U.S. patent application number 14/133173 was filed with the patent office on 2014-06-26 for non-invasive monitoring of respiratory rate, heart rate and apnea.
This patent application is currently assigned to MASIMO CORPORATION. The applicant listed for this patent is MASIMO CORPORATION. Invention is credited to Victor F. Lanzo, Gilberto Sierra, Valery Telfort.
Application Number | 20140180154 14/133173 |
Document ID | / |
Family ID | 35124783 |
Filed Date | 2014-06-26 |
United States Patent
Application |
20140180154 |
Kind Code |
A1 |
Sierra; Gilberto ; et
al. |
June 26, 2014 |
NON-INVASIVE MONITORING OF RESPIRATORY RATE, HEART RATE AND
APNEA
Abstract
A method and apparatus for estimating a respiratory rate of a
patient. The method comprises the steps of recording respiratory
sounds of the patient, deriving a plurality of respiratory rates
from the recorded sounds using a plurality of respiratory rate
estimating methods and applying a heuristic to the plurality of
derived respiratory rates, the heuristic selecting one of the
derived respiratory rates. The selected respiratory rate is the
estimated respiratory rate. The apparatus comprises at least one
sensor recording respiratory sounds of the patient, a plurality of
respiratory rate processors, each of the processors comprising a
respiratory rate calculating method, a heuristic means for
selecting one of the calculated respiratory rates and a display
means for displaying the selected respiratory as the estimated
respiratory rate.
Inventors: |
Sierra; Gilberto; (Montreal,
CA) ; Lanzo; Victor F.; (Laval, CA) ; Telfort;
Valery; (Laval, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MASIMO CORPORATION |
Irvine |
CA |
US |
|
|
Assignee: |
MASIMO CORPORATION
Irvine
CA
|
Family ID: |
35124783 |
Appl. No.: |
14/133173 |
Filed: |
December 18, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11547570 |
Jun 19, 2007 |
8641631 |
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PCT/CA05/00536 |
Apr 8, 2005 |
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14133173 |
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60560277 |
Apr 8, 2004 |
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Current U.S.
Class: |
600/529 |
Current CPC
Class: |
A61B 5/0205 20130101;
A61B 5/4818 20130101; A61B 2562/0204 20130101; A61B 5/7203
20130101; A61B 5/7257 20130101; A61B 7/00 20130101; A61B 7/003
20130101; A61B 5/726 20130101; A61B 5/0816 20130101 |
Class at
Publication: |
600/529 |
International
Class: |
A61B 7/00 20060101
A61B007/00 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 8, 2004 |
CA |
2,464,029 |
Claims
1-20. (canceled)
21. A method for estimating a respiratory rate of a patient, the
method comprising: receiving respiratory sound data from at least
one acoustic sensor, the respiratory sound data representing
respiratory sounds of a patient; deriving a plurality of
respiratory rates from the respiratory sound data with a processor
using a plurality of different respiratory rate estimating methods,
wherein the plurality of different respiratory rate estimating
methods comprises at least one time domain method and at least one
frequency domain method; and determining an estimated respiratory
rate based at least in part on the plurality of respiratory rates;
and outputting the estimated respiratory rate.
22. The method of claim 21, wherein the at least one time domain
method comprises determining a first respiratory rate of the
plurality of respiratory rates using a time domain representation
of the respiratory sound data, and the at least one frequency
domain method comprises determining a second respiratory rate of
the plurality of respiratory rates using a frequency domain
representation of the respiratory sound data.
23. The method of claim 21, wherein the at least one time domain
method comprises determining a first envelope of the respiratory
sound data in a time domain representation of the respiratory sound
data and deriving a first respiratory rate of the plurality of
respiratory rates based at least on a frequency of the first
envelope or a period of the first envelope.
24. The method of claim 23, wherein the at least one frequency
domain method comprises determining a second envelope of the
respiratory sound data in a frequency domain representation of the
respiratory sound data and deriving a second respiratory rate of
the plurality of respiratory rates based at least on a frequency of
the second envelope or a period of the second envelope.
25. The method of claim 21, wherein the at least one frequency
domain method comprises determining a second envelope of the
respiratory sound data in a frequency domain representation of the
respiratory sound data and deriving a second respiratory rate of
the plurality of respiratory rates based at least on a frequency of
the second envelope or a period of the second envelope.
26. The method of claim 21, wherein the estimated respiratory rate
comprises one of the plurality of respiratory rates.
27. The method of claim 21, wherein said determining the estimated
respiratory rate comprises determining the estimated respiratory
rate based at least on a comparison between a previously estimated
respiratory rate and the plurality of respiratory rates.
