Relevance of Linearizing Nasal Prongs for Assessing
Hypopneas and Flow Limitation During Sleep
RAMON FARRÉ, JORDI RIGAU, JOSEP M. MONTSERRAT, EUGENI BALLESTER, and DANIEL NAVAJAS
Unitat de Biofísica i Bioenginyeria, Facultat de Medicina, Universitat de Barcelona and Institut de Pneumologia, Hospital Clinic,
Institut d’Investigacions Biomèdiques August Pi Sunyer, Barcelona, Spain
Respiratory disturbances in patients with the sleep apnea–hypopnea syndrome (SAHS) may be detected by means of nasal prongs
(NP) pressure (PNP). Nevertheless, PNP is nonlinearly related to flow
·
(V). Our aim was to demonstrate the relevance of linearizing PNP
·
for assessing hypopneas and flow limitation in SAHS. V was measured with a pneumotachograph during the hypopneas and flow
limitation events in a continuous positive airway pressure (CPAP)
titration in six patients with severe SAHS. These flow patterns were
reproduced by a flow generator through an analog of the nares
·
and recorded by NP. PNP was linearized [VNP ⫽ (PNP)1/2] by a spe·
cially designed analog circuit. For each event we used V, PNP, and
·
VNP to compute the hypopnea flow amplitude (HFA) and a flow
limitation index (FLI). Owing to NP nonlinearity, PNP considerably
·
misestimated HFA and FLI. By contrast, VNP provided HFA and FLI
·
·
values that were very close to those obtained from V: HFA (VNP) ⫽
·
·
·
1.098 ⭈ HFA(V) ⫺ 0.063 (r2 ⫽ 0.98) and FLI(VNP) ⫽ 1.044 ⭈ FLI(V) ⫹
2
0.004 (r ⫽ 0.99). Square-root linearization of NP greatly increases
the accuracy of quantifying hypopneas and flow limitation. This
procedure, which could be readily carried out in routine practice
by means of the analog circuit we developed, is of interest in optimizing the assessment of respiratory sleep disturbances in SAHS.
The sleep apnea–hypopnea syndrome (SAHS) is characterized by reductions in ventilation resulting from total or partial
collapse of the upper airway. As has been recently pointed out,
accurate quantification of the magnitude of flow reductions is
required because one of the most important indices used to
determine the severity of the disorder, the apnea–hypopnea
index, is based on the detection of breathing cycles with reduced flow (1, 2). Moreover, accurate flow assessment is helpful in detecting and classifying inspiratory flow–limited events
by analyzing the time-course pattern of the flow curve (3, 4).
Although the pneumotachograph is the reference device
for measuring flow (1), its use in routine diagnostic studies is
not practical because it requires the application of a mask to
the patient during sleep. The most common device used to assess flow in clinical practice is the thermistor/thermocouple.
Although this device is useful to detect apneas, it is not suitable for accurately quantifying the degree of hypopnea or for
tracking the flow time course to detect flow limitation (5). To
find out a simple alternative device to assess flow during sleep,
recent studies have demonstrated that pressure recorded at
nasal prongs is a useful signal to detect flow events during
sleep (6–10). Although this device exhibits an excellent time
response for tracking the details of the flow time course, its
nonlinear response could pose a problem for accurately measuring flow (7, 11). In fact, in a previous study in awake healthy
(Received in original form June 12, 2000 and revised form October 28, 2000)
Supported in part by Comisión Interministerial de Ciencia y Tecnología (CICYT,
SAF99-0001) and by Dirección General de Enseñanza Superior e Investigación
Científica (DGESIC, PM98-0027).
Correspondence and requests for reprints should be addressed to Ramon Farré,
Ph.D., Unitat Biofísica i Bioenginyeria, Facultat de Medicina, Casanova 143,
08036 Barcelona, Spain. E-mail: [email protected]
Am J Respir Crit Care Med Vol 163. pp 494–497, 2001
Internet address: www.atsjournals.org
subjects we showed that the relationship between the nasal
prongs pressure and actual flow was quadratic (12). Moreover,
we also showed that performing the square root of the nasal
prongs signal could be a suitable procedure to linearize and,
therefore, optimize the device.
