Pulse oximetry-derived respiratory rate in general care floor patients

Abstract

Respiratory rate is recognized as a clinically important parameter for monitoring respiratory status on the general care floor (GCF). Currently, intermittent manual assessment of respiratory rate is the standard of care on the GCF. This technique has several clinically-relevant shortcomings, including the following: (1) it is not a continuous measurement, (2) it is prone to observer error, and (3) it is inefficient for the clinical staff. We report here on an algorithm designed to meet clinical needs by providing respiratory rate through a standard pulse oximeter. Finger photoplethysmograms were collected from a cohort of 63 GCF patients monitored during free breathing over a 25-min period. These were processed using a novel in-house algorithm based on continuous wavelet-transform technology within an infrastructure incorporating confidence-based averaging and logical decision-making processes. The computed oximeter respiratory rates (RRoxi) were compared to an end-tidal CO2 reference rate (RRETCO2). RRETCO2 ranged from a lowest recorded value of 4.7 breaths per minute (brpm) to a highest value of 32.0 brpm. The mean respiratory rate was 16.3 brpm with standard deviation of 4.7 brpm. Excellent agreement was found between RRoxi and RRETCO2, with a mean difference of -0.48 brpm and standard deviation of 1.77 brpm. These data demonstrate that our novel respiratory rate algorithm is a potentially viable method of monitoring respiratory rate in GCF patients. This technology provides the means to facilitate continuous monitoring of respiratory rate, coupled with arterial oxygen saturation and pulse rate, using a single non-invasive sensor in low acuity settings.

Full text

ORIGINAL RESEARCH
Pulse oximetry-derived respiratory rate in general care floor
patients
Paul S. Addison •James N. Watson •
Michael L. Mestek •James P. Ochs •
Alberto A. Uribe •Sergio D. Bergese
Received: 6 December 2013 / Accepted: 2 April 2014
The Author(s) 2014. This article is published with open access at Springerlink.com
Abstract Respiratory rate is recognized as a clinically
important parameter for monitoring respiratory status on the
general care floor (GCF). Currently, intermittent manual
assessment of respiratory rate is the standard of care on the
GCF. This technique has several clinically-relevant short-
comings, including the following: (1) it is not a continuous
measurement, (2) it is prone to observer error, and (3) it is
inefficient for the clinical staff. We report here on an algorithm
designed to meet clinical needs by providing respiratory rate
through a standard pulse oximeter. Finger photoplethysmo-
grams were collected from a cohort of 63 GCF patients
monitored during free breathing over a 25-min period. These
were processed using a novel in-house algorithm based on
continuous wavelet-transform technology within an infra-
structure incorporating confidence-based averaging and log-
ical decision-making processes. The computed oximeter
respiratory rates (RR
oxi
) were compared to an end-tidal CO
2
reference rate (RR
ETCO2
). RR
ETCO2
ranged from a lowest
recorded value of 4.7 breaths per minute (brpm) to a highest
value of 32.0 brpm. The mean respiratory rate was 16.3 brpm
with standard deviation of 4.7 brpm. Excellent agreement was
found between RR
oxi
and RR
ETCO2
, with a mean difference of
-0.48 brpm and standard deviation of 1.77 brpm. These data
demonstrate that our novel respiratory rate algorithm is a
potentially viable method of monitoring respiratory rate in
GCF patients. This technology provides the means to facilitate
continuous monitoring of respiratory rate, coupled with arte-
rial oxygen saturation and pulse rate, using a single non-
invasive sensor in low acuity settings.
Keywords Respiratory rate Pulse oximeter Continuous
monitoring Low acuity monitoring
1 Introduction
Respiratory rate (RR) is well known to be a clinically
important parameter owing to the fact that it provides
important information pertaining to many aspects of a
patient’s respiratory status. Frequently, a change in RR is
one of the earliest and more important indicators that
precedes major clinical manifestations of serious compli-
cations such as respiratory tract infections, respiratory
depression associated with opioid consumption, anaesthe-
sia and/or sedation, as well as respiratory failure [5].
Accordingly, the monitoring of RR is of paramount
importance in several clinical conditions, particularly in
settings where direct and close clinician supervision is
minimal, such as the general care floor (GCF).
In current clinical practice, the standard of care technique
for monitoring RR is intermittent, manual observation. This
technique consists of visual assessment of patient breathing
for a one-minute period of time to establish RR via a manual
count. While manual observation is currently in widespread
P. S. Addison (&)J. N. Watson
Covidien Respiratory and Monitoring Solutions, Edinburgh,
Scotland, UK
e-mail: paul.addison@covidien.com
M. L. Mestek J. P. Ochs
Covidien Respiratory and Monitoring Solutions, Boulder, CO,
USA
A. A. Uribe
Department of Anesthesiology, The Ohio State University
Medical Center, Columbus, OH, USA
S. D. Bergese
Departments of Anesthesiology and Neurological Surgery,
The Ohio State University Wexner Medical Center, Columbus,
OH, USA
123
J Clin Monit Comput
DOI 10.1007/s10877-014-9575-5