28. The method of claim 21, wherein at least some of the same set
of samples of the respiratory sound data are used to derive a first
respiratory rate of the plurality of respiratory rates and a second
respiratory rate of the plurality of respiratory rates, the first
respiratory rate derived using the at least one time domain method
and the second respiratory rate derived using the at least one
frequency domain method.
29. The method of claim 21, wherein the at least one acoustic
sensor comprises one acoustic sensor.
30. The method of claim 21, wherein the at least one acoustic
sensor is coupled to a neck of the patient.
31. An apparatus for estimating a respiratory rate of a patient,
the apparatus comprising: an input configured to receive
respiratory sound data from at least one acoustic sensor, the
respiratory sound data representing respiratory sounds of a
patient; and a processor configured to: derive a plurality of
respiratory rates from the respiratory sound data using a plurality
of different respiratory rate estimating methods, wherein the
plurality of different respiratory rate estimating methods
comprises at least one time domain method and at least one
frequency domain method; determine an estimated respiratory rate
based at least on the plurality of respiratory rates; and output
the estimated respiratory rate.
32. The apparatus of claim 31, wherein the at least one time domain
method comprises determining a first respiratory rate of the
plurality of respiratory rates using a time domain representation
of the respiratory sound data, and the at least one frequency
domain method comprises determining a second respiratory rate of
the plurality of respiratory rates using a frequency domain
representation of the respiratory sound data.
33. The apparatus of claim 31, wherein the at least one time domain
method comprises determining a first envelope of the respiratory
sound data in a time domain representation of the respiratory sound
data and deriving a first respiratory rate of the plurality of
respiratory rates based at least on a frequency of the first
envelope or a period of the first envelope.
34. The apparatus of claim 33, wherein the at least one frequency
domain method comprises determining a second envelope of the
respiratory sound data in a frequency domain representation of the
respiratory sound data and deriving a second respiratory rate of
the plurality of respiratory rates based at least on a frequency of
the second envelope or a period of the second envelope.
35. The apparatus of claim 31, wherein the at least one frequency
domain method comprises determining a second envelope of the
respiratory sound data in a frequency domain representation of the
respiratory sound data and deriving a second respiratory rate of
the plurality of respiratory rates based at least on a frequency of
the second envelope or a period of the second envelope.
36. The apparatus of claim 31, wherein the estimated respiratory
rate comprises one of the plurality of respiratory rates.
37. The apparatus of claim 31, wherein the processor is configured
to determine the estimated respiratory rate based at least on a
comparison between a previously estimated respiratory rate and the
plurality of respiratory rates.
38. The apparatus of claim 31, wherein the processor is configured
to use at least some of the same set of samples of the respiratory
sound data to derive a first respiratory rate of the plurality of
respiratory rates and a second respiratory rate of the plurality of
respiratory rates, the first respiratory rate derived using the at
least one time domain method and the second respiratory rate
derived using the at least one frequency domain method.
39. The apparatus of claim 31, wherein the at least one acoustic
sensor comprises one acoustic sensor.
40. The apparatus of claim 31, wherein the at least one acoustic
sensor is coupled to a neck of the patient.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to a method and apparatus for
the non-invasive monitoring of respiratory rate, heart rate and
apnea. In particular, the present invention relates to a method for
determining respiratory rate by combining the results of a
plurality of respiratory rate estimation methods and selecting a
preferred rate using a heuristic, and an apparatus implementing the
same.
BACKGROUND OF THE INVENTION
Respiratory Rate
[0002] Respiratory failure can become a life-threatening condition
in a few minutes or be the result of a build up over several hours.
Respiratory failure is very difficult to predict, and as a result
continuous monitoring of respiratory activity is typically
necessary in clinical, high-risk situations. Appropriate monitoring
equipment can be life-saving (see Folke M, Cernerud L, Ekstrom M,
HoK B; Critical Review of Non-invasive Respiratory Monitoring in
Medical Care; Medical & Biological Engineering & Computing
2003, Vol 41, pp. 377-383).
[0003] Numerous studies have shown that Respiratory Rate (RR)
provides one of the most accurate markers for indicating acute
respiratory dysfunction, and thus is used to track the progress of
patients in intensive care or post-operative care or anyone with
potentially unstable respiration (see Krieger B, Feinerman D, Zaron
A, Bizousky F; tinuous Noninvasive Monitoring of Respiratory Rate
in Critically III Patients; Chest/90/5/November, 1986, pp 632-634,
Browning I B, D'Alonzo G E, Tobin M J; Importance of Respiratory
Rate as an Indicator of Respiratory Dysfunction in Patients with
Cystic Fibrosis; Chest/97/6/June, 1990, pp 1317-1321, Gravelyn T R,
Weg J G; Respiratory Rate as an Indicator of Acute Respiratory
Dysfunction; JAMA, Sep. 5, 1980--Vol 244, No. 10, pp
1123-1125).