To date there are no data evaluating the impact of nasal
prongs nonlinearity on the quantification of hypopneas and
flow limitation events in sleep studies in SAHS. There is also
a lack of data demonstrating the potential improvement
achieved by linearizing nasal prongs in this application. Accordingly, the main aim of this work was to evaluate the relevance of linearizing the nasal prongs signal for assessing flow
in patients with SAHS. A secondary aim was to design and
test a simple analog circuit to facilitate the real-time linearization of nasal prongs in routine use. We carried out a study
based on flow signals recorded with a reference pneumotachograph in a polysomnography-controlled continuous positive airway pressure (CPAP) titration in six patients with severe SAHS. These flow signals, including hypopneas, flow
limited events, and normal breathing cycles, were reproduced
through a nasal analog and recorded by nasal prongs. The raw
and linearized signals from nasal prongs were compared with
actual flow recorded by a pneumotachograph. The analysis
was focused on evaluating the relevance of linearizing nasal
prongs to detect and quantify the reduction in flow amplitude
during hypopneas and to characterize flow-limited events by
assessing the curvature of the flow–time curve.
METHODS
The study was carried out by means of an experimental setting specifically implemented to analyze the performance of nasal prongs in the
laboratory (Figure 1). A flow generator consisting of a servocontrolled pump (5) was able to reproduce any breathing flow waveform
stored in the computer. This flow generator was connected through a
pneumotachograph to a nasal analog output. The nares were mimicked by two adjacent tubes with an internal diameter of 7.5 mm. Conventional nasal prongs were placed on the artificial nares and pressure
at the nasal prongs (PNP) was measured with a pressure transducer
(176PC14HG2; Honeywell, Freeport, IL). The nasal prongs device
·
was linearized (VNP) by on-line computation of the square root of PNP
signal using a specially designed analog circuit which was previously
described (13).
Nasal prongs linearization was analyzed by using a set of nasal flow
signals recorded with a pneumotachograph in a patient study which
was previously described in detail (14). Briefly, flow and esophageal
pressure were recorded during a period of full-night, polysomnography-controlled stepwise CPAP titration in six patients with severe
SAHS. These flow signals were exactly reproduced (Figure 1) and ac·
tual flow (V), PNP, and flow estimated by the linearized nasal prongs
·
signal (VNP) were simultaneously sampled at 100 Hz and stored.
·
To compare the performance of VNP to estimate flow, we selected
a total of 21 representative inspiratory patterns from the six patients
and CPAP values. These flow patterns were hypopneas with different
degree of flow limitation in the case of suboptimal CPAP and normal
inspirations when optimal CPAP was applied to the patient. The selection of the respiratory events was made from the flow and esophageal pressure curves exclusively. The selection was blind to the sig·
nals obtained from the nasal prongs (PNP and VNP). An event was
Farré, Rigau, Montserrat, et al.: Optimized Flow Assessment in Sleep Apnea
Figure 1. Diagram of the experimental set-up to evaluate the relevance of linearizing the nasal prongs signal. PUMP ⫽ servocontrolled
flow generator; PNT ⫽ pneumotachograph; NP ⫽ nasal prongs; PT ⫽
pressure transducer; SQRT ⫽ circuit to linearize a signal by computing
its square root, PC ⫽ personal computer.
classified as flow-limited when increased inspiratory effort was not associated with an increase in flow (3, 4). The degree of flow decrease
during an event was quantified by computing an index accounting
for the hypopnea flow amplitude (HFA). HFA was defined as the
mean inspiratory flow during the event normalized by the mean inspiratory flow in the same patient when subjected to optimal CPAP;
i.e., HFA ⫽ 0.30 indicated that the flow amplitude during the event
was 30% of the value observed with optimal CPAP. For the hypopneas with a HFA ⬎ 0.30, we computed the flow limitation index
(FLI) previously described by Teschler and coworkers (15). This index quantifies the curvature of the inspiratory flow signal. Specifically, FLI measures to what extent the central part of inspiratory flow
exhibits a sinusoidal-like round contour (normal inspiration: FLI ⵑ 0.3),
a square-like contour (moderately flow limited: FLI ⵑ 0), or a central
part of inspiration with a magnitude below mean inspiratory flow (extremely flow limited: FLI ⬍ 0). For each event, HFA and FLI were
·
·
computed from V, PNP, and VNP. The values obtained from PNP and
·
·
VNP were compared with the ones obtained from V as proposed by
Bland and Altman (16).