use in many patient care settings, there are several clinically
important shortcomings associated with this approach. For
example, manual counting requires clinical staff interven-
tion, which often leads to low rates of compliance for RR
monitoring. Furthermore, it is prone to significant errors that
may stem from a number of sources, including failure to
observe distinct breaths; counting and rounding errors;
and, not least, erroneous respiration due to self-conscious
breathing caused by patient-clinician interaction [14].
Several technological approaches have been advanced in
the attempt to address the limitations associated with manual
observation of RR. Examples of these technologies include
several different continuous monitoring devices such as end-
tidal CO
2
(ETCO
2
), ECG-based trans-thoracic impedance
systems, nasal thermistors, and abdominal and chest bands
(i.e. respiratory inductance plethysmography) [6]. However,
continuous measurements using these techniques all involve
costly, specialized, and intrusive equipment that ultimately
is limiting to their practical application in many care set-
tings, particularly in low acuity settings such as the GCF.
In addition to the technical challenges and expense of the
current continuous measurement options, such devices may
be cumbersome for the patient and thus patient compliance
may be low due to physical discomfort. Indeed, it has pre-
viously been demonstrated that when ETCO
2
monitoring is
employed on the GCF, many patients remove the nasal
cannula due to physical annoyance [7]. Evaluation of the
current methodologies, both manual observation and tech-
nological approaches, reveals that there is an obvious need
for an alternative methodology for monitoring RR that
overcomes the deficiencies of these approaches. An ideal
clinical solution would be one that is continuous in nature,
non-invasive, simple to operate, unobtrusive, clinically
acceptable, and robust in the presence of signal interference.
One such non-invasive continuous monitoring technol-
ogy candidate that is affordable, user-friendly, and already
accepted in clinical practice is pulse oximetry. Recent
evidence indicates that the measurement of RR from the
pulse oximeter signal, or photoplethysmogram (PPG), may
be possible. Numerous groups have previously demon-
strated that evaluating the respiratory-related fluctuations
from the PPG signal is both a biologically plausible and
technically attainable approach to obtaining RR using a
variety of methods including: inspection of the respiratory
oscillations in the filtered PPG [15,25,27,31–33]; fre-
quency spectra-based approaches [28,35]; frequency-based
smart fusion approaches using multiple modulations [18];
independent component analysis [36]; short-time Fourier
transform analysis [34]; neural networks [26]; variable
frequency complex demodulation methods [13,21]; auto-
regressive models [23,24,29]; pulse width variability [17];
and approaches based on the continuous wavelet transform
by our own group [2,10–12,22,30].
This cumulative body of evidence strongly suggests the
possibility of deriving RR from a single combined sensing
system that leverages standard pulse oximetry. A techno-
logical approach providing these metrics from a single
sensor would yield tremendous clinical utility in a manner
that is cost effective and efficient from a workflow per-
spective. We have shown in a recent study [2] that the
respiratory rate determined from our in house algorithm
(RR
oxi
) represents a potentially viable technology for the
measurement of RR in healthy subjects. The algorithm has
the necessary filtering, logic and decision making processes
required to provide a fully-automated technology capable
of coping with the extremes of data characteristics in the
clinical environment and ultimately provide a clinically
useful number for display. The purpose of the follow-up
study we report here was to demonstrate the viability of
RR
oxi
in a GCF patient population.
2 Methods and materials
2.1 The respiratory rate algorithm (RR
oxi
)
Respiratory activity may cause the PPG to contain three
fundamental waveform modulations [2,18,47]. These can
be seen in the example signal segment in Fig. 1and
described as follows:
1. Baseline (DC) Modulation: caused by changes in
venous return secondary to changes in intrathoracic
pressure throughout the respiratory cycle. During
inspiration, the decrease in intrathoracic pressure
results in a small decrease in central venous pressure
increasing venous return. The opposite occurs during
expiration. As more blood is shunted from the low
pressure venous system at the probe site and the
venous bed cyclically fills and drains, the baseline is
modulated accordingly (‘BM’ in Fig. 1).
Inhale
RSA
Change in pulse period
indicative of RSA
Exhale
BM
AM
Fig. 1 A segment of PPG exhibiting the three modulations. BM
baseline modulation (cardiac pulses riding on top of baseline
modulation), AM amplitude modulation (cardiac pulse amplitudes
varying over respiratory cycle), RSA respiratory sinus arrhythmia
(pulse period varying over respiratory cycle). Regions of inhalation
and exhalation are shown schematically on one respiratory cycle
J Clin Monit Comput
123

