Sleep monitoring using a photoplethysmograph

• US 7,674,230 B2
• Filed: 05/17/2007
• Issued: 03/09/2010
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: Widemed Technologies Ltd.
• Original Assignee: Widemed Ltd.
• Inventors: Reisfeld, Daniel
• Primary Examiner(s): Marmor, II; Charles A
• Assistant Examiner(s): Natnithithadha; Navin
• Application Number:
  US11/750,221
• Publication Number:
  US 20070213620A1
• Time in Patent Office: 1,027 Days
• Field of Search: 600/472, 600/481, 600/483, 600/484, 600500-504, 600/323, 600/324, 600/328, 600/529, 600/300
• US Class Current: 600/484
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/1135 by monitoring thoracic expansion
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/6838 Clamps or clips
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• 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

A method for diagnosis includes receiving from a sensor coupled to a body of a sleeping patient a photoplethysmograph signal. The photoplethysmograph signal is processed independently of any other physiological measurements in order to identify sleep stages of the patient.

Claims

1. A method for diagnosis, comprising:

receiving from a sensor coupled to a body of a sleeping patient a photoplethysmograph signal; and

operating a data processor for processing the photoplethysmograph signal independently of any other physiological measurements in order to identify apnea episodes which comprise Cheyne-Stokes breathing events and to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes;

wherein processing the photoplethysmograph signal comprises distinguishing a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

2. The method according to claim 1, and comprising processing the photoplethysmograph signal in order to measure a vasomodulation in the body.

3. The method according to claim 1, and comprising processing the photoplethysmograph signal in order to measure a heart rate of the patient.

4. The method according to claim 1, and comprising processing the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.

5. Apparatus for diagnosis, comprising:

a sensor, which is configured to be coupled to a body of a sleeping patient and to output a photoplethysmograph signal; and

a processor, which is coupled to process the photoplethysmograph signal independently of any other physiological measurements in order to identify apnea episodes which comprise Cheyne-Stokes breathing events and to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes;

wherein the processor is configured to distinguish a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

6. The apparatus according to claim 5, wherein the processor is configured to process the photoplethysmograph signal in order to measure a vasomodulation in the body.

7. The apparatus according to claim 5, wherein the processor is configured to process the photoplethysmograph signal in order to measure a heart rate of the patient.

8. The apparatus according to claim 5, wherein the processor is configured to process the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.

9. A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive a photoplethysmograph signal from a body of a sleeping patient, and to process the photoplethysmograph signal independently of any other physiological measurements in order to identify apnea episodes which comprise Cheyne-Stokes breathing events, to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes, and to distinguish a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

10. The product according to claim 9, wherein the instructions cause the computer to process the photoplethysmograph signal in order to measure a vasomodulation in the body.

11. The product according to claim 9, wherein the instructions cause the computer to process the photoplethysmograph signal in order to measure a heart rate of the patient.

12. The product according to claim 9, wherein the instructions cause the computer to process the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.

13. A method for diagnosis, comprising:

receiving from a sensor coupled to a body of a sleeping patient a photoplethysmograph signal; and

operating a data processor for processing the photoplethysmograph signal in order to identify apnea episodes which comprise Cheyne-Stokes breathing events and to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes;

wherein said processing comprises distinguishing a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

14. The method according to claim 13, and comprising processing the photoplethysmograph signal in order to measure at least one of a vasomodulation in the body and a heart rate of the patient.

15. The method according to claim 13, and comprising processing the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.

16. Apparatus for diagnosis, comprising:

a sensor, which is configured to be coupled to a body of a sleeping patient and to output a photoplethysmograph signal; and

a processor, which is coupled to process the photoplethysmograph signal in order to identify apnea episodes which comprise Cheyne-Stokes breathing events and to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes;

wherein the processor is configured to distinguish a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

17. The apparatus according to claim 16, wherein the processor is configured to process the photoplethysmograph signal in order to measure at least one of:

a vasomodulation in the body and a heart rate of the patient.

18. The apparatus according to claim 16, wherein the processor is configured to process the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.

19. A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive a photoplethysmograph signal from a body of a sleeping patient, and to process the photoplethysmograph signal in order to identify apnea episodes which comprise Cheyne-Stokes breathing events and to distinguish a light sleep stage from a deep sleep stage of the patient based on the identified apnea episodes;

wherein the instructions cause the computer to distinguish a rapid eye movement (REM) sleep stage from a light sleep stage responsively to a characteristic of the Cheyne-Stokes breathing events, selected from the group of characteristics consisting of a Cheyne-Stokes wavelength and a desaturation depth.

20. The product according to claim 19, wherein the instructions cause the computer to process the photoplethysmograph signal in order to measure at least one of:

a vasomodulation in the body and a heart rate of the patient.

21. The product according to claim 19, wherein the instructions cause the computer to process the photoplethysmograph signal in order to detect an artifact that is characteristic of motion of the patient.