Techniques for prediction and monitoring of respiration-manifested clinical episodes
View Patent ↗A method is provided for predicting an onset of a clinical episode, the method including sensing breathing of a subject, determining at least one breathing pattern of the subject responsively to the sensed breathing, comparing the breathing pattern with a baseline breathing pattern, and predicting the onset of the episode at least in part responsively to the comparison. Other embodiments are also described.
1. A method for predicting an onset of a clinical episode, comprising:
sensing breathing of a subject;
determining at least one breathing rate pattern of the subject responsively to the sensed breathing;
comparing the breathing rate pattern with a baseline breathing rate pattern; and
predicting the onset of the episode at least in part responsively to the comparison,
wherein the clinical episode includes an episode associated with a condition selected from the list consisting of: chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), congestive heart failure (CHF), diabetes, and epilepsy.
2. The method according to claim 1 , wherein the condition includes CHF, and wherein the clinical episode includes the episode associated with CHF.
3. The method according to claim 2 , wherein comparing comprises determining the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
4. The method according to claim 2 , wherein comparing comprises setting the baseline breathing rate pattern responsively to a population average breathing rate pattern.
5. The method according to claim 2 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject while the subject is sleeping, and wherein determining the breathing rate pattern comprises:
analyzing the body motion data to determine a breathing-related movement pattern; and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
6. The method according to claim 5 , wherein acquiring the body motion data comprises acquiring the body motion data without contacting the subject or clothes the subject is wearing.
7. The method according to claim 2 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject by measuring a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, a pressure under a mattress upon which the subject lies, and a pressure under a mattress covering upon which the subject lies.
8. The method according to claim 1 , wherein the condition includes diabetes, and wherein the clinical episode includes the episode associated with diabetes.
9. The method according to claim 8 , wherein comparing comprises determining the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
10. The method according to claim 8 , wherein comparing comprises setting the baseline breathing rate pattern responsively to a population average breathing rate pattern.
11. The method according to claim 8 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject while the subject is sleeping, and wherein determining the breathing rate pattern comprises:
analyzing the body motion data to determine a breathing-related movement pattern; and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
12. The method according to claim 11 , wherein acquiring the body motion data comprises acquiring the body motion data without contacting the subject or clothes the subject is wearing.
13. The method according to claim 8 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject by measuring a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, a pressure under a mattress upon which the subject lies, and a pressure under a mattress covering upon which the subject lies.
14. The method according to claim 1 , wherein the condition includes COPD, and wherein the clinical episode includes the episode associated with COPD.
15. The method according to claim 1 , wherein the condition includes CF, and wherein the clinical episode includes the episode associated with CF.
16. The method according to claim 1 , wherein the condition includes epilepsy, and wherein the clinical episode includes the episode associated with epilepsy.
17. The method according to claim 1 ,
wherein the breathing rate pattern includes a breathing rate variability pattern,
wherein the baseline breathing rate pattern includes a baseline breathing rate variability pattern, and
wherein predicting the onset comprises predicting the onset responsively to a decrease in breathing rate variability over time compared to the baseline breathing rate variability pattern.
18. The method according to claim 1 ,
wherein determining the at least one breathing rate pattern comprises determining a breathing rate variability pattern and a slow trend breathing rate pattern,
wherein comparing the breathing rate pattern with the baseline breathing rate pattern comprises comparing the breathing rate variability pattern with the baseline breathing rate variability pattern, and comparing the slow trend breathing rate pattern with a baseline slow trend breathing rate pattern, and
wherein predicting the onset comprises predicting the onset responsively to both comparisons.
19. The method according to claim 1 , wherein the breathing rate pattern and the baseline breathing rate pattern include respective slow trend breathing rate patterns, and wherein comparing the breathing rate pattern with the baseline breathing rate pattern comprises comparing the slow trend breathing rate pattern with the baseline slow trend breathing rate pattern.
20. The method according to claim 19 , wherein the baseline slow trend breathing rate pattern includes a monotonic decline in breathing rate over at least 1 hour, and wherein predicting the onset comprises predicting the onset responsively to a difference between the slow trend breathing rate pattern and the monotonic decline in breathing rate.
21. A method for predicting an onset of a clinical episode, comprising:
sensing breathing of a subject;
determining at least one breathing rate pattern of the subject responsively to the sensed breathing;
comparing the breathing rate pattern with a baseline breathing rate pattern; and
predicting the onset of the episode at least in part responsively to the comparison,
wherein the clinical episode includes an asthma attack, and wherein predicting the onset of the episode comprises predicting the onset of the asthma attack.
22. The method according to claim 21 , wherein comparing comprises determining the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
23. The method according to claim 21 , wherein comparing comprises setting the baseline breathing rate pattern responsively to a population average breathing rate pattern.