[0004] RR has also been shown to be a very accurate marker for
weaning outcomes for ventilated patients (see Tobin M J, Perez W.
Guenther M, Semmes B J, Mador J, Allen S J, Lodato R F, Dantzker D
R; The Pattern of Breathing during Successful and Unsuccessful
Trials of Weaning from Mechanical Ventilation; AM Rev Respir DIS
1986; 134:1111-1118 and EI-Khatib M, Jamaleddine G, Soubra R,
Muallem M; Pattern of Spontaneous Breathing: Potential Marker for
Weaning Outcome. Spontaneous Breathing Pattern and Weaning from
Mechanical Ventilation; Intensive Care Med (2001) 27:52-58) as it
exhibits high correlation with both the success and failure of
extubations.
[0005] During sedation, monitoring of the RR has been shown to be a
more rapid marker of the induction of anesthesia than any other
clinical measure, such as lash reflex, loss of grip, cessation of
finger tapping, and loss of arm tone (see Strickland T L, Drummond
G B; Comparison of Pattern of Breathing with Other Measures of
Induciton of Anesthesia, Using Propofol, Methohexital, and
Servoflurane; British Journal Of Anesthesia, 2001, Vol. 86, No. 5,
pp 639-644). During conscious sedation (narcotic sedation), there
is always a risk of respiratory depression. However, monitoring of
the respiratory pattern combined with pulse oximetry yield the most
useful information about the occurrence of respiratory depression
and changes in RR typically provide an earlier warning than does
pulse oximetry or end-tidal CO.sub.2 tension (see Shibutani K,
Komatsu T, Ogawa T, Braatz T P, Tsuenekage T; Monitoring of
Breathing Intervals in Narcotic Sedation; International Journal of
Clinical Monitoring & Computing; 8: 159-162, 1991).
[0006] Respiration monitoring is also useful during non critical
care, e.g. during exercise testing and different types of cardiac
investigations. In the latter case there is also need to time the
different phases of respiration, since the heart function is
modulated by respiration. A forthcoming area of application for
respiration monitoring may be that of home-care (see Hult P, et
al., An improved bioacoustic method for monitoring of respiration.
Technology and Health Care 2004; 12: 323-332).
[0007] Despite the obvious benefits of performing continuous
respiratory monitoring, the search for an accurate, non-invasive,
and non-obtrusive method to continuously monitor RR has proven to
be long and unsuccessful. Several technologies have been developed
in an attempt to fill this clinical gap, but none has gained
sufficient physician confidence to become a standard of care. In
this regard, inductive plethysmography, fiber optic humidification
and capnography are among the most popular technologies. Each of
these has advantages and disadvantages, but none has proven to be
clearly superior. More suitable technologies are still needed to
address such issues as: low signal to noise ratio, different breath
sound intensities, phase duration, variable breathing patterns,
interferences from non-biological sounds (electromagnetic
interference, movement artifacts, environmental noise, etc.), and
interference from biological sounds such as the heart beat,
swallowing, coughing, vocalization, etc.
[0008] Tracheal sounds, typically heard at the suprasternal notch
or at the lateral neck near the pharynx, have become of significant
interest during the last decade. The tracheal sound signal is
strong, covering a wider range of frequencies than lung sounds at
the chest wail, has distinctly separable respiratory phases, and a
close relation to airflow. Generally, the placement of a sensor
over the trachea is relatively easy as there is less interference
from body hair, garments, etc, as compared to chest-wall recording
sites.
[0009] The generation of tracheal sounds is primarily related to
turbulent air flow in upper airways, including the pharynx,
glottis, and subglottic regions. Flow turbulence and jet formation
at the glottis cause pressure fluctuations within the airway lumen.
Sound pressure waves within the airway gas and airway wall motion
are likely contributing to the vibrations that reach the neck
surface and are recorded as tracheal sounds. Because the distance
from the various sound sources in the upper airways to a sensor on
the neck surface is relatively short and without interposition of
lung tissue, tracheal sounds are often interpreted as a more pure,
less filtered breath sound. Tracheal sounds have been characterized
as broad spectrum noise, covering a frequency range of less than
100 Hz to more than 1500 Hz, with a sharp drop in power above a
cutoff frequency of approximately 800 Hz. While the spectral shape
of tracheal sounds varies widely from person to person, it is quite
reproducible within the same person. This likely reflects the
strong influence of individual airway anatomy.
[0010] Pulmonary clinicians are interested in tracheal sounds as
early indicators of upper airway flow obstruction and as a source
for quantitative as well as qualitative assessments of ventilation.
Measurements of tracheal sounds provide valuable and in some cases
unique information about respiratory health.
