comprises sensing the at least one parameter without requiring human compliance.

16. A method for predicting an onset of a clinical episode, comprising:

sensing, using a single sensor, at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

using a pattern-analysis module, predicting the onset at least in part responsively to the sensed parameter.

17. The method according to claim 16, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein sensing the at least one parameter comprises sensing the at least one motion-related parameter without requiring human compliance.

18. The method according to claim 16, wherein the subject is hospitalized, and comprising alerting a healthcare worker upon the predicting.

19. The method according to claim 16, wherein sensing the at least one parameter without requiring human compliance comprises measuring a pressure in, on, or under a reclining surface upon which the subject lies.

20. The method according to claim 16, wherein the clinical episode includes an asthma attack, and wherein predicting the onset comprises predicting the onset of the asthma attack.

21. The method according to claim 16, wherein the clinical episode includes an episode associated with congestive heart failure (CHF) of the subject, and wherein predicting the onset comprises predicting the onset of the episode associated with the CHF.

22. The method according to claim 16, wherein the clinical episode includes an episode of hypoglycemia caused by diabetes, and wherein predicting the onset comprises predicting the onset of the episode of hypoglycemia.

23. The method according to claim 16, wherein the clinical episode is selected from the list consisting of:

an episode of abnormal autonomic nervous system activity caused by a neurological condition, an epileptic seizure, an episode of Periodic Limb Movements in Sleep (PLMS), a stroke, an episode of essential tremor, an episode of stress, an episode of fibrillation, an episode associated with chronic obstructive pulmonary disease (COPD), an episode associated with cystic fibrosis (CF), and an episode of anaphylactic shock, and wherein predicting the onset comprises predicting the onset of the selected clinical episode.

24. The method according to claim 16, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and wherein sensing the at least one parameter comprises sensing the at least one breathing-related parameter without requiring human compliance.

25. The method according to claim 24, wherein predicting the onset comprises:

determining at least one breathing pattern of the subject responsively to the sensed breathing-related parameter;

comparing the breathing pattern with a baseline breathing pattern; and

predicting the onset at least in part responsively to the comparison.

26. Apparatus for predicting an onset of an asthma attack, comprising:

a single non-contact sensor, adapted to sense at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

a control unit, adapted to predict the onset of the asthma attack at least in part responsively to the sensed parameter.

27. The apparatus according to claim 26, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein the sensor is adapted to sense the at least one motion-related parameter.

28. The apparatus according to claim 26, wherein the control unit is adapted to predict the onset before the subject or a caretaker of the subject becomes aware of the onset.

29. The apparatus according to claim 26, wherein the control unit is adapted to determine a measure of coughing of the subject, and to predict the onset at least in part responsively to the sensed parameter and the measure of coughing.

30. The apparatus according to claim 26, wherein the sensor is adapted to sense the at least one parameter without requiring human compliance.

31. The apparatus according to claim 26, wherein the sensor is adapted to sense the at least one parameter while the subject is sleeping.

32. The apparatus according to claim 26, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and the sensor is adapted to sense the at least one breathing-related parameter.

33. The apparatus according to claim 32, wherein the control unit is adapted to:

determine at least one breathing pattern of the subject responsively to the sensed breathing-related parameter,

compare the breathing pattern with a baseline breathing pattern, and

predict the onset at least in part responsively to the comparison.

34. The apparatus according to claim 26, wherein the sensor comprises a pressure gauge, configured to measure a pressure in, on, or under a reclining surface upon which the subject lies.

35. The apparatus according to claim 34, wherein the pressure gauge comprises exactly one pressure gauge.

36. Apparatus for predicting an onset of an episode associated with congestive heart failure (CHF), comprising:

a single non-contact sensor, adapted to sense at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

a control unit, adapted to predict the onset of the episode at least in part responsively to the sensed parameter.

37. The apparatus according to claim 36, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein the sensor is adapted to sense the at least one motion-related parameter.

38. The apparatus according to claim 36, wherein the sensor comprises a pressure gauge, configured to measure a pressure in, on, or under a reclining surface upon which the subject lies.

39. The apparatus according to claim 36, wherein the sensor is adapted to sense the at least one parameter while the subject is sleeping.

40. The apparatus according to claim 36, wherein the sensor is adapted to sense the at least one parameter without requiring human compliance.

41. Apparatus for predicting an onset of a clinical episode, comprising:

a single non-contact sensor, adapted to sense at least one parameter of a subject without contacting the subject or clothes the subject is wearing, and without performing viewing; and

a control unit, adapted to predict the onset at least in part responsively to the sensed parameter.

42. The apparatus according to claim 41, wherein the at least one parameter includes at least one motion-related parameter of the subject, and wherein the sensor is adapted to sense the at least one motion-related parameter without requiring human compliance.

43. The apparatus according to claim 41, wherein the sensor is configured to be coupled to a hospital bed, and comprising an alert interface, wherein the system is configured to drive the alert interface to generate an alert upon a prediction of the onset.

44. The apparatus according to claim 41, wherein the sensor comprises a pressure gauge, configured to measure a pressure in, on, or under a reclining surface upon which the subject lies.

45. The apparatus according to claim 41, wherein the clinical episode includes an asthma attack, and wherein the control unit is adapted to predict the onset of the asthma attack.

46. The apparatus according to claim 41, wherein the clinical episode includes an episode associated with congestive heart failure (CHF) of the subject, and wherein the control unit is adapted to predict the onset of the episode associated with the CHF.

47. The apparatus according to claim 41, wherein the clinical episode includes an episode of hypoglycemia caused by diabetes, and wherein the control unit is adapted to predict the onset of the episode of hypoglycemia.

48. The apparatus according to claim 41, wherein the clinical episode is selected from the list consisting of:

an episode of abnormal autonomic nervous system activity caused by a neurological condition, an epileptic seizure, an episode of Periodic Limb Movements in Sleep (PLMS), a stroke, an episode of essential tremor, an episode of stress, an episode of fibrillation, an episode associated with chronic obstructive pulmonary disease (COPD), an episode associated with cystic fibrosis (CF), and an episode of anaphylactic shock, and wherein the control unit is adapted to predict the onset of the selected clinical episode.

49. The apparatus according to claim 41, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and wherein the sensor is adapted to sense the at least one breathing-related parameter without requiring human compliance.

50. The apparatus according to claim 49, wherein the control unit is adapted to:

determine at least one breathing pattern of the subject responsively to the sensed breathing-related parameter,

compare the breathing pattern with a baseline breathing pattern, and

predict the onset at least in part responsively to the comparison.