24. The method according to claim 21 , wherein sensing breathing of the subject comprises sensing at least one type of breathing-related mechanical vibrations selected from the list consisting of: a sound caused by wheezing, a sound caused by coughing, mechanical vibrations caused by wheezing, and mechanical vibrations caused by coughing, and wherein predicting the onset comprises predicting the onset responsively to an aspect of the breathing-related mechanical vibrations.
25. The method according to claim 21 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject while the subject is sleeping, and wherein determining the breathing rate pattern comprises:
analyzing the body motion data to determine a breathing-related movement pattern; and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
26. The method according to claim 25 , wherein acquiring the body motion data comprises acquiring the body motion data without contacting the subject or clothes the subject is wearing.
27. The method according to claim 1 , wherein sensing the breathing comprises acquiring breathing-related body motion data of the subject by measuring a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, a pressure under a mattress upon which the subject lies, and a pressure under a mattress covering upon which the subject lies.
28. The method according to claim 21 ,
wherein the breathing rate pattern includes a breathing rate variability pattern,
wherein the baseline breathing rate pattern includes a baseline breathing rate variability pattern, and
wherein predicting the onset comprises predicting the onset responsively to a decrease in breathing rate variability over time compared to the baseline breathing rate variability pattern.
29. The method according to claim 21 ,
wherein determining the at least one breathing rate pattern comprises determining a breathing rate variability pattern and a slow trend breathing rate pattern,
wherein comparing the breathing rate pattern with the baseline breathing rate pattern comprises comparing the breathing rate variability pattern with the baseline breathing rate variability pattern, and comparing the slow trend breathing rate pattern with a baseline slow trend breathing rate pattern, and
wherein predicting the onset comprises predicting the onset responsively to both comparisons.
30. The method according to claim 21 , wherein the breathing rate pattern and the baseline breathing rate pattern include respective slow trend breathing rate patterns, and wherein comparing the breathing rate pattern with the baseline breathing rate pattern comprises comparing the slow trend breathing rate pattern with the baseline slow trend breathing rate pattern.
31. The method according to claim 30 , wherein the baseline slow trend breathing rate pattern includes a monotonic decline in breathing rate over at least 1 hour, and wherein predicting the onset comprises predicting the onset responsively to a difference between the slow trend breathing rate pattern and the monotonic decline in breathing rate.
32. Apparatus for predicting an onset of a clinical episode, comprising:
a breathing sensor, adapted to sense breathing of a subject, and to generate a signal responsively thereto; and
a control unit, adapted to:
receive the signal,
determine at least one breathing rate pattern of the subject responsive to the signal,
compare the breathing rate pattern with a baseline breathing rate pattern, and
predict the onset of the episode at least in part responsively to the comparison,
wherein the clinical episode includes an episode associated with a condition selected from the list consisting of: chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), congestive heart failure (CHF), diabetes, and epilepsy.
33. The apparatus according to claim 32 , wherein the condition includes CHF, and wherein the clinical episode includes the episode associated with CHF.
34. The apparatus according to claim 33 , wherein the control unit is adapted to determine the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
35. The apparatus according to claim 33 , wherein the control unit is adapted to set the baseline breathing rate pattern responsively to a population average breathing rate pattern.
36. The apparatus according to claim 33 , wherein the breathing sensor comprises a motion sensor, adapted to sense breathing-related body motion of the subject indicative of the breathing, and wherein the control unit is adapted to determine the breathing rate pattern by:
analyzing the body motion data to determine a breathing-related movement pattern, and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
37. The apparatus according to claim 36 , wherein the breathing sensor is adapted to acquire the body motion data without contacting the subject or clothes the subject is wearing.
38. The apparatus according to claim 33 , wherein the breathing sensor comprises a pressure gauge configured to measure a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, and a pressure under a mattress upon which the subject lies.
39. The apparatus according to claim 32 , wherein the condition includes diabetes, and wherein the clinical episode includes the episode associated with diabetes.
40. The apparatus according to claim 39 , wherein the control unit is adapted to determine the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
41. The apparatus according to claim 39 , wherein the control unit is adapted to set the baseline breathing rate pattern responsively to a population average breathing rate pattern.
42. The apparatus according to claim 39 , wherein the breathing sensor comprises a motion sensor, adapted to sense breathing-related body motion of the subject indicative of the breathing, and wherein the control unit is adapted to determine the breathing rate pattern by:
analyzing the body motion data to determine a breathing-related movement pattern, and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
43. The apparatus according to claim 42 , wherein the breathing sensor is adapted to acquire the body motion data without contacting the subject or clothes the subject is wearing.
44. The apparatus according to claim 39 , wherein the breathing sensor comprises a pressure gauge configured to measure a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, and a pressure under a mattress upon which the subject lies.