RESULTS
·
Figure 2 shows an example
of V measured by the pneumo·
tachograph, PNP, and VNP corresponding to the signals originally recorded in a patient with SAHS. In the figure, two first
normal cycles are followed by flow limited cycles ending with
495
a cycle of increased amplitude due to arousal. For the· sake of
comparison, in this figure the scales of both PNP and VNP were
modified in such a way that the amplitude of the first inspiratory cycle was the same for the three signals. In agreement
with its nonlinear behavior, nasal prongs underestimated low
flow values and overestimated high flow values. By contrast,
linearizing the nasal prongs signal by computing its square
root allowed us to accurately reproduce the actual pattern of
flow measured by the pneumotachograph.
More detailed examples of the typical patterns of inspiratory flows found in patients with SAHS are shown in Figure 3.
The left panels correspond to a markedly flow-limited inspiration recorded when applying suboptimal CPAP. As described
by Teschler and coworkers (15), in this figure the time scale
was shown as a fraction of inspiratory time and the flow scale
was normalized in such a way that the unit value corresponded
to the mean of the inspiratory flow. The figure clearly shows
that, compared with the pneumotachograph, PNP over/under
estimated the high/low flows. In addition to underestimating
HFA (HFA ⫽ 0.26 instead of the actual HFA ⫽ 0.49), PNP resulted in an underestimation of the FLI from the actual FLI ⫽
⫺0.30 to FLI ⫽ ⫺0.61. By contrast, the linearized nasal prongs
signal accurately reproduced the actual flow pattern, resulting
in a correct estimation of the degree of hypopnea (HFA ⫽
0.48) and flow limitation (FLI ⫽ ⫺0.32). Similarly, the right
panels in Figure 3 show an inspiratory flow recorded when optimal CPAP was applied. Also in this case, high flow values
were overestimated by nasal prongs with the result that the
FLI was biased (FLI ⫽ 0.52 versus actual FLI ⫽ 0.34). The linearized signal accurately estimated the flow contour and the
FLI (FLI ⫽ 0.36).
The· relationship between HFA computed from the actual
flow (V) and· the ones obtained from PNP and from the linearized signal VNP in all the events corresponding to suboptimal
CPAP is shown in Figure 4A. This plot clearly reflects that
PNP is a signal that is nonlinearly related to flow. Owing to the
parabolic relationship of the curve corresponding to PNP, PNP
resulted in an underestimation
of HFA. By contrast, this fig·
of
ure also shows that VNP provided a suitable estimation
·
HFA.
Linear regression of HFA values obtained from V and
·
VNP was close to the identity line: HFA(estimated) ⫽ 1.098
⭈
·
HFA(actual) ⫺ 0.063 (r2 ⫽ 0.99). The good accuracy of VNP
for assessing the degree of hypopnea is also demonstrated in
Figure 4B. The difference between HFA values showed a
Figure 2. Example of the
actual flow simultaneously
recorded with a pneumotachograph (top), flow estimated with nasal prongs
(PNP), and linearized
nasal
·
prongs signal (V NP). The
flow signal includes normal and flow-limited breathing cycles in a patient with
SAHS. Positive flow corresponds to inspiration.
Figure 3. Detailed example of inspiratory flows recorded with a reference pneumotachograph (PNT), with nasal prongs (NP) and linearized
nasal prongs signal (Linearized NP) in the case of an extremely flowlimited inspiration (left) and of a normalized inspiration (right).