2. Pulse Amplitude Modulation: Respiratory-related changes
in intrathoracic pressure alter cardiac function. Principally,
this stems from decreased left ventricular stroke volume
during inspiration, leading to decreased pulse amplitude
during this phase of respiration (‘AM’ in Fig. 1).
3. Respiratory Sinus Arrhythmia (RSA): This is a vari-
ation in heart rate that occurs throughout the respira-
tory cycle. Specifically, it has been well-documented
that heart rate increases during inspiration and
decreases during expiration. However, the presence
of RSA is influenced by several factors including age,
disease status, and physical fitness. While the precise
mechanisms of RSA remain controversial, in general,
it is a result of autonomic nervous system activity
fluctuation during respiration (‘RSA’ in Fig. 1).
We have developed a powerful signal processing meth-
odology that can extract respiratory information from the
PPG. This is embodied within our RR algorithm (RR
oxi
)that
optimizes the extraction of respiratory information from
within the PPG signal. This is achieved by deriving a series
of new characterizing signals which are optimally config-
ured to enhance respiratory information content. These are
fed into the main analysis engine which processes the
characterizing signals in order to determine a RR. The
analysis engine incorporates advanced signal processing
techniques based on continuous wavelet transform meth-
ods [1]. The wavelet transform of a signal x(t)isdefinedas:
Tða;bÞ¼ 1ffiffiffi
a
pZþ1
1
xðtÞwtb
a

dt ð1Þ
where w*(t) is the complex conjugate of the wavelet func-
tion w(t),ais the dilation or scale parameter of the wavelet,
bis the location parameter of the wavelet, tis time and x(t)
is the signal under investigation: this may be the PPG or
secondary signals derived from the PPG. In our work we
employ tunable complete Morlet wavelets (1) of the form:
wðtÞ¼ 1ffiffiffi
p
4
peixotex2
o
2

et2
2ð2Þ
where x
o
is the central frequency of the mother wavelet.
The second term in the brackets is known as the correction
term, as it corrects for the non-zero mean of the complex
sinusoid of the first term. The RR
oxi
algorithm iterates
every 5 s, deriving an RR from the previous 45-s segment
of infrared PPG. These current rates are averaged further
with the previously displayed rate, and continue through
additional logic before displaying a final reported rate to
the user. We provide additional detailed information on the
algorithm in references [2,48–53] including the state
machine-driven logic used to determine whether to report
the information to the end user or blank out the display [2].
2.2 Study details
2.2.1 Subjects
Subsequent to IRB approval, the study was conducted at
The Ohio State University Medical Center in Columbus,
OH, USA. A cohort of 63 adult patients was recruited for
the trial (33 male and 30 female). RR was determined from
the data acquired from all subjects.
The study exclusion criteria were:
•Contact allergies that may cause a reaction to standard
adhesive materials found in the sensors used.
•Abnormalities that may prevent proper application of
the pulse oximeter probe.
•Previous injury or co-morbidity to fingers or hands that
may change blood flow and vascular supply.
•Pregnant or lactating women.
The latter being a standard criterion for the site where
most of the studies exclude pregnant or lactating woman.
2.2.2 Protocol
The data were acquired using a standard Nell-1 oximeter
OEM module with a Nellcor Max-A disposable probe
attached firmly to the index finger of the right hand.
A Datex-Ohmeda CardioCap/S5 device was used to record
an end-tidal CO
2
signal from the patient using a nasal
cannula. Once the subject was comfortable with the
equipment, the PPG signal was acquired for a duration of
approximately 25 min. A spontaneous breathing protocol
was conducted whereby the subjects were asked to relax
and breathe naturally. In addition, research clinical per-
sonnel recorded any external artifacts or subjects’ move-
ments during data collection to ensure data quality. The
patients were observed but no other instructions were
given.
3 Results
Participant characteristics are detailed in Table 1. The
participants exhibited a wide range of medical conditions.
These are detailed in Table 2.
Table 1 Selected subject characteristics
Variable Mean ±SD Min Max
Age (year) 55 ±17 24 89
Weight (kg) 92 ±26 45 170
Height (cm) 170 ±10 150 198
BMI (kg/m
2
)31±91561
J Clin Monit Comput
123

