Prediction and monitoring of clinical episodes

• US 8,992,434 B2
• Filed: 04/01/2014
• Issued: 03/31/2015
• 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, Pinhas, Itzhak, Gross, Yosef
• Primary Examiner(s): Lavert, Nicole F
• Application Number:
  US14/231,855
• Publication Number:
  US 20140207204A1
• Time in Patent Office: 364 Days
• Field of Search: 600508-509, 600/515, 600/529
• US Class Current: 600/508
• 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

Apparatus and methods are described, including a method for treating a clinical episode. The method comprises sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing, analyzing the parameter, detecting the clinical episode at least in part responsively to the analysis, and responsively to detecting the clinical episode, treating the clinical episode using a device implanted in the subject. Other applications are also described.

Claims

1. A method comprising:

sensing a motion-related parameter of a subject without contacting or viewing the subject or clothes the subject is wearing;

deriving heartbeat- and breathing-related signals from the motion-related parameter; and

demodulating the heartbeat-related signal using the breathing-related signal.

2. The method according to claim 1, wherein demodulating the heartbeat-related signal comprises multiplying the heartbeat-related signal by the breathing-related signal.

3. The method according to claim 1, wherein deriving the heartbeat- and breathing-related signals comprises filtering the motion-relating signals at heartbeat- and breathing-related frequency ranges, respectively.

4. The method according to claim 3, wherein the heartbeat-related frequency range is between 0.8 and 5 Hz.

5. The method according to claim 3, wherein the breathing-related frequency range is between 0.05 and 0.8 Hz.

6. A method for treating a subject, comprising:

sensing a motion-related parameter of a subject without contacting or viewing the subject or clothes the subject is wearing;

analyzing the parameter;

responsively at least in part to the analysis, determining an initial dosage of a drug for administration to the subject; and

communicating the initial dosage to a drug administration device used by the subject.

7. The method according to claim 6, wherein analyzing the parameter comprises:

analyzing a clinical effect of the drug administered by the drug administration device at the communicated initial dosage;

responsively to the analysis, determining an updated dosage of the drug, which updated dosage is different from the initial dosage; and

communicating the updated dosage to the drug administration device.

8. A method for treating a clinical episode, comprising:

sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing;

analyzing the parameter;

detecting the clinical episode at least in part responsively to the analysis; and

responsively to detecting the clinical episode, treating the clinical episode using a device implanted in the subject.

9. A method comprising:

sensing at least one parameter of a subject, using exactly one sensor placed in or under a reclining surface upon which the subject lies, which sensor does not contact the subject or clothes the subject is wearing; and

detecting coughing of the subject by analyzing the parameter.

10. The method according to claim 9, 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.

11. A method for determining a heart rate of a subject, comprising:

sensing a first pulse signal of the subject in a vicinity of a first location of the subject selected from;

a chest of the subject, and an abdomen of the subject, without contacting the subject or clothes the subject is wearing;

sensing a second pulse signal of the subject in a vicinity of a second location of the subject anatomically below a waist of the subject, without contacting the subject or clothes the subject is wearing; and

determining the heart rate responsively to the first and second pulse signals.

12. The method according to claim 11, wherein determining the heart rate comprises calculating a cross correlation signal of the first and second pulse signals, and determining the heart rate to be a frequency of the cross correlation signal.

13. The method according to claim 12, wherein the second location of the subject includes a location in a vicinity of legs of the subject.

14. The method according to claim 12, wherein sensing the first and second pulse signals comprises sensing the first and second pulse signals without contacting or viewing the subject or clothes the subject is wearing.

15. The method according to claim 12,

wherein sensing the first pulse signal comprises sensing a first motion-related parameter of the subject in the vicinity of the first location, and deriving the first pulse signal from the first motion-related parameter, and

wherein sensing the second pulse signal comprises sensing a second motion-related parameter of the subject in the vicinity of the second location, and deriving the second pulse signal from the second motion-related parameter.

16. The method according to claim 12, comprising predicting an onset of a clinical episode responsively to the heart rate.