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AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
Figure 4. (A) Relationship between the actual values of HFA computed
from the pneumotachograph and the values of HFA estimated from
nasal prongs pressure (open circles) and from the linearized nasal
prongs signal (closed circles). Dashed lines correspond to the linear and
quadratic regressions of closed circles and open circles, respectively. Additional vertical (HFA ⫽ 0.50) and horizontal (HFA ⫽ 0.25 and 0.50)
axes are shown in dotted lines. (B) Difference between HFA estimated
from the linearized nasal prongs signal and actual value as a function
of actual value. Dashed line is the mean of the differences, dotted lines
are mean ⫾ 2 SD of the differences, and solid line is the linear regression line.
slight positive dependence on the actual value. However, the
magnitude of this difference could be neglected for clinical applications: the mean difference in HFA values was ⫺0.014 and
the limits of agreement, defined as mean ⫾ 2 SD of differences (16), were 0.064 and ⫺0.092.
Figure
5A compares the values of FLI estimated
from PNP
·
·
and VNP with the actual ones obtained from V. Estimation of
FLI from PNP was biased: this signal resulted in an overestimation for normalized inspirations (FLI ⬎ 0) and in an underesti·
mation for flow-limited events (FLI ⫽ 0). By contrast, VNP provided virtually the same values of FLI as nasal flow measured
with the pneumotachograph: FLI(estimated) ⫽ 1.044 ⭈ FLI(actual) ⫹ 0.007 (r2 ⫽ 0.99). As shown in Figure 5B, the mean difference in FLI was 0.007 and the limits of agreement were 0.044
and ⫺0.031. The dependence of this difference on the actual index value could be neglected for clinical applications.
DISCUSSION
In this work we evaluated the relevance of linearizing the pressure measured with nasal prongs to quantify the hypopneas
and flow limitation events typically found in sleep studies in
patients with SAHS. We found that assessing flow by means of
PNP resulted in a misestimation of the magnitude of respiratory
events. We also found that computing the square root of nasal
pressure linearizes the device and allows accurate quantification of both the magnitude of hypopneas and the degree of inspiratory flow limitation. This linearization was readily carried
out by means of the analog circuit we developed.
The experimental design employed to study the improvement achieved in flow estimation by linearizing PNP consisted
of two steps. First, in a patient study we collected a series of
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2001
Figure 5. (A) Relationship between the actual values of FLI computed
from the pneumotachograph
and the values of FLI estimated from PNP
·
(open circles) and from V NP (closed circles). Solid lines correspond to
the linear and quadratic regressions of closed circles and open circles, respectively. (B) Difference between FLI estimated from the linearized
nasal prongs signal and actual value as a function of actual value.
Dashed line is the mean of the differences, dotted lines are mean ⫾ 2 ⭈
SD of the differences, and solid line is the linear regression line.
flow patterns that was representative of the ones found in
SAHS: hypopneas of different magnitude and flow limitation
events with a broad spectrum of curvature in the flow–time
curve. In this patient study flow was recorded by means of a
pneumotachograph, which is the reference device for flow
measurements (1), and inspiratory effort was assessed by means
of an esophageal balloon. Second, these representative flow
signals were studied in an experimental setting aimed at exclusively analyzing both the impact of nasal prongs nonlinearity
and the effects of its linearization on the quantitative assessment of hypopneas and flow limitation. Indeed, reproducing
the breathing flow patterns recorded in patients through an
analog of the nares and recording them by nasal prongs constituted a controlled procedure which avoided the potential practical problems encountered when using nasal prongs in patients
(e.g., movement of the nasal prongs and mouth breathing) (12).
A number of recent studies have pointed out that the use of
nasal prongs is a simple and minimally disturbing method to assess flow during sleep (8, 9). When compared with the most
widely used device, the thermistor/thermocouple, nasal prongs
have the advantage of a fast response to track the time course of
flow along the breathing cycle (12). Nevertheless, as flow detection is based on the pressure drop caused by turbulence in the
nares, this device is essentially
nonlinear, with a quadratic rela·
tionship between PNP and V (12). Consequently, such a nonlinearity could result in a bias when detecting hypopneas. The results of the present study provide evidence that when applied to
the flow–time patterns typical of SAHS, PNP actually overestimates the reduction in flow amplitude during hypopneas and
could, therefore, result in overdetection of these events (Figure
4A). For instance, according to the criterion for defining hypopnea as a 50% reduction from normal breathing (1), 13 of the 21
events in Figure 4A would be classified as hypopnea using the
actual flow measured by the pneumotachograph (HFA ⬍ 0.50).