Figure 2contains the spread of the RR during the study.
The histogram plot is comprised of 16,980 RR
ETCO2
data
points taken at 5-s increments over the whole population;
that is, at each 5 s increment, the previous 45 s of data is
used to determine RR
oxi
.RR
ETCO2
ranged from a lowest
recorded value of 4.7 brpm to a highest value of 32.0 brpm.
The mean rate was 16.3 brpm with a standard deviation of
4.7 brpm. To ensure that we had the highest possible
confidence in the ETCO
2
reference, we took steps to
eliminate regions of poor quality ETCO
2
waveforms that
might typically be included in normal device operation.
For example, ETCO
2
devices will often report through a
degree of talking and motion induced artifact; however,
the actual RR is often ambiguous in these regions.
Therefore, in an effort to ensure a high confidence in the
ETCO
2
RR, we ignored these regions in the performance
analysis. Of the regions tested, a rate was not
computed from the RR
ETCO2
for 17 % of the data due
to poor or ambiguous signal quality or the device
recalibrating.
Figure 3contains a histogram of the differences
between RR
oxi
and RR
ETCO2
.RR
ETCO2
reported a rate
83.0 % of the time. RR
oxi
reported a rate 92.4 % of the
time. The overlap of non-reporting times for the two RRs
was 1.2 %. The mean difference between the rates was
-0.48 brpm with a standard deviation of 1.77 brpm. The
root mean square deviation (RMSD) was 1.83 brpm (Pulse
oximetry-based parameters SpO
2
and pulse rate use RMSD
as a measure that combines both bias (mean error) and
precision (SD error) to give a measure of total error as per
the ISO80601-2-61 standard [54]. We have adopted this in
lieu of a standard for pulse oximetry-derived RR.) Figure 4
expands the view of the data further. The figure contains a
Bland–Altman plot of the data. We have used a density
scale of the data points to indicate the density of points
contributing to the Bland–Altman plot. The mean and ±3
standard deviations of the data are plotted on the figure. We
advocate these ‘‘density Bland–Altman’’ plots over the
traditional method of simply plotting the data points for
large sets. An appreciation of the number of points in a
region is often difficult using the traditional method where
large numbers of points may be plotted over each other,
Table 2 Subject medical condition classification
Medical condition Number Percentage (%)
Respiratory
Asthma 4 6.3
Chronic obstructive pulmonary disease 8 12.7
Dyspnea 2 3.2
Obstructive sleep apnea 7 11.1
Pneumonia 2 3.2
Cardiovascular
Aortic stenosis 3 4.8
Coronary artery disease 8 12.7
Heart failure 5 7.9
Hypertension 24 38.1
Stroke 3 4.8
Metabolic/autonomic
Hyperlipidemia 9 14.3
Obesity 31 49.2
Neuropathy 12 19.0
Type II diabetes mellitus 15 23.8
Renal
End stage renal disease 2 3.2
Fig. 2 Distribution of breathing rates (RR
ETCO2
) of GCF patients
during the trial
Fig. 3 Distribution of differences between RR
ETCO2
and RR
oxi
J Clin Monit Comput
123

