System and method for sleep disorder diagnosis and treatment

• US 10,265,014 B2
• Filed: 01/23/2017
• Issued: 04/23/2019
• Est. Priority Date: 09/06/2013
• Status: Active Grant

• Current Assignee: Somnology, Inc.
• Original Assignee: Somnology, Inc.
• Inventors: Lim, Melissa Susann
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US15/413,130
• Publication Number:
  US 20170128003A1
• Time in Patent Office: 820 Days
• Field of Search: None
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• 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 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/087 Measuring breath flow
• 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 5/1135 by monitoring thoracic expansion
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take 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: A61M 2021/0027 : by the hearing sense
• CPC Class Codes: A61M 21/02 : for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia for massage A61H; electrotherapy A61N, e.g. applying alternating or intermittent electric currents for producing anaesthesia A61N1/36021
• CPC Class Codes: A61M 2205/3561 : local, e.g. within room or hospital
• CPC Class Codes: A61M 2205/3584 : using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/505 : Touch-screens; Virtual keyboard or keypads; Virtual buttons; Soft keys; Mouse touches
• CPC Class Codes: A61M 2205/52 : with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/06 : Heartbeat rate only
• CPC Class Codes: A61M 2230/205 : partial oxygen pressure (P-O2)
• CPC Class Codes: A61M 2230/42 : Rate
• CPC Class Codes: A61M 2230/50 : Temperature
• CPC Class Codes: G16H 10/20 : for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/70 : relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

A system and method for sleep disorder diagnosis and treatment are disclosed. A particular embodiment includes: establishing a wireless data communication interface between a networked server and a sleep metering system worn by a user, the sleep metering system including a sensor array, wireless transceiver, and a processor; activating the sleep metering system to begin collection of sensor data from the user based on data signals from the sensor array of the sleep metering system; receiving a respiratory waveform and data corresponding to a level of arterial oxygen saturation (SpO2) in the user'"'"'s blood over time as an SpO2 waveform based on the collected sensor data; receiving a set of user-configured control variables; and generating a sleep efficiency score based on the respiratory waveform, the SpO2 waveform, and the user-configured control variables, the sleep efficiency score including a log of the user'"'"'s respiratory effort reduction events (RERE) and respiratory effort exaggeration events.

Claims

1. A computer-implemented method for improving detection of sleep-related conditions, the method comprising:

establishing, by use of a data processor, a wireless data communication interface between the data processor and a sleep metering system worn by a user, the sleep metering system including a sensor array, wireless transceiver, and a processor;

positioning the sleep metering system for data recording externally at the top of the user'"'"'s abdomen, the sleep metering system being removably attached to the user with an inductive belt being a chest expansion and respiratory effort measuring device to detect and measure airflow and respiratory effort in the user;

collecting, by use of the sleep metering system, sensor data from the user based on data signals from the sensor array of the sleep metering system;

receiving, by use of the data processor, a single abdominal effort channel waveform based on the collected sensor data from the sleep metering system, the single abdominal effort channel waveform representing breathing pattern data corresponding to variations in the degree of the user'"'"'s abdominal effort in a series of breaths over a given time interval;

retrieving a set of user-configured control variables;

identifying, by use of the data processor, the user'"'"'s respiratory effort reduction events (RERE) and the user'"'"'s respiratory effort exaggeration events (REEE) using the breathing pattern data from the sleep metering system and the user-configured control variables;

anonymizing the user'"'"'s collected sensor data to remove specific identifying information related to the particular user; and

transferring the user'"'"'s anonymized data to a central server.

2. The computer-implemented method of claim 1 wherein identifying the user'"'"'s RERE REEE includes filtering the breathing pattern data.

3. The computer-implemented method of claim 1 including generating a sleep efficiency score.

4. The computer-implemented method of claim 3 wherein generating the sleep efficiency score includes scoring an RERE event based on the user-configured control variables.

