Cardio-Kinetic Cross-Spectral Density for Assessment of Sleep Physiology

• US 20170181691A1
• Filed: 12/29/2016
• Published: 06/29/2017
• Est. Priority Date: 12/29/2015
• Status: Active Grant

• Current Assignee: Lifeq Global Limited
• Original Assignee: Lifeq Global Limited
• Inventors: Olivier, Laurence Richard
• Granted Patent: US 10,791,985 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 5/742 using visual displays A61B5/339 takes precedence

Abstract

The invention comprises methods and systems capable of processing simultaneously measured cardiac and motion signals, selecting from said signals the optimal cardiac and motion signal combination for cross spectral density calculations and, hereafter, itemizing the shared information in terms of the individual constituent frequencies and the effect exerted upon said frequencies by common underlying physiological states. The information is passed through a classifier, where said information is used to quantify the underlying physiological states, exemplified by but not limited to, sleep stage, quality and/or disorders that affect sleep stability. Inferences made by the methods described, for example, can be employed to aid home based sleep analysis and automated screening of patients with sleep disorders.

Claims

1. A method to assess sleep physiological states of a subject, comprising:

a) using as input, at least two measured signals containing degrees of cardiac activity and motion data of the subject;

b) performing a cross-spectral density calculation on said at least two measured signals to determine the degree of shared activity over a range of frequencies in said at least two of said signals; and

c) using the shared activity features to classify different underlying physiological states.

2. The method of claim 1, further comprising using spectral coherence with the performing the cross-spectral density calculation to detect shared activity at different frequencies between the cardiac channel and the motion channel.

3. The methods of claim 1, further comprising calculating a quality metric that allows for conditional prediction of the underlying biological states to limit predictions to periods when data of sufficient quality is collected.

4. The method of claim 1, wherein the physiological states comprise sleep stages, sleep pathologies, and sleep quality.

5. The method of claim 1, wherein the method is configured to detect narcolepsy, the method further comprising distinguishing between REM and wake states, where narcolepsy is detected by means of identifying the early/direct transition from wakefulness to REM during a sleep session.

6. The method of claim 1, wherein the at least two measured signals are collected by a data acquisition device, wherein the data acquisition device is configured to wirelessly communicating data to other devices and/or platforms.

7. The systems and methods of claim 6, wherein the data from said data acquisition device can be wirelessly communicated to a cloud-based platform.

8. The method of claim 1, further comprising utilizing contextual data to classify different underlying physiological states of the subject.

9. The method of claim 1, wherein the cardiac activity is captured by an ECG recorder and/or a PPG recorder.

10. The method of claim 1, wherein the motion data is captured by an accelerometer and/or a BCG recorder.

11. The method of claim 1, wherein the inputs are collected in time windows.

12. The method of claim 1, wherein using the shared activity features to classify different underlying physiological states further comprises using polysomnography data.

13. A system for non-invasive assessment of sleep physiological states of a subject comprising:

a. a data acquisition device, the data acquisition device configured to capture at least two measured signals containing degrees of cardiac activity and motion data of the subject; and

b. a computing device, the computing device configured to;

i. perform a cross-spectral density calculation on said at least two measured signals to determine the degree of shared activity over a range of frequencies in the at least two of said signals; and

ii. use the shared activity features to classify different underlying physiological states of the subject.

14. The system of claim 13, wherein the data acquisition device comprises a wearable device.

15. The system of claim 13, wherein the computing device is further configured to use spectral coherence with the cross-spectral density calculation to detect shared activity at different frequencies between the cardiac channel and the motion channel.

16. The system of claim 13, wherein the computing device is further configured to calculate a quality metric that allows for conditional prediction of the underlying biological states to limit predictions to periods when data of sufficient quality is collected.

17. The system of claim 13, wherein the data acquisition device further comprises an ECG recorder, a PPG recorder, an accelerometer, and a BCG recorder, wherein the ECG and the PPG recorder are configured to produce the signals of the cardiac data and the accelerometer and the BCG recorder are configured to produce the signals of the motion data.

18. The system of claim 13, wherein the computing device is configured to obtain contextual data from the subject, wherein the contextual data is used in the classifying of the different underlying physiological states of the subject.