Systems and methods for respiratory event detection

• US 7,267,652 B2
• Filed: 04/09/2004
• Issued: 09/11/2007
• Est. Priority Date: 04/10/2003
• Status: Active Grant

• Current Assignee: Adidas AG
• Original Assignee: VivoMetrics, Inc.
• Inventors: Mitchnick, Mark, Inman, Dana Michael, Derchak, P. Alexander, Behar, Andrew, Sackner, Marvin, Kennedy, Paul, Coyle, Michael, Gilroy, Keith, Wilhelm, Frank, Gummels, Emerance M., Keenan, Desmond B.
• Primary Examiner(s): Marmor, II; Charles A.
• Assistant Examiner(s): Toth; Karen E
• Application Number:
  US10/822,260
• Publication Number:
  US 20050119586A1
• Time in Patent Office: 1,250 Days
• Field of Search: 128/200.24, 128/204.23, 600/300, 600/301, 600/409, 600/508, 600/529, 600/532, 600/534, 600/535, 600/536, 600/538, 600/561, 600/586, 600/587, 600/595
• US Class Current: 600/538
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0806 by whole-body plethysmography measuring blood flow using plethysmography A61B5/0295; impedance plethysmography A61B5/053; measuring volume of the body or parts thereof A61B5/1073
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/1135 by monitoring thoracic expansion
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6805 Vests
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The present invention is directed to improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors mounted on a garment for ambulatory recording. The systems and methods provide improved signal filtering for artifact rejection, improved calibration of sensor data to produce outputs indicative of lung volumes. Further, this invention provides improved systems and methods directed to processing lung volume signals, however measured or derived, to provide improved determination of respiratory parameters and improved recognition of selected respiratory events.

Claims

1. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal one or more temporal sequences of respiratory volume parameters, and

recognizing sigh events in dependence on at least one of the derived temporal sequences of respiratory volume parameters, wherein a breath is recognized as a sigh if that breath is not an artifact according to the true breath rule, and has volume parameters greater than a threshold sigh volume, wherein the threshold sigh volume varies in dependence on running median baseline lung volumes.

2. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal one or more temporal sequences of respiratory parameters including peak expiratory flow (PEF) parameters, and

recognizing cough events in dependence on at least the derived temporal sequence of PEF parameters wherein a breath is recognized as a cough if that breath is not an artifact according to the true breath rule, and has a PEF greater than a threshold cough PEF, the threshold cough PEF varying in dependence on running median baseline PEF.

3. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal one or more temporal sequences of respiratory parameters including one of more of the inspiratory/expiratory (IE) ratio, the fractional inspiratory time, the inspiratory flow rate, and the expiration time; and

recognizing speech events, wherein one or more breaths are recognized as comprising speech in dependence on the derived temporal sequences of one or more of the parameters inspiratory/expiratory (IE) ratio, the fractional inspiratory time, the inspiratory flow rate, or the expiratory time.

4. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal one or more temporal sequences of respiratory parameters, including minute ventilation volume and peak expiratory flow (PEF) parameters; and

recognizing dyspnea events, wherein dyspnea is recognized in dependence on a ratio of these derived parameters.

5. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving one or more temporal sequences of respiratory parameters including the time to reach peak expiratory flow, the expiratory time, the peak expiratory flow, and the mean expiratory flow; and

recognizing changes in the ratio of forced expiratory volume in one second to vital capacity in dependence on a ratio of the derived temporal sequences of the time to reach peak expiratory flow and the expiratory time paarameters, or on a ratio of the derived temporal sequences of the peak expiratory flow and the mean expiratory flow parameters.

6. A method for recognizing respiratory events in a monitored subiect comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal one or more temporal sequences of respiratory parameters including the peak inspiratory flow and the tidal volume, and

recognizing changes in the ratio of forced expiratory volume in one second to vital capacity in dependence on a ratio of these derived parameters.

7. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving from the Vt signal and a signal reflective of rib cage size one or more temporal sequences of respiratory parameters including a temporal sequence of the rib cage contribution to the tidal volume; and

recognizing changes in the ratio of forced expiratory volume in one second to vital capacity in dependence on this derived parameter.