Apnea
[0011] Apnea monitoring by simple acoustical detection of tracheal
sounds is an obvious application and has been successfully applied
in both adults and in children. The detection of apneic events are
a normal derivative from the RR estimation. A temporary cessation
in breathing, typically lasting at least 10 seconds in duration, is
referred to as apnea. Longer pauses may be of sufficient duration
to cause a fall in the amount of oxygen in the arterial blood, and
have the potential to cause permanent organ damage, or, in the
extreme case, death. Adults with sleep apnea are very susceptible
to exacerbation of this condition post-surgery, and therefore their
respiration must be carefully monitored. Disordered breathing
during sleep is a common condition with an estimated prevalence of
up to 24% in men and 9% in women in North America It is associated
with excessive morbidity and increased mortality from
cardiovascular and cerebrovascular events and increased risk of
road traffic accidents (see Young et al., The occurrence of
sleep-disordered breathing among middle-aged adults, N Engl J Med
1993; 328: 1230-1235). The condition can be suspected clinically in
the presence of classic symptoms such as snoring, daytime
hyper-somnolence, obesity, and male gender. The diagnosis is
typically confirmed by polysomnography. The most common sleep
disorder is Obstructive Sleep Apnea Syndrome (OSAS), also known as
Sleep Apnea Hypopnea Syndrome (SAHS). This condition is so much
linked to excessive morbidity and mortality, that it is considered
a public health hazard at par with smoking (see Findley et al.,
Automobile accidents involving patients with obstructive sleep
apnea, Am Rev Respir //pis 1988; 138: 337-340).
Heart Rate
[0012] The rhythm of the heart in terms of beats per minute may be
easily estimated on the tracheal site by counting the readily
identifiable heart sound waves. Heart rate (FIR) is altered by
cardiovascular diseases and abnormalities such as arrhythmias and
conduction problems. The main cause of death in developed countries
is due to cardiovascular diseases and mostly they are triggered by
an arrhythmic event (ventricular tachycardia or ventricular
fibrillation). The HR is controlled by specialized pacemaker cells
that form the sinoatrial (SA) node located at the junction of the
superior vena cava and the right atrium. The firing rate of the SA
node is controlled by impulses from the autonomous and central
nervous system. It is now commonly accepted that the heart sounds
are not caused by valve leaflet movement per se, as earlier
believed, but by vibrations of the whole cardiovascular system
triggered by pressure gradients (see Rangayyan R M, Biomedica,
Signal Analysis 2002, IEEE Press Series, Wiley Inter-Science). The
normal (resting) HR is about 70 bpm. The HR is slower during sleep,
but abnormally low HR (below 60 bpm) during activity could indicate
a disorder called bradycardia. The instantaneous HR could reach
values as high as 200 bpm during vigorous exercise or athletic
activity; a high resting HR could be due to illness, disease, or
cardiac abnormalities, and is termed tachycardia.
SUMMARY OF THE INVENTION
[0013] In order to address the above and other drawbacks, there is
provided a method for estimating a respiratory rate of a patient
comprising the steps of recording respiratory sounds of the
patient, deriving a plurality of respiratory rates from the
recorded sounds using a plurality of respiratory rate estimating
methods and applying a heuristic to the plurality of derived
respiratory rates, the heuristic selecting one of the derived
respiratory rates. The selected respiratory rate is the estimated
respiratory rate.
[0014] There is also provided a method for signaling sleep apnea
comprising the steps of the above method wherein when the estimated
respiratory rate exceeds a predetermined interval an alarm is
raised.
[0015] Furthermore, there is provided a method for estimating a
respiratory rate of a patient. The method comprises the steps of
recording respiratory sounds of the patient and determining silent
intervals in the recorded sounds. The estimated respiratory rate is
equivalent to a frequency of the silent intervals.
[0016] Additionally, there is provided an apparatus for providing
an estimated respiratory rate of a patient. The apparatus comprises
at least one sensor recording respiratory sounds of the patient, a
plurality of respiratory rate processors, each of the processors
comprising a respiratory rate calculating method, a heuristic means
for selecting one of the calculated respiratory rates and a display
means for displaying the selected respiratory as the estimated
respiratory rate.
[0017] Also, there is provided an apparatus for signalling sleep
apnea in a patient. The apparatus comprises at least one sensor
recording respiratory sounds of the patient, a plurality of
respiratory rate processors where each of the processors comprising
a respiratory rate calculating methods, a heuristic means for
selecting one of the calculated respiratory rates and an alarm.
When the selected respiratory rate is slower than a predetermined
rate, the alarm is activated.