45. The apparatus according to claim 32 , wherein the condition includes COPD, and wherein the clinical episode includes the episode associated with COPD.
46. The apparatus according to claim 32 , wherein the condition includes CF, and wherein the clinical episode includes the episode associated with CF.
47. The apparatus according to claim 32 , wherein the condition includes epilepsy, and wherein the clinical episode includes the episode associated with epilepsy.
48. The apparatus according to claim 32 ,
wherein the breathing rate pattern includes a breathing rate variability pattern,
wherein the baseline breathing rate pattern includes a baseline breathing rate variability pattern, and
wherein the control unit is adapted to predict the onset responsively to a decrease in breathing rate variability over time compared to the baseline breathing rate variability pattern.
49. The apparatus according to claim 32 , wherein the control unit is adapted to:
determine a breathing rate variability pattern and a slow trend breathing rate pattern,
compare the breathing rate variability pattern with the baseline breathing rate variability pattern,
compare the slow trend breathing rate pattern with a baseline slow trend breathing rate pattern, and
predict the onset responsively to both comparisons.
50. The apparatus according to claim 32 , wherein the breathing rate pattern and the baseline breathing rate pattern include respective slow trend breathing rate patterns, and wherein the control unit is adapted to compare the slow trend breathing rate pattern with the baseline slow trend breathing rate pattern.
51. The apparatus according to claim 50 , wherein the baseline slow trend breathing rate pattern includes a monotonic decline in breathing rate over at least 1 hour, and wherein the control unit is adapted to predict the onset responsively to a difference between the slow trend breathing rate pattern and the monotonic decline in breathing rate.
52. Apparatus for predicting an onset of a clinical episode, comprising:
a breathing sensor, adapted to sense breathing of a subject, and to generate a signal responsively thereto; and
a control unit, adapted to:
receive the signal,
determine at least one breathing rate pattern of the subject responsive to the signal,
compare the breathing rate pattern with a baseline breathing rate pattern, and
predict the onset of the episode at least in part responsively to the comparison,
wherein the clinical episode includes an asthma attack, and wherein the control unit is adapted to predict the onset of the asthma attack.
53. The apparatus according to claim 52 , wherein the control unit is adapted to determine the baseline breathing rate pattern by analyzing breathing of the subject during at least one non-symptomatic period.
54. The apparatus according to claim 52 , wherein the control unit is adapted to set the baseline breathing rate pattern responsively to a population average breathing rate pattern.
55. The apparatus according to claim 52 , wherein the breathing sensor comprises an acoustic sensor, adapted to sense at least one breathing-related sound selected from the list consisting of: a sound caused by wheezing, and a sound caused by coughing, and wherein the control unit is adapted to predict the onset responsively to an aspect of the breathing-related sound.
56. The apparatus according to claim 52 , wherein the breathing sensor comprises a motion sensor, adapted to sense breathing-related body motion of the subject indicative of the breathing, and wherein the control unit is adapted to determine the breathing rate pattern by:
analyzing the body motion data to determine a breathing-related movement pattern, and
determining the breathing rate pattern responsively to the breathing-related movement pattern.
57. The apparatus according to claim 56 , wherein the breathing sensor is adapted to acquire the body motion data without contacting the subject or clothes the subject is wearing.
58. The apparatus according to claim 52 , wherein the breathing sensor comprises a pressure gauge configured to measure a pressure selected from the list consisting of: a pressure at a mattress upon which the subject lies, and a pressure under a mattress upon which the subject lies.
59. The apparatus according to claim 52 ,
wherein the breathing rate pattern includes a breathing rate variability pattern,
wherein the baseline breathing rate pattern includes a baseline breathing rate variability pattern, and
wherein the control unit is adapted to predict the onset responsively to a decrease in breathing rate variability over time compared to the baseline breathing rate variability pattern.
60. The apparatus according to claim 52 , wherein the control unit is adapted to:
determine a breathing rate variability pattern and a slow trend breathing rate pattern,
compare the breathing rate variability pattern with the baseline breathing rate variability pattern,
compare the slow trend breathing rate pattern with a baseline slow trend breathing rate pattern, and
predict the onset responsively to both comparisons.
61. The apparatus according to claim 52 , wherein the breathing rate pattern and the baseline breathing rate pattern include respective slow trend breathing rate patterns, and wherein the control unit is adapted to compare the slow trend breathing rate pattern with the baseline slow trend breathing rate pattern.
62. The apparatus according to claim 61 , wherein the baseline slow trend breathing rate pattern includes a monotonic decline in breathing rate over at least 1 hour, and wherein the control unit is adapted to predict the onset responsively to a difference between the slow trend breathing rate pattern and the monotonic decline in breathing rate.