8. A method for recognizing respiratory events in a monitored subject comprising:

deriving a signal (Vt) indicative of lung volume from a plurality of respiratory signals received from the monitored subject,

deriving one or more temporal sequences of respiratory parameters including the fraction of expiration time with thoraco-abdominal asynchrony from a signal reflective of rib cage size and a signal reflective of abdominal size, and

recognizing changes in the ratio of forced expiratory volume in one second to vital capacity in dependence this derived parameter.

23. A computer system for processing signals reflective of a monitored subject'"'"'s respiration comprising:

a processor, and a memory in communication with the processor, the memory comprising encoded instructions for causing the processor to perform the methods of claim 8.

24. A program product for causing a computer system to process signals reflective of a monitored subject'"'"'s respiration comprising a computer readable medium with encoded instructions for causing the system to perform the method of claim 8.

9. A method for recognizing a cough in a monitored subject comprising:

recognizing a candidate sound event when an input level of a microphone signal exceeds a sound-event threshold, wherein the microphone is responsive to the subject'"'"'s vocalizations,

filtering a lung volume signal (Vt) in dependence on characteristics of the candidate sound event,

recognizing a candidate respiratory event if the selected filtered tidal volume signal exhibits an expiration and a following inspiration having amplitudes greater than a amplitude threshold,

determining the occurrence of a cough if the candidate sound event coincides temporally with the candidate respiratory event.

10. The method of claim 9 wherein the sound-event threshold is a pre-determined multiple of a background noise intensity that is determined in the vicinity of the monitored subject.

11. The method of claim 10 wherein a candidate sound-event extends from the time when the input signal level exceeds the sound-event threshold to the time the time when the input level no longer exceeds the sound-event threshold.

12. The method of claim 9 wherein the amplitude threshold is individually calibrated for the monitored subject.

13. The method of claim 9 wherein the amplitude threshold is approximately 200 ml.

18. The method of claim 9 wherein a cough is further determined only if the candidate respiratory event exhibits a sharp expiration, a minimum lung volume occurring during the central region of the associated sound event, and a sharp inspiration.

14. A method for recognizing a cough in a monitored subject comprising:

recognizing a candidate sound event when an input level of a microphone signal exceeds a sound-event threshold, wherein the microphone is responsive to the subject'"'"'s vocalizations,

filtering a lung volume signal (Vt) in dependence on characteristics of the candidate sound event into a lower frequency band (LFB) if the candidate sound event has a duration longer than a duration threshold, and into a higher frequency band (HFB) if the candidate sound event has a duration shorter than the duration threshold,

recognizing a candidate respiratory event if the selected filtered tidal volume signal exhibits an expiration and a following inspiration having amplitudes greater than a amplitude threshold, and

determining the occurrence of a cough if the candidate sound event coincides temporally with the candidate respiratory event.

15. The method of claim 14 wherein the duration threshold is approximately 600 msec.

16. The method of claim 14 wherein the HFB excludes a range of low frequencies that are included in the LFB.

17. The method of claim 16 wherein the excluded range of frequencies is from approximately 0.4 to approximately 1.1 Hz, and wherein the frequency bands extend approximately 4.9 Hz.

19. A method for recognizing a cough in a monitored subject comprising:

recognizing a candidate sound event when an input level of a microphone signal exceeds a sound-event threshold, wherein the microphone is responsive to the subject'"'"'s vocalizations.

filtering a lung volume signal (Vt) in dependence on characteristics of the candidate sound event,

recognizing a candidate respiratory event if the selected filtered tidal volume signal exhibits an expiration and a following inspiration having amplitudes greater than a amplitude threshold,

determining the occurrence of a cough if the candidate sound event coincides temporally with the candidate respiratory event and if the candidate sound event has a pitch characteristic below a cough-pitch threshold.

20. The method of claim 19 wherein pitch characteristics are determined by short term spectral analysis of the input microphone signal.

21. The method of claim 20 wherein the short-term spectral analysis comprises determining mel-frequency cepsturm coefficients.

22. The method of claim 21 wherein the cough-pitch threshold is at met-frequency cepsturm coefficients of approximately 1.5-2.0.