[0018] Other objects, advantages and features of the present
invention will become more apparent upon reading of the following
non-restrictive description of illustrative embodiments thereof,
given by way of example only with reference to the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In the appended drawings:
[0020] FIG. 1 is a front view of a patient with a sensor attached
to monitor respiratory sounds according to an illustrative
embodiment of the present invention;
[0021] FIG. 2 is a flow chart of a non-invasive respiratory rate,
heart rate and apnea monitor according to an illustrative
embodiment of the present invention;
[0022] FIG. 3 is a graph of a respiratory sound signal showing
artifacts (glitches) and with glitches removed according to an
illustrative embodiment of the present invention;
[0023] FIG. 4 is a graph of a respiratory sound signal, flow signal
and wavelet decomposition envelope according to an illustrative
embodiment of the present invention;
[0024] FIG. 5 is a graph of a respiratory sound signal divided into
frequency bands according to an illustrative embodiment of the
present invention;
[0025] FIG. 6 is a graph of a respiratory sound signal, flow signal
and squared envelope according to an illustrative embodiment of the
present invention;
[0026] FIG. 7 is a flow chart of speech processing method according
to an illustrative embodiment of the present invention; and
[0027] FIG. 8 is a graph of an example of a method based on a
quadratic detection function of the speech processing method
according to an illustrative embodiment of the present
invention.
DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS
[0028] Referring now to FIG. 1, a non-invasive respiratory rate,
heart rate and apnea monitor, generally referred to using the
reference numeral 10, will now be described. Biological sound
sensors 12, illustratively identical and for example as those
described in U.S. Pat. No. 6,661,161, detect the biological sounds
and vibrations emanating from the throat of a patient 14 and
produces an output electrical signal. Note that in a given
embodiment, a single biological sound sensor as in 12 or more than
one could also be used to detect biological sounds and vibrations.
The signals are transferred via appropriate electrical leads 16 to
a data acquisition system 18, which amplifies and filters the
electrical signal prior to converting them into a digital format.
Finally, the methods implemented in a computer 20 extract the
physiological information from the data and display the results
through a graphical user interface 22.
[0029] Acquisition System
[0030] The acquisition system comprises a Pentium based laptop
computer running Windows 2000 and a multi-channel custom designed
biosignal amplifier. The bandwidth of the sound channel(s) is
selectable from 0 to 1500 Hz. A sampling frequency for the sound
channel(s) was chosen and set at 3 kHz. The resolution of the A/D
conversion of the data acquisition board was 12 bits. The graphical
user interface was designed using the Labview.RTM. (National
lnstrument, Austin, Tex., USA) programming language and digital
signal processing methods were developed and tested in Matlab.RTM.
(The MathWorks, Inc., Natick, Mass., USA).
[0031] Methods
[0032] A flow chart indicating the elements of the signal
processing method used to estimate the RR from the respiratory
tracheal sound signal is provided in FIG. 2 (See also, Sierra G,
Telfort V, Popov B, Durand L G Agarwal R, Lanzo V; Monitoring
Respiratory Rate Based on Tracheal Sounds, First Experiences;
IEEE/EMBS 26th Conferences, San Francisco, Calif., 2004 which is
incorporated herein by reference). These elements are described
herein below.
[0033] Sound Signals and Segmentation Step. The respiratory sound
collection is illustratively performed at a sampling frequency of
3000 Hz. For analysis purposes, the sound signal is illustratively
segmented into 20 second blocks (although variable block lengths
could also be processed) with each block containing five seconds of
signal from the previous block (the overlap may also be variable).
The breathing frequency is estimated from the sampled 20 seconds of
data collection, averaged and displayed every minute.
[0034] Pre-processing Step. This step is aimed at ensuring a
respiratory sound signal which is as free of interference from
internal and external sources of sounds as possible. The following
actions are performed during the preprocessing step: [0035] a) A
comb-filter is applied to remove the interference from 60 Hz and
its harmonics; [0036] b) signals with extremely low signal to noise
ratio or high artifacts that saturate the amplifiers are excluded
(as will apparent to persons of skill in the art, if saturation
occurs, for example when a person talks or cough, signals cannot be
processed because the amplifier starts clipping these strong/high
amplitude signals. In this case it is preferred to exclude that
data segment from analysis. Concerning the case where recordings
with extremely low signal to noise ratio exist, they should also be
excluded because the signal contribution is almost null due to the
masking effect of noises.); [0037] c) glitches (or motion
artifacts) arising from rubbing clothes on the sensor, intermittent
contact, etc., are removed (or attenuated); [0038] d) filtering
based on the multi-resolution decomposition (MRD) of a wavelet
Transform; and [0039] e) removal of strong biological sounds that
do not saturate amplifiers but contribute to RR wrong estimation
(such strong biological sounds may modify some of the statistical
characteristics of the signal being processed, such as maximum
amplitude, etc, that are used to detect apnea or low signal to
noise ratio.).
