METHOD AND APPARATUS FOR ASSESSMENT OF SLEEP DISORDERS

• US 20120116187A1
• Filed: 07/16/2010
• Published: 05/10/2012
• Est. Priority Date: 07/17/2009
• Status: Active Grant

• Current Assignee: Oregon Health & Science University
• Original Assignee: Oregon Health & Science University
• Inventors: Pavel, Misha, Hayes, Tamara, Beattle, Zachary, Hagen, Chad
• Granted Patent: US 8,905,928 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• 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 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6887 mounted on external non-worn devices, e.g. non-medical devices
• CPC Class Codes: A61B 5/6891 Furniture
• 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 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments provide systems, methods and apparatuses for monitoring the sleep of a subject in a home environment. In embodiments, load cells placed under bed supports may be coupled to a computing device that may process the load cell data to detect disordered breathing. In some embodiments, a computing device may apply a pattern recognition algorithm to load cell data to distinguish between normal movements and movements associated with a sleep disorder. In an embodiment, apparatuses and methods for monitoring sleep may perform functions associated with detection of sleep disturbances and/or identify a sleep disorder.

Claims

1. A method of monitoring sleep, the method comprising:

collecting data from one or more load cells, the load cells being positioned below one or more supports of a bed and the data indicating force exerted against the load cell, the collecting being performed during a period of sleep;

processing the data;

extracting a first physiological parameter from the data, the parameter comprising at least one of respiration, movement, and heart rate; and

identifying, based at least on the first physiological parameter, a sleep disorder,

wherein the collecting, extracting and identifying are performed by a computing device comprising executable instructions for applying a pattern recognition algorithm to the data.

2. The method of claim 1, wherein the processing comprises at least one of decimating the data, filtering the data, and segmenting the data into periods of movement and stillness.

3. The method of claim 1, wherein the extracting further includes extracting a second physiological parameter from the data and wherein the identifying is based at least on the first physiological parameter and the second physiological parameter.

4. The method of claim 1, wherein at least one of the one more supports of a bed is added to the bed and tensioned for use with at least one of the one or more load cells.

5. The method of claim 1, wherein the sleep disorder comprises at least one of abnormal respiration and disordered movement.

6. The method of claim 2, further including combining the first and second physiological parameters and extracting a third physiological parameter from the combined first and second physiological parameters.

7. The method of claim 6, wherein the identifying is based at least on the first, second and third physiological parameters.

8. The method of claim 1, wherein the first physiological parameter is extracted from data representing periods of stillness.

9. An apparatus configured to receive information related to a sleep disorder, the apparatus comprising:

one or more load cells configured for placement below one or more supports of a bed, the load cells further configured to convert force to an electrical signal indicative of the force; and

a computing device coupled to at least one of the one or more load cells, the computing device comprising computer executable instructions for

receiving signals from at least one of the one or more load cells,

processing the signals,

extracting a physiological feature from the signal data, and

identifying, based at least on the physiological feature, an incident associated with a sleep disorder.

10. The apparatus of claim 9, wherein the computer executable instructions comprise a pattern recognition algorithm.

11. The apparatus of claim 9, further including a transceiver coupled to at least one of the computing device and to at least one of the one or more load cells.

12. The apparatus of claim 9, further comprising an alarm, the computer executable instructions operable to actuate the alarm in response to the identifying an incident associated with a sleep disorder.

13. The apparatus of claim 9, wherein the incident comprises at least one of disordered movement, abnormal respiration and abnormal heart rate.

14. The apparatus of claim 9, further including a support for a bed, the support configured to be added to the bed and tensioned for use with the load cell.