whereas the density plot provides a much clearer picture of
where the majority of the data lies.
4 Discussion
We have demonstrated an algorithm for the computation of
RR from a standard, commercially available pulse oxime-
try system during spontaneous breathing in GCF patients in
the GCF setting. RR
oxi
was derived for data collected from
a 63-subject cohort and compared to a reference rate,
RR
ETCO2
. We found excellent agreement between the two
with an RMSD of 1.83 brpm. Significantly, the algorithm
was developed and tested using a wide range of in-hospital
patient data [2,55–62] and healthy subject data and
therefore is not tuned specifically for GCF patients. This
was done to mitigate overtraining on GCF data and to
ensure that the algorithm has the ability to cope with as
wide a range of situations in the field as possible. These
results are in accordance with a previous study by our
group of healthy volunteers [2]. Compared to this earlier
study, there is a slight decrease in performance (mean 0.48
vs. 0.23 brpm; STD: 1.77 vs. 1.14 brpm and RMSD: 1.83
vs. 1.16 brpm). This may be a result of the more chal-
lenging nature of the GCF patients compared to the healthy
subjects studied in this earlier work. In order to assess the
effects of intra-patient dependency within the analysis, we
investigated the inter-patient variability of the results. The
individual statistics for each subject were computed and it
was found that the mean RMSD was 1.73 brpm and stan-
dard deviation of the RMSD’s was 1.28 brpm. This sup-
ports the likelihood of an acceptably small intra-patient
variability in the difference between RR
oxi
and the
reference.
Importantly, this performance was accomplished using a
single sensor that combines the ability to monitor RR,
arterial oxygen saturation (SpO
2
), and pulse rate; these
findings highlight a unique, clinically useful approach to
monitoring multiple respiratory variables in a continuous,
non-invasive, and easy-to-use manner. An advantage of the
use of a single senor for SpO
2
, HR and RR is that it
potentially reduces the number of alarm modes. For
example, a single ‘‘sensor off’’ or ‘‘motion artifact’’ alarm
could cover all three parameters, whereas in two separate
devices there is a likelihood of significant increased false
alarm rate, with a corresponding likelihood of increased
alarm fatigue and workflow disruption [46].
The significance of the magnitude of absolute error
associated with respiratory rate depends on the on the true
rate. The results published here mostly lie in a central range
of respiration between approximately 10 and 20 brpm. We
performed sub-analyses of the results for RR \10 brpm and
[20 brpm and found the mean, SD and RMSD to be 1.07,
2.42, 2.65 brpm respectively for rates \10 brpm and 1.33,
1.76, 2.20 brpm respectively for rates greater than 20 brpm.
These sub-analyses involved 6.1 and 17.1 % data points
respectively of the total collected. These values are slightly
greater than for the whole data set. It is important, how-
ever, not to over-interpret these results as there are rela-
tively few data at these extremes. Our group is very aware
of the importance of the algorithm performance at the
extremes and have ongoing work considering patient
groups that cover these regions [55,56].
The physiological processes that both enable and con-
found the measurement of respiratory rate appear well
understood in this space and link to the wider literature on
the causes of erroneous PPG components including:
vasotone, vasomotion, posture, patient motion, tempera-
ture, metabolic state, pain, drug administration, lung
compliance, upper airway obstruction, edema, heart rate,
respiratory rate, catheterization, ablation, the venous blood
component and arrhythmia. These are documented more
fully in the work of others [2,16,37–45]. There are little
data on GCF monitoring of the PPG for RR as this is an
area of care where continuous oximeter monitoring is not
regularly carried out. However, we have observed in our
own work that operating room data exhibits considerable
PPG artifact from: motion, drug administration, vasomo-
tion, administration of fluids, heart rate changes, etc.,
whereas the GCF is a more benign environment. If con-
tinuous PPG-based RR were to gain traction in the GCF,
we expect that much of the signal would be of good quality
with intermittent instances of severe artifact due to patient
motion: voluntary or assisted.
Fig. 4 Bland-Altman density plot of the data (lowest density of
points to highest density =Dark Blue,Light Blue,Green,Yellow,
Red)
J Clin Monit Comput
123

