METHOD AND SYSTEM FOR DETECTING APNEA

• US 20120172730A1
• Filed: 08/22/2011
• Published: 07/05/2012
• Est. Priority Date: 01/04/2011
• Status: Active Grant

• Current Assignee: College of William & Mary
• Original Assignee: College of William & Mary
• Inventors: Delos, John B., Lee, Hoshik
• Granted Patent: US 8,708,920 B2
• Time in Patent Office: Days
• US Class Current: 600/484
• 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 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

Existing monitors for apnea miss many serious events because they do not adequately distinguish the heart signal in chest impedance from the respiratory signal. Described herein is a respiratory monitoring system and method for improved detection and response to apnea, particularly in a NICU setting but also useful in a home setting. This method filters from the chest impedance the part of the impedance that is caused by the beating of the heart in a human subject, and then identifies in real time significant silence in the filtered chest impedance signal, including determining the probability of apnea. If the probability of apnea exceeds a threshold value, the apneic subject can be stimulated using automated interactions such as a vibrating mattress or air blower.

Claims

1. A method for detecting and responding to apnea in a human subject comprising:

measuring in real time the chest impedance and heartbeat of a human subject;

converting said chest impedance per second to a re-sampled chest impedance signal per heartbeat;

filtering said re-sampled chest impedance signal to a filtered chest impedance signal; and

identifying in real time significant silence in said filtered chest impedance signal;

wherein said filtered chest impedance signal attenuates the impedance caused by the beating of the heart.

2. The method of claim 1, further comprising the step of stimulating said human subject during said significant silence utilizing automated interaction.

3. The method of claim 2, wherein said automated interaction is performed by a mechanical device contacting said human subject.

4. The method of claim 2, wherein said automated interaction comprises an alarm that sounds when significant silence in said filtered chest impedance signal is identified.

5. The method of claim 2, wherein said automated interaction is performed by a device selected from the group consisting of a jacket worn by said human subject, a mat on which said human subject rests, and a blower which delivers a strong puff of air.

6. The method of claim 2, wherein the stimulation provided by said automated interaction gradually increases in intensity during said significant silence.

7. The method of claim 2, wherein the stimulation provided by said automated interaction increases by discrete, incremental steps during said significant silence.

8. The method of claim 1, further comprising the step of alerting medical personnel during said significant silence.

9. The method of claim 8, further comprising the step of stimulating said human subject during said significant silence utilizing automated interaction prior to said medical practitioner responding to the apnea episode.

10. The method of claim 8, wherein said medical personnel are alerted significantly earlier during an apnea episode than with traditional apnea monitors.

11. The method of claim 1, wherein said human subject is a premature infant.

12. The method of claim 1, wherein said step of filtering said re-sampled chest impedance signal to a filtered chest impedance signal comprises:

obtaining a Fourier transform of the re-sampled chest impedance signal; and

utilizing a band-stop filter.

13. The method of claim 12, wherein said step of obtaining a Fourier transform of the re-sampled chest impedance signal occurs before said step of utilizing a band-stop filter.

14. The method of claim 1, wherein the step of filtering said re-sampled chest impedance signal to a filtered chest impedance signal comprises:

obtaining a Fourier transform of the re-sampled chest impedance signal;

utilizing a band-stop filter; and

utilizing a high-pass filter.

15. The method of claim 14, wherein said step of obtaining a Fourier transform of the re-sampled chest impedance signal occurs before both said step of utilizing a band-stop filter and said step of utilizing a high-pass filter.

16. The method of claim 1, wherein the step of identifying in real time significant silence in said filtered chest impedance signal comprises:

calculating in real time the running variance of said filtered chest impedance signal of said human subject; and

correlating said running variance of said filtered chest impedance signal of said human subject with the probability of apnea;

wherein said probability of apnea is derived from a histogram compiled from the running variance of the filtered chest impedance signal for a set of apneic infants; and

wherein said histogram is interpreted as a statistical mixture of a first distribution corresponding to apnea events and a second distribution corresponding to regular breathing, and said first distribution divided by the sum of said first distribution and second distribution produces a probability of apnea for each variance.

17. The method of claim 16, wherein the method correctly identifies at least 90% of apnea occurrences compared with expert analysis.

18. The method of claim 1, wherein the number of false alarms triggered by said significant silence is substantially lower than the number of false alarms triggered by traditional apnea monitors.

19. The method of claim 18, wherein the number of false alarms triggered by said significant silence is at least 25% lower than the number of false alarms triggered by traditional apnea monitors.

20. A method for reducing the number of false alarms for the detection of apnea in a human subject comprising:

attenuating the impedance caused by the beating of the heart in a filtered chest impedance signal; and

identifying in real time significant silence in said filtered chest impedance signal;

wherein the number of false alarms triggered by said significant silence is substantially lower than the number of false alarms triggered by traditional apnea monitors.

21. The method of claim 20, wherein the number of false alarms triggered by said significant silence is at least 25% lower than the number of false alarms triggered by traditional apnea monitors.

22. A system for detecting and responding to apnea in a human subject comprising:

a detector module which measures in real time the chest impedance and heartbeat of a human subject;

a computing module in communication with said detector module which converts said chest impedance per second to a re-sampled chest impedance signal per heartbeat;

a filtering module which filters said re-sampled chest impedance signal to a filtered chest impedance signal whereby impedance from the heartbeat is attenuated; and

a second computing module which identifies in real time significant silence in said filtered chest impedance signal.

23. The system of claim 22, further comprising a stimulating module which stimulates said human subject during said significant silence utilizing automated interaction.