17. A method comprising:

sensing first and second motion-related parameters of a subject in a vicinity of a first site and in a vicinity of a second site of the subject, respectively, without contacting or viewing the subject or clothes the subject is wearing;

deriving first and second breathing-related signals from the first and second motion-related parameters, respectively; and

analyzing the first and second breathing-related signals to determine a measure of thoracoabdominal asynchrony (TAA) of the subject.

18. The method according to claim 17, wherein analyzing the first and second breathing-related signals comprises calculating a phase shift between the first and second breathing-related signals.

19. The method according to claim 17, wherein the first site includes lungs of the subject, wherein the second site includes a lower abdomen of the subject, and wherein sensing comprises sensing the first and second motion-related parameters in the vicinity of the lungs and lower abdomen, respectively.

20. A method comprising:

sensing first and second motion-related parameters of a subject in a vicinity of a first site and in a vicinity of a second site of the subject, respectively, without contacting or viewing the subject or clothes the subject is wearing;

deriving first and second breathing-related signals from the first and second motion-related parameters, respectively; and

analyzing the first and second breathing-related signals to determine a measure of accessory muscle activity of the subject.

21. The method according to claim 20, wherein analyzing the first and second breathing-related signals comprises calculating a ratio of the first and second breathing-related signals.

22. The method according to claim 20, wherein the first site includes lungs of the subject, wherein the second site includes a lower abdomen of the subject, and wherein sensing comprises sensing the first and second motion-related parameters in the vicinity of the lungs and lower abdomen, respectively.

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

sensing at least one parameter of a subject; and

while a breathing rate of the subject is less than 120% of a baseline asymptomatic breathing rate of the subject, predicting the onset of the clinical episode at least in part responsively to the sensed parameter.

24. The method according to claim 23, wherein sensing the at least one parameter comprises sensing the at least one parameter without contacting or viewing the subject or clothes the subject is wearing.

25. The method according to claim 23, wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the clinical episode comprises predicting the onset of the asthma attack.

26. The method according to claim 23, wherein predicting the onset comprises predicting the onset while the breathing rate is less than 110% of the baseline asymptomatic breathing rate.

27. The method according to claim 26, wherein predicting the onset comprises predicting the onset while the breathing rate is less than 105% of the baseline asymptomatic breathing rate.

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

sensing at least one parameter of a subject; and

while a forced expiratory volume in one second (FEV1) of the subject is greater than 90% of a baseline asymptomatic FEV1 of the subject, predicting the onset of the asthma attack at least in part responsively to the sensed parameter.

29. The method according to claim 28, wherein sensing the at least one parameter comprises sensing the at least one parameter without contacting or viewing the subject or clothes the subject is wearing.

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

sensing at least one parameter of a subject without contacting or viewing the subject or clothes the subject is wearing; and

at least one hour prior to the onset of the clinical episode, predicting the onset at least in part responsively to the sensed parameter.

31. The method according to claim 30, wherein sensing the at least one parameter comprises sensing at least two parameters of the subject without contacting or viewing the subject or clothes the subject is wearing, and wherein predicting the onset comprises predicting the onset at least in part responsively to the sensed parameters.

32. The method according to claim 30, wherein predicting the onset comprises predicting the onset at least four hours prior to the onset.

33. The method according to claim 30, wherein sensing the at least one parameter comprises sensing the at least one parameter substantially continuously for a period having a duration of at least one hour.

34. The method according to claim 30, wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the clinical episode comprises predicting the onset of the asthma attack.

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

sensing at least one parameter of a subject substantially continuously during a period having a duration of at least one hour; and

at least one hour prior to the onset of the clinical episode, predicting the onset at least in part responsively to the sensed parameter.

36. The method according to claim 35, wherein sensing the at least one parameter comprises sensing the at least one parameter without contacting or viewing the subject or clothes the subject is wearing.

37. The method according to claim 35, wherein sensing the at least one parameter comprises sensing at least two parameters of the subject substantially continuously during the period, and wherein predicting the onset comprises predicting the onset at least in part responsively to the sensed parameters.