[0040] Glitch Removal
[0041] Referring to FIG. 3, illustratively, the presence of
glitches is determined by sampling the respiratory tracheal data.
Samples having an amplitude in excess of three times (a value
determined as sufficient to identify a larges portion of glitches
while avoiding capturing other non-glitch signals) the value of the
standard deviation are categorized as glitches and removed and
replaced by a constant value equal to the amplitude of the sample
immediately preceding the removed sample. The mean and standard
deviation are illustratively calculated for every one second of
signal. The net effect is clipping the signal which means that
glitches are not completely removed but rather attenuated. A
previous attempt using twice the standard deviation was found to be
less effective as an attenuated signal was produced making the
estimation process significantly more difficult to accomplish,
specially in recordings with low signal to noise ratio. FIG. 3
shows in the top panel the input signal with scattered glitches and
bottom panel after removing some of the glitches.
[0042] Multi-Resolution Decomposition (MRD)
[0043] Referring to FIG. 4, respiratory sounds (as well as heart
sounds) are complicated multi-component non-stationary signals and
lend themselves to the use of non-stationary analysis techniques
for analyses. MRD allows splitting the respiratory signal into
different spectral bands. This decomposition allows for extensive
separation of sounds and allows the selection of the best frequency
band for processing the respiratory sound signals with the least
interference (see FIG. 5).
[0044] The frequency range of the above-mentioned frequency bands
is determined by the sampling frequency (illustratively Fs=300 Hz).
The output is the filtered signal contained in the frequency bands
from 187 Hz to 750 Hz (from 200 Hz to 800 Hz is considered to
contain the most important information of the tracheal signal). The
MRD approach of the wavelet transform is applied to the respiratory
sound signals based on a methodology known in the art for the
analysis of different cardiovascular bio-signals. See Sierra G,
Fetsch T, Reinhardt L. Martinez-Rubio A, Makijarvi M, Balkenhoff K,
Borggrefe M, Breithardt G., Multiresolution decomposition of the
signal-averaged ECG using the Mallat approach for prediction of
arrhythmic events after myocardial infarction, J Electrocardiol
1995, 29:223-234, Sierra G, Reinhardt L, Fetsch T, Martinez-Rubio
A, Makijarvi M, Yli-Mayry S, Montonen J, Katila T, Borggref M,
Breithardt G, Risk stratification of patients after myocardial
infarction based on wavelet decomposition of the signal-averaged
electrocardiogram, Annals of Noninvasive Electrocardiology 1997; 2:
47-58, Sierra G, Gomez M J, Le Guyader P, Trelles F, Cardinal R,
Savard P, Nadeau R, Discrimination between monomorphic and
polymorphic ventricular tachycardia using cycle length variability
measured by wavelet transform analysis, Electrocardiol 1998; 31:
245-255, and Sierra G, Morel P, Savard P, Le Guyader P,
Benabdesselam M, Nadeau R., Multiresolution decomposition of the
signal-averaged ECG of postinfarotion patients with and without
bundle branch block, Proceedings of the 18th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society,
Amsterdam, Oct. 31-Nov. 3, 1996, all incorporated herein by
reference.
[0045] The MRD approach to wavelet transform allows noise to be
removed from the input signals and the biological sounds to be
separated into different frequency bands. As is known in art, a
variety of wavelet families exist, one or more of which may be
appropriate in a particular application. No established rules exist
on how to evaluate the most suitable wavelet family for a specific
application. Illustratively, the `Coifflet` wavelet family was used
although other wavelet families, such as Lenaire-Battle and Symlet
may in some implementations be preferable.
[0046] Illustratively, the original signal is decomposed into ten
(10) frequency bands: 750 Hz-1500 Hz, 375 Hz-750 Hz, 187 Hz-375 Hz,
93 Hz-187 Hz, 46 Hz-93 Hz, 23 Hz-46 Hz, 12 Hz-23 Hz, 6 Hz-12 Hz, 3
Hz-6 Hz and from DC to 3 Hz. As mentioned above, the range of these
bands is determined by the sampling frequency (in the case at hand
Fs=3000 Hz although a person of skill in the art would understand
that a higher or lower sampling rate could be used). An
illustrative example of the amplitudes of the samples in the first
five (5) frequency bands is shown in FIG. 5. Referring to FIG. 5,
frequency bands two (2) and three (3) carry the most important
information to estimate respiratory rate. Frequency bands four (4)
and five (5) show clearly information related to a beating
heart.