5. The computer-implemented method of claim 3 wherein generating the sleep efficiency score includes scoring an RERE event based on whether a current respiratory effort value from the breathing pattern data exceeds a baseline value from the user-configured control variables.

6. The computer-implemented method of claim 3 wherein generating the sleep efficiency score includes scoring an REEE event based on whether a current respiratory effort value from the breathing pattern data exceeds a baseline value from the user-configured control variables.

7. The computer-implemented method of claim 3 wherein generating the sleep efficiency score includes coalescing a plurality of sleep events into a single sleep event.

8. The computer-implemented method of claim 3 wherein generating the sleep efficiency score includes generating a breathing disturbance index (BDI) score based on a number of detected sleep events, and generating a compensated breathing disturbance index (BDI) score based on the BDI score and values from the user-configured control variables.

9. The computer-implemented method of claim 1 including generating an annotated log file including entries corresponding to detected sleep events.

10. The computer-implemented method of claim 1 including generating a sleep report containing information and datasets generated using the sensor data captured and processed for the user.

11. A system for improving detection of sleep-related conditions, the system comprising:

a data processor;

a memory, in data communication with the data processor;

a sleep metering system in wireless data communication with the data processor, the sleep metering system being worn by a user and including a sensor array, wireless transceiver, and a processor, the sleep metering system being positioned for data recording externally at the top of the user'"'"'s abdomen, the sleep metering system being removably attached to the user with an inductive belt being a chest expansion and respiratory effort measuring device to detect and measure airflow and respiratory effort in the user; and

a sleep disorder diagnosis and treatment system, executable by the data processor, to;

establish a wireless data communication interface between the data processor and the sleep metering system;

activate the sleep metering system to collect sensor data from the user based on data signals from the sensor array of the sleep metering system;

receive from the sleep metering system a single abdominal effort channel waveform based on the collected sensor data from the sleep metering system, the single abdominal effort channel waveform representing breathing pattern data corresponding to variations in the degree of the user'"'"'s abdominal effort in a series of breaths over a given time interval;

retrieve a set of user-configured control variables;

identify the user'"'"'s respiratory effort reduction events (RERE) and the user'"'"'s respiratory effort exaggeration events (REEE) using the breathing pattern data from the sleep metering system and the user-configured control variables;

anonymize the user'"'"'s collected sensor data to remove specific identifying information related to the particular user; and

transfer the user'"'"'s anonymized data to a central server.

12. The system of claim 11 wherein the sleep disorder diagnosis and treatment system being further configured to filter the breathing pattern data.

13. The system of claim 11 wherein the sleep disorder diagnosis and treatment system being further configured to generate a sleep efficiency score.

14. The system of claim 13 wherein the sleep disorder diagnosis and treatment system being further configured to score an RERE event based on the user-configured control variables.

15. The system of claim 13 wherein the sleep disorder diagnosis and treatment system being further configured to score an RERE event based on whether a current respiratory effort value from the breathing pattern data exceeds a baseline value from the user-configured control variables.

16. The system of claim 13 wherein the sleep disorder diagnosis and treatment system being further configured to score an REEE event based on whether a current respiratory effort value from the breathing pattern data exceeds a baseline value from the user-configured control variables.

17. The system of claim 13 wherein the sleep disorder diagnosis and treatment system being further configured to coalesce a plurality of sleep events into a single sleep event.

18. The system of claim 13 wherein the sleep disorder diagnosis and treatment system being further configured to generate a breathing disturbance index (BDI) score based on a number of detected sleep events, and to generate a compensated breathing disturbance index (BDI) score based on the BDI score and values from the user-configured control variables.

19. The system of claim 11 wherein the sleep disorder diagnosis and treatment system being further configured to generate an annotated log file including entries corresponding to detected sleep events.

20. The system of claim 11 wherein the sensor array includes sensors of a type from the group consisting of:

a motion detection device, a sound detection device, a pulse oximeter device, and a respiratory effort detection device.