38. The method according to claim 35, wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the clinical episode comprises predicting the onset of the asthma attack.

39. The method according to claim 35, wherein predicting the onset comprises predicting the onset at least four hours prior to the onset.

40. The method according to claim 35, wherein the period has a duration of at least four hours, and wherein sensing the at least one parameter comprises sensing the at least one parameter substantially continuously during the period having the duration of at least four hours.

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

sensing at least one parameter of a subject substantially continuously during at least 80% of a period of time a subject is sleeping at night; and

at least in part responsively to the sensed parameter, predicting the onset of the clinical episode at least one hour prior to the onset.

42. The method according to claim 41, wherein sensing the at least one parameter comprises sensing at least two parameters of the subject substantially continuously during the 80% of the period, and wherein predicting the onset comprises predicting the onset at least in part responsively to the sensed parameters.

43. The method according to claim 41, wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the clinical episode comprises predicting the onset of the asthma attack.

44. The method according to claim 41, wherein sensing the at least one parameter comprises sensing the at least one parameter without contacting or viewing the subject or clothes the subject is wearing.

45. A method comprising:

sensing at least one parameter of a subject; and

at least in part responsively to the sensed parameter, calculating a probability that a clinical episode will occur within a predetermined period of time after the calculating.

46. The method according to claim 45, wherein sensing the at least one parameter comprises sensing at least two parameters of the subject, and wherein calculating a probability comprises calculating the probability at least in part responsively to the sensed parameters.

47. The method according to claim 45, comprising notifying the subject if the probability exceeds a threshold value.

48. The method according to claim 45, wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the clinical episode comprises predicting the onset of the asthma attack.

49. The method according to claim 45, wherein sensing the at least one parameter comprises sensing the at least one parameter without contacting or viewing the subject or clothes the subject is wearing.

50. The method according to claim 45, wherein calculating the probability comprises calculating the probability at least in part responsively to data including a population-based average of the parameter.

51. The method according to claim 45, wherein calculating the probability comprises generating subject-specific data regarding the parameter by analyzing previous occurrences of the clinical episode, and calculating the probability at least in part responsively to the data.

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

a sensor, adapted to acquire a breathing-related time-domain signal of a subject; and

a control unit, adapted to;

transform the time-domain signal into a frequency-domain signal,

determine a breathing rate of the subject by identifying a peak in a breathing-related frequency range of the frequency-domain signal,

determine one or more harmonics of the peak frequency,

determine a relationship between;

(a) a first energy level, selected from the list consisting of;

an energy level associated with one of the one or more harmonics, and an energy level associated with a frequency of the peak, and

(b) a second energy level, associated with one of the one or more harmonics;

compare the relationship with a baseline level of the relationship, and predict the onset of the episode at least in part responsively to the comparison.

53. Apparatus for monitoring blood pressure, comprising:

a non-contact sensor, adapted to sense a motion-related parameter of a subject without contacting the subject or clothes the subject is wearing; and

a control unit, adapted to analyze the parameter to determine a measure of blood pressure of the subject.

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

a sensor, adapted to sense at least one parameter of a subject; and

a control unit, adapted to;

analyze the parameter,

receive data regarding a drug administered to the subject, and

predict the onset of a clinical episode at least in part responsively to the analysis and the drug administration data in combination.

55. The apparatus according to claim 54, comprising a drug administration device adapted to administer the drug to the subject, wherein the control unit is adapted to receive the drug administration data from the drug administration device.

56. Apparatus comprising:

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

a control unit, adapted to analyze the parameter to determine a measure of restlessness of the subject.

57. Apparatus comprising:

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

a control unit, adapted to detect Periodic Limb Movements in Sleep (PLMS) of the subject by analyzing the parameter.

58. Apparatus for detecting pulsus paradoxus of a subject, comprising:

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

a control unit, adapted to;

analyze the parameter to generate a measure of blood pressure change of the subject over an inspiration/expiration cycle,

compare the measure of blood pressure change to a threshold, and

responsively to the measure being greater than the threshold, detect the pulsus paradoxus.