[0047] The estimation of RR is hindered by several factors such as
the non-stationarity and non-linear nature of the respiratory sound
signal, the interference of non-biological (60 Hz, environment
noise, glitches, etc) and biological signals (heart beat, swallow,
cough, speech and others) and recordings with low signal to noise
(S/N) ratio. Another problem arises when one of the respiratory
phases is significantly stronger than the other and abnormal
patterns, for example those which are the result of certain
pulmonary diseases, although abnormal patterns are also present in
some individuals without these diseases.
[0048] As discussed above, apnea is a temporary cessation of the
respiratory function. The most widely used criterion as an
indication of apnea is 10 seconds or greater of duration for the
cessation. In the present proposed method, the duration, or time
threshold, of cessation of respiratory function indicating apnea is
a configurable parameter. Once the peaks in the envelope signal are
being detected, the time interval between two consecutive peaks is
estimated and compared with the configured time threshold. If it is
greater than the threshold, apnea is flagged as having been
detected and an alarm raised,
[0049] A special type of apnea occurs when no envelope peaks are
detected. To discriminate apnea from a `sensor disconnected` we use
the power spectrum analysis. While the sensor is connected to the
patient the low biological frequencies (frequencies with higher
power values in the band from 200 Hz to 300 Hz) will prevail. If
the sensor is disconnected higher frequencies (frequencies with
high power values over 500 Hz) will prevail. Additionally, to be
certain that no envelope peaks exist, both the root mean squared
(RMS) value of the envelope and an envelope history of the previous
one minute of signal are retained. If a significant drop of
amplitude happens in the 20 sec segment (to less than 12% of the
RMS value), then apnea is detected.
[0050] In order to overcome the difficulties found when using a
single method to produce an estimation of RR with high accuracy, a
multiple technique approach is used. As a result, the pre-processed
signals are analyzed using both envelope-based and
speech-processing related methods as described in more detail
herein below.
[0051] Envelope Related Methods for Estimating Respiratory Rate
[0052] The respiratory sound signal acquired on the tracheal site
can be modeled as sinusoidal signals from 200 Hz to 800 Hz
modulated by a slow oscillatory signal that represents inspiratory
and expiratory envelopes. Illustratively, the envelope is obtained
based on a Hilbert transform and decimation of the wavelet filtered
sound signal (from 187 Hz to 750 Hz) in a proportion of fifty to
one. The envelope is a very low frequency signal that modulates
those components of the respiratory sounds located in the band from
187 Hz to 750 Hz. in this regard, decimation means that the
envelope signal (obtained with the Hilbert transform) is down
sampled to have less data points to process and thus decrease the
execution time targeting real-time applications. For example, a
respiratory sound sampled at 3 kHz for a duration of 20 seconds
corresponds to 60000 data points, which when down sampled is only
1200 data points. The low frequency envelope is detected, followed
by the determination of its oscillatory period. Based on this
period (time lags between consecutive inhalations or exhalations)
the RR that would be accounted for after one minute has elapsed is
estimated.
[0053] Estimating Respiratory Rate Based on the Fast Fourier
Transform (FFT).
[0054] The power spectrum is estimated from the detrended and
windowed (Harming) envelope signal based on a nonparametric fast
Fourier transform (FFT). The magnitude squared of the FFT
coefficients formed the power Spectrum. Once the envelope is
represented in the frequency domain, typically the component with
the second highest peak has the information to correctly estimate
the RR result.
[0055] Estimating Respiratory Rate Based on Envelope Cotanting
[0056] In order to estimate RR based on envelope counting, all
possible peaks in the envelope signal of the respiratory sound
signal are identified and then the RR computed as a function of the
time between consecutive inhalations or exhalations enclosed in the
selected segment.
[0057] All possible peaks are detected by an analysis of samples
that fulfil a criterion of local maximum plus a criterion of
stability (amplitude higher than a number of samples before and
after the peak, see FIG. 6). In this regard, all peaks detected
within a given frame of the signal (illustratively 20 seconds but
other lengths are also possible) are passed through heuristic
validation method. This validation method selects only the peaks
higher than 10% of the amplitude of the higher peak on the given
signal. A rule of minimal possible distance between two consecutive
peaks is also used. Once all peaks have been determined, the mean
of the difference of consecutive acid peaks included in the data
segment (illustratively of 20 seconds in length.) is calculated.
The inverse of this value multiplied by 60 equals the estimation of
RR.
[0058] Estimating Respiratory Rate Based on the Autocorriation
Function
[0059] The autocorrelation function exploits the fact that a
periodic signal, even if it is not a pure sine wave, will be
similar from one period to the next. This is true even if the
amplitude of the signal is changing in lime, provided those changes
do not occur too rapidly. Once the autocorrelation function is
obtained, the first two peaks are analyzed to select the one with
the RR information. Typically, the second peak is the correct
choice (but th is is not always so). Samples where one respiratory
phase was more accentuated than the other and some other cases were
better estimated by the first peak.