It is worth noting that our wavelet-based algorithm does
not tend to exhibit the erroneous low rates posted across the
range of reference rates prevalent in the work Karlen et al.
[18]. We believe that this is due to increased flexibility in
identifying and partitioning respiratory components inher-
ent in our approach over their smart-fusion, frequency
spectrum based approach. Our results compare favourably
with those reported by other groups [13–18,21–29,31–36].
However, such comparisons should be considered carefully
as the results are highly dependent on the characteristics
of the raw signal and its manipulation, exclusion criteria,
manual selection of data (if applicable), the patient group
studied and, of course, the algorithmic implementation
(including pre-processing, processing and post-processing
steps). The determination of a clinically useful physiolog-
ical parameter is therefore a distinctly non-trivial task.
An important aspect of our work is that it targets the
development of a fully-automated algorithm capable of
coping with the extremes of data characteristics in the
clinical environment: i.e. the RR
oxi
values generated are
those that would be displayed on the device screen to the
clinician. A sophisticated algorithmic infrastructure is
therefore required to take the raw biosignal from the
hardware, process it, present it to the core algorithm, then
apply further post-processing to the output in order to
produce a value with the integrity necessary for display on
the screen of a medical monitoring device [2].
It is well-established that many patient deaths on the
hospital GCF may be prevented, at least in part, through more
vigilant monitoring aimed at detecting clinically meaningful
antecedents to patient deterioration [8]. For example,
Hodgetts et al. [9] reported in a root-cause analysis that
approximately 80 % of the cardiac arrests occurring on the
GCF were preventable. It has been reported that approxi-
mately 40 % of such alterations are considered respiratory in
nature, underscoring the importance of attentive respiratory
monitoring in this setting [20]. Despite this, it has been
suggested that upon the arrival of a hospital rapid response
team, up-to-date vital signs, such as RR, are not available for
three out of four patients [4]. Clearly, providing this infor-
mation in a continuous and timely manner to clinicians could
provide the foundation for improved patient outcomes on the
GCF. A critical factor contributing to respiratory distress on
the GCF is the administration of opioid analgesia and asso-
ciated respiratory depression [3]. Consequently, the Anes-
thesia Patient Safety Foundation has issued guidance
suggesting that for patients receiving post-operative opioid
analgesia administration, vital sign monitoring should occur
with increased frequency [19]. Thus, monitoring RR con-
tinuously may offer an avenue to specifically reduce the
deleterious impact of opioid-induced respiratory depression.
Despite the overwhelming importance of a patient’s
respiratory status while on the GCF, manual observation
remains the standard of care for assessment of RR. It is
clear to see that this intermittent approach is lacking
because it leaves substantial periods of time in which the
patient’s respiratory status is unmonitored. Given the
rapidity with which a patient’s respiratory status may
devolve, critical clinical information during these unmon-
itored periods of time leave the patient susceptible to the
untoward clinical complications mentioned above. In
addition to patient safety considerations, there is also a
clinical burden placed on the staff to monitor RR at peri-
odic intervals. By establishing a means through which RR
can be monitored continuously, in conjunction with pulse
oximetry from a single sensor site, our algorithm provides a
mechanism to potentially improve patient outcomes on the
GCF while improving compliance with vital sign moni-
toring requirements.
4.1 Concluding remarks
Our results demonstrate that the RR
oxi
algorithm is a
potentially viable technological approach for monitoring
RR in a diverse GCF patient population. Currently, pulse
oximeters use the differential absorption of red and infrared
light between oxygenated hemoglobin and deoxygenated
hemoglobin to provide a measure of oxygen saturation,
with heart rate also provided. These devices do not mea-
sure RR, and will only detect inadequate respiration after
hypoxia has occurred. Hence, pulse oximetry may be
considered a lagging indicator of evolving respiratory
complications, limiting its efficacy in this domain. How-
ever, the combination of pulse oximetry with RR, in a
single sensor, may provide earlier indication of evolving
respiratory compromise. We believe that the RR
oxi
algo-
rithm would provide this vital information by offering the
capability to monitor RR via a probe that is routinely
attached to patients in many clinical situations, thus
enhancing patient safety and facilitating reduced clinical
workflow with combined RR and oxygen saturation
monitoring.
Conflict of interest Paul S. Addison, James N. Watson, Michael L.
Mestek, James P. Ochs are employees of Covidien who sponsored the
research. Drs Uribe and Bergese are consultants to Covidien.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use, dis-
tribution, and reproduction in any medium, provided the original
author(s) and the source are credited.
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