Discrimination of apnea type by medical device

• US 9,788,782 B2
• Filed: 07/18/2016
• Issued: 10/17/2017
• Est. Priority Date: 04/04/2014
• Status: Expired due to Fees

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Thakur, Pramodsingh Hirasingh, Ternes, David J., Ruble, Stephen B.
• Primary Examiner(s): MANUEL, GEORGE C
• Application Number:
  US15/213,120
• Publication Number:
  US 20160324467A1
• Time in Patent Office: 456 Days
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• 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 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 7/02 Stethoscopes acoustic details thereof G10K11/00
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R

Abstract

Disclosed herein, among other things, are methods and apparatus related to identification of apnea type. One aspect of the present subject matter provides a method for real-time apnea discrimination. The method includes sensing an impedance-based tidal volume signal to monitor a respiratory cycle of a patient, and detecting a reduction in tidal swing using the sensed impendence to detect an apnea event. When the apnea event is detected, a shape of the sensed signal is compared to a stored signal shape to determine whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event, in various embodiments.

Claims

1. A medical device for apnea discrimination, the device comprising:

a sensor configured to sense a parameter related to heart sounds of a patient;

a processor adapted to be connected to the sensor, the processor configured to use the sensed parameter to determine whether a detected apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event; and

a memory to store historical data of CSA events and OSA events for the patient,

wherein the processor is configured to calculate likelihood for each apnea event based on the historical data and to use the calculated likelihood to determine whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event.

2. The device of claim 1, wherein the parameter related to heart sounds of the patient includes variability in low frequency components of a spectrum of R-R intervals.

3. The device of claim 1, wherein the parameter related to heart sounds of the patient includes variability in low frequency components of a spectrum of S1 amplitude.

4. The device of claim 1, wherein the parameter related to heart sounds of the patient includes variability in low frequency components of a spectrum of S2 amplitude.

5. The device of claim 1, wherein the parameter related to heart sounds of the patient includes variability in low frequency components of a spectrum of S3 amplitude.

6. The device of claim 1, wherein the parameter related to heart sounds of the patient includes variability in low frequency components of a spectrum of systolic time intervals.

7. The device of claim 1, wherein the processor is configured to use the sensed parameter to determine a mix or ratio of OSA and CSA for the patient.

8. The device of claim 7, further comprising a display configured to present the mix or ratio of OSA and CSA to a clinician for diagnostic assistance.

9. A method for apnea discrimination, the method comprising:

sensing a parameter related to heart sounds of a patient using a sensor; and

using a processor connected to the sensor to process the sensed parameter to determine whether a detected apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event,

wherein using the processor to process the sensed parameter includes conducting a time-frequency decomposition of the signal of interest to obtain a spectrogram of the signal over a predetermined time period.

10. The method of claim 9, wherein sensing the parameter related to heart sounds of the patient includes sensing variability in low frequency components of a spectrum of systolic time intervals.

11. The method of claim 10, wherein systolic time intervals include one or more of pre-ejection period (PEP):

Q-S1, R-S1, ejection time, diastolic interval and S1-S2 interval.

12. The method of claim 9, wherein using the processor to process the sensed parameter includes using variability along a frequency axis on the spectrogram to determine a likelihood of observing CSA or OSA on a next apnea event.

13. The method of claim 12 further comprising using the processor to adjust a discrimination threshold for the next apnea event based on the determined likelihood.

14. The method of claim 9, comprising using the processor to track a set of consistent spectral peaks on the spectrogram and use energies of tracked peaks to determine relative prevalence of primarily OSA events and primarily CSA events for the patient.

15. The method of claim 9, further comprising using a display connected to the processor to present the mix or ratio of OSA and CSA to a clinician for diagnostic assistance.

16. The method of claim 9, further comprising using a memory connected to the processor to store historical data of CSA events and OSA events for the patient.

17. The method of claim 16, further comprising using the processor to calculate likelihood for each apnea event based on the historical data and to use the calculated likelihood to determine whether the apnea event is primarily an OSA event or primarily a central sleep apnea CSA event.