[0060] Estimating Respiratory Rate Based on Wavelet Transform
[0061] The appropriate frequency band to be selected for RR
analysis changes according to the actual RR. Therefore guidance is
required for the right band selection. This guidance is provided by
the RR result of the FFT analysis, which allows choosing typically
two, or exceptionally three, possible frequency bands. In these
bands, the selection of peaks (based on maxima and minima analyses)
and the estimation of RR (two or three) is performed similarly as
explained in the method of envelope counting. Finally, the RR
closest to the RR estimated by the FFT is taken as the RR estimated
by the wavelet method.
[0062] Speech Processing Related Methods for Estimating Respiratory
Rate
[0063] The speech processing approach was used to overcome some
limitations of the methods that dealt directly with determining the
envelope of the respiratory signal, particularly in low SIN ratio
recordings. By combining methods based on the envelope and methods
based on the respiratory signal, a better estimation of the RR can
be achieved.
[0064] Using the speech processing approach, relevant information
of the signal under analysis is acquired through the processing of
small segments of signals (20 ms duration in this), case) where the
statistical properties of the signal are assumed to remain stable
(stationarity). The analysis is performed in the frequency domain
and the main tool is a short-time FFT technique. The flowchart of
FIG. 7 illustrates the approach.
[0065] The speech processing approach faces some challenges due to
the fact that there are no clear spectral differences between
inhalation and exhalation phases recorded at the tracheal site. To
overcome these limitations, a pilot signal with a frequency (for
example, 1 kHz) which is out of the frequency range of interest
(200 Hz to 800 Hz), and having an amplitude at least device the
minimum RMS of the respiratory signal is combined with the
respiratory signal. During intervals of silence between inhalation
and exhalation (i.e. where there is an absence of any respiratory
sounds) the pilot signal prevails. Likewise, during
inhalation/exhalation phases the respiratory signal prevails. As a
result, detection of the pilot signal gives an indication of a
silence interval.
[0066] An additional measure to help accentuate the difference
between respiratory signal and silence is the removal (or
attenuation) of biological sounds within the silence interval. This
function removes or attenuates biological sounds (mainly heart
sounds) found in the silence interval and that were not considered
glitches in the pre-processing stage. This processing is based on
an adaptive filter technique that takes the respiratory signal
contaminated with the heart sounds in a first channel (from 100 to
1500 Hz) and the heart sounds from a second channel (from 1 to 30
Hz, both channels simultaneously recorded) and produces as output a
respiratory signal `free` of heart interferences. Respiratory
signals combined with the 1 kHz pilot signal provide the input. A
FFT is applied to windows of 20 ms and parameters such as power
(FFT magnitude squared), centroid (frequency multiplied by power
divided by power) and a quadratic detection function (squared
frequency multiplied by power) are estimated. All these parameters
are used as RR estimators.
[0067] As an illustration, FIG. 8 displays on the top panel a
preprocessed respiratory sound signal. The middle panel shows a
signal produced by the quadratic detection function with peaks
indicating the position of zones of silence. The bottom panel
represents the autocorrelation function of the signal in the middle
panel. The second peak of the autocorrelation is used to estimate
the RR.
[0068] Finally, a scoring system, comprising a heuristically-based
analysis of the individual estimators, is applied to determine the
final RR based on the results of the individual estimators.
[0069] Scoring System
[0070] The final respiratory rate (FR) for a particular segment is
determined as a function of the RR as determined by each of the
individual estimators (as discussed hereinabove) as well as the
final respiratory rate of the previous segment (FR Old). In an
illustrative embodiment, the individual estimators are examined and
if there is one value of RR which is predominant, FR is set to the
predominant RR value. If no value of RR is predominant, but two or
three values have equal representation, then the value which is
closest to FR Old is selected. Finally, if more than three values
have equal representation FR is set to the same value as FR
Old.
[0071] Heart Rate
[0072] Heart sounds are also present among the sounds captured on
the trachea site. For the estimation of the respiratory rate they
are considered as `noise` and are removed. However, these sounds
allow the possibility of estimating heart rate (one of the most
important vital signs) easily. We have implemented a second
hardware channel with filter settings (20-200 Hz) to enhance the
detection of heart sounds and reject all other biological sounds
(including respiratory). Applications involving the
cardio-respiratory interactions, regulation of the autonomous
nervous system on the cardiovascular system and others will be
easily targeted. Once the heart sounds are filtered, sound peaks
are detected. Based on the inter-peaks timing the heart rate is
estimated (beats per minute).
[0073] Although the present invention has been described
hereinabove by way of an illustrative embodiment thereof, this
embodiment can be modified at will, within the scope of the present
invention, without departing from the spirit and nature of the
subject of the present invention.
* * * * *