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Farré, Rigau, Montserrat, et al.: Optimized Flow Assessment in Sleep Apnea
Nevertheless, if PNP was used to estimate flow events, four nonhypopneic events would be erroneously classified as hypopneas
(approximately 30% overdetection). This bias is contrary to the
one exhibited by thermistors/thermocouples (5). In addition to
misclassification of hypopneas, the use of PNP would also affect
the computation of FLI. However, this could not constitute a
special problem because the index values we computed from
the PNP signal were still linear with the reference values (Figure
5A). Consequently, any criterion for classifying flow limitation
could be maintained by simply adapting the threshold index
values. The main result of our work was that linearizing PNP
provided us with a signal which almost perfectly reproduced actual nasal flow (Figure 2). Therefore, computation of the square
root of PNP enabled us to accurately quantify the magnitude of
flow reduction and, therefore, to improve the classification of
hypopneas (Figure 4B).
The linearized nasal prongs signal provided an excellent reproduction of the time course of flow. Nevertheless, it should be
emphasized that nasal prongs are not suitable
for assessing pa·
tient ventilation. Indeed, similarly to PNP, V· NP is a signal with an
uncalibrated
gain because the value of VNP depends on the
·
PNP–V relationship. This relationship is determined by the geometry of the nares and by the placement
of the nasal prongs.
·
The coefficients relating PNP and V, which may vary after uncontrolled nasal prongs movement (12), are unknown in a given
patient setting. However,
the quadratic nature of the relation·
ship between PNP and V is not modified by changes in the nasal
prongs position (12). Consequently, any change owing to nasal
prongs ·movement would simply result in a variation of the gain
in the VNP. This would not be a practical problem given that hypopneas and flow limitation events are detected by relative
comparison with the immediate normal breathing cycles and
that possible changes resulting from nasal prongs movement
are not reasonably expected within such a short time period.
Practical linearization of PNP could be carried out by a simple modification of the software when using a microprocessorbased polysomnographic recorder. Nevertheless, in routine
practice such a procedure is not easily applicable because
most of the available conventional polysomnographic systems
do not allow arithmetic computations. Consequently, the use
of an analog circuit similar to the one we developed would be
most convenient because it provides a square root on-line
voltage which can be directly fed to any conventional polysomnograph. As the analog circuit described in this work is
small, simple, and inexpensive and does not require periodic
calibration, it can be incorporated as an optional final stage
into any pressure transducer system. In regard to the practical
application of the linearization procedure proposed, it should
be pointed out that computation of the square root of a signal
is affected by any unbalanced offset in the nasal pressure signal. To avoid artifacts, the zero level of the pressure transducer connected to the nasal prongs should be verified during
the routine polysomnographic calibration procedure. Given
the excellent offset stability in conventional pressure transducers (17), the potential errors induced when computing the
square root are expected to be negligible. Nevertheless, special attention should be paid in case of recording PNP by means
of pressure transducers with internal AC compensation of the
baseline because in this case computation of the square root of
PNP may result in erroneous flow assessment. In this situation,
it could be possible to indirectly linearize nasal prongs by
modifying the threshold of PNP amplitude reduction in the hypopnea definition. Indeed, as shown in Figure 4, it would be
equivalent to take a HFA threshold equal to 0.25 [i.e., (0.50)1/2]
for the raw nasal· pressure signal ·or to take the conventional
0.50 for actual (V) or estimated (VNP) flow.
·
In conclusion, comparison of PNP with V measured with a
pneumotachograph demonstrates that nasal prongs is a nonlinear fast-response device useful in assessing breathing flow
(6–10, 12). We found that the nonlinearity of this device results in misestimation of the magnitude of hypopneas and flow
limitation. Accordingly, using the criterion of 50% reduction
in the raw PNP pressure does not coincide with the recommended definition of hypopnea as 50% reduction in actual
flow (1). We also showed that the nonlinearity of nasal prongs
is easily and effectively corrected by simply computing the
square root of PNP. This procedure could be readily implemented in routine practice. Although further clinical studies
should be focused on better evaluating the practical performance of nasal prongs within the framework of polysomnography, our findings suggest that nasal prongs linearization
could improve the quantification of the magnitude of respiratory sleep disturbances in SAHS.
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