Prediction and monitoring of clinical episodes

• US 8,731,646 B2
• Filed: 09/06/2013
• Issued: 05/20/2014
• 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, Yossi
• Primary Examiner(s): Lavert, Nicole F
• Application Number:
  US14/020,574
• Publication Number:
  US 20140012099A1
• Time in Patent Office: 256 Days
• Field of Search: 600508-509, 600/515, 600/529
• US Class Current: 600/509
• 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 provided including sensing at least one parameter of a subject while the subject sleeps. The parameter is analyzed, and a condition of the subject is determined at least in part responsively to the analysis. The subject is alerted to the condition only after the subject awakes. Other applications are also described.

Claims

1. A method comprising:

sensing at least one parameter of a subject while the subject sleeps;

analyzing the parameter;

determining a condition of the subject at least in part responsively to the analysis; and

alerting the subject to the condition only after the subject awakes,

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.

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 without requiring human compliance.

3. 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.

4. The method according to claim 1, wherein determining the condition of the subject comprises predicting an onset of an asthma attack.

5. The method according to claim 1, wherein determining the condition of the subject comprises predicting an onset of an episode associated with the congestive heart failure (CHF) of the subject.

6. The method according to claim 1, wherein determining the condition of the subject comprises predicting an onset of an episode of hypoglycemia caused by diabetes.

7. The method according to claim 1, wherein determining the condition of the subject comprises predicting an onset of an episode selected from the group 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.

8. 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 without requiring human compliance.

9. The method according to claim 1, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and wherein determining the condition of the subject 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

determining the condition at least in part responsively to the comparison.

10. Apparatus comprising:

a sensor, adapted to sense at least one parameter of a subject while the subject sleeps;

a user interface; and

a control unit, adapted to;

analyze the parameter,

determine a condition of the subject at least in part responsively to the analysis, and

drive the user interface to alert the subject to the condition only after the subject awakes,

wherein the sensor comprises a non-contact sensor, adapted to sense the at least one parameter without contacting the subject or clothes the subject is wearing.

11. The apparatus according to claim 10, 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.

12. The apparatus according to claim 10, wherein the apparatus is for use with a reclining surface upon which the subject lies, and wherein the sensor comprises a pressure gauge, configured to measure a pressure in, on, or under the reclining surface upon which the subject lies.

13. The apparatus according to claim 10, wherein the control unit is adapted to determine the condition of the subject by predicting an onset of an asthma attack.

14. The apparatus according to claim 10, wherein the control unit is adapted to determine the condition of the subject by predicting an onset of an episode associated with congestive heart failure (CHF) of the subject.

15. The apparatus according to claim 10, wherein the control unit is adapted to determine the condition of the subject by predicting an onset of an episode of hypoglycemia caused by diabetes.

16. The apparatus according to claim 10, wherein the control unit is adapted to determine the condition of the subject by predicting an onset of an episode selected from the group 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.

17. The apparatus according to claim 10, 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.

18. The apparatus according to claim 10, wherein the at least one parameter includes at least one breathing-related parameter of the subject, and 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

determine the condition of the subject at least in part responsively to the comparison.