Discussion
Safe, noncontact
techniques
The primary objective of this scoping review was to investigate the
methods that have been studied for safe and noncontact monitoring of
respiration in young children. The included studies explored various
approaches, including bed-based methods, UWB radar, Doppler radar,
video, IR cameras, garment-embedded sensors, and sound analysis. Among
the 17 studies reviewed, the majority focus on monitoring either RR or
apneas, while five studies aimed to do both. None of the studies
reported safety risks. For very high-power UWB radar systems, there
could be concerns for interference with other radiofrequency devices and
safety, but such high powers are not reached in UWB radar in the medical
field29. Liu et al.30 have outlined
that RR measurement methods can be derived from other physiological
signals, respiration movements or airflow. Notably, most of the
techniques investigated relied on software for signal analysis, offering
advantages such as faster and more objective analysis compared to manual
assessment.
In the study conducted by Lee et al.18,
ballistocardiography was employed to derive RR. Notably, this technique
offers the potential to detect other physiological parameters that
generate motion, including snoring and limb
movements31,32. This capability opens up the
possibility of assessing additional sleep characteristics such as REM
sleep, as demonstrated by Kortelainen et al.33.
However, it is important to note that research on these measurements has
predominantly concentrated on adults.
Accuracy
The second aim of this review was to describe the accuracy of the
techniques. Because the monitoring techniques would mostly function as a
screening tool for SDB, sensitivity is an important measure of accuracy.
Only Norman et al.14, Collaro et
al.16 and Bani Amer et al.25provided the sensitivity of their techniques (82%, 85% and 94%
respectively).
RR measurement accuracies found by Al-Naji and
Chahl23, Kim et al.19 and Ranta et
al.27 fall within an accuracy of ±2 breaths per
minute. These researchers used different types of methods, namely video,
UWB radar, and a garment-embedded sensor, respectively.
Not all studies explicitly described the impact on motion artifacts.
However, the authors that did, were not able to monitor respiration.
Interestingly, Bani Amer et al.25 claimed that their
measurements with an IR sensor did not have any effect of motion
artifacts. A conventional apnea monitor was used as a reference
standard, but it was not specified what type of monitor this was. It is
known that different respiration measurement techniques, such as methods
that measure chest and abdominal wall movements, are also subject to
motion artifacts.34 It would be interesting to see the
performance validated against PSG.
Most authors removed data containing motion artifacts from the signal
for apnea detection. Although this is not a problem for apnea detection
in adults, children can experience apneas during body movements that
last a long time13. Therefore, the ability of a
technique to monitor respiration during movement would enable more
accurate measurements. For Doppler radar, efforts have been made to
fight the problem of motion artifacts. Li and Lin35researched a method using two radar sensors, and Gu et
al.36 used a hybrid radar-camera system for random
body movement cancellation.
When presenting data on a new monitoring technique, it is essential to
provide comprehensive information on accuracy to establish its validity
and reliability. This includes describing the validation methods used to
assess the performance of the technique, such as comparisons with
established reference standards or expert evaluations. Geographical
validation is also crucial to ensure the generalizability of the results
across different populations or settings. Additionally, reporting
metrics such as receiver operating characteristics (ROC) curves,
sensitivity, specificity, positive predictive value, negative predictive
value, and F1 score can further demonstrate the accuracy of the
monitoring technique. These metrics allow for an in-depth analysis of
the technique’s ability to correctly identify and distinguish between
various physiological parameters or conditions. Furthermore, providing
information on any potential limitations or sources of error associated
with the monitoring technique is important for a comprehensive
evaluation. This may include factors such as signal quality, potential
interference, or specific conditions under which the technique may yield
less accurate results.
Advantages and limitations
of the techniques
Most researched methods were easy to set up, except those involving load
cells in a bed frame, which would be more challenging in a domestic
setting compared to a mattress or mattress overlay. Both bed-based
methods and UWB radar demonstrated potential for movement tracking in
addition to monitoring RR and detecting apneas. It is expected that all
techniques, except sound analysis, would enable movement tracking, which
is valuable for accurately measuring sleep duration, fragmentation, and
assessing the impact of SDB. Combining sleep-wake classification with
apnea detection allows a more precise estimation of the apnea-hypopnea
index (AHI) compared to estimating sleep time based on time spent in
bed. Arimoto et al.13, who took the time in bed as
sleeping time, found an underestimation of 10-15% of AHI compared to
PSG. Several studies have compared AHI calculations using UWB radar with
PSG in adults. Zhou et al.37 found a sensitivity and
specificity of the AHI measured with UWB radar were 0.95-1 and 0.96 –
1, respectively, depending on the AHI cut-off values. Kang et
al.38 found that using UWB radar, three types of apnea
(central, obstructive, and mixed) and hypopneas could be detected in
adults. The authors concluded that UWB radar could be used as a
screening tool for sleep apnea. If these results can be achieved in
adults, there is reason to believe this is also possible in children.
Bed-based techniques and video analysis were found to be able to measure
respiration in children in any sleeping position, allowing more accurate
real-life measurements in children. Gramse et al.26,
who integrated strain sensors in a pair of pajamas, found that more
signals were out of range in prone and side positions compared to supine
positions. Authors researching radar-based monitoring did not specify
the effect of sleeping position. In a radar-based vital sign monitoring
study by Turppa et al.39, larger errors in RR
measurements in lateral sleeping positions were found compared to prone
and supine positions. Not only the sleeping position influence the
measurements, but the location and direction in which the radar is
facing also play a significant role in measurement accuracy. Therefore,
it would be valuable to explore various experimental setups in this
context.
The added value of sound recording has been studied by Emoto et
al.28 and Norman et al.15 Apart from
using breathing sounds for the classification of obstructive, central
and mixed apneas, Norman et al.15 found that measuring
snoring and stertor allows assessment of partial upper airway
obstruction, providing more information compared to identifying discrete
obstructive apnea/hypopnea events. In addition to this, snoring and
breathing sounds have recently been researched for the estimation of
sleep-wake activity and sleep quality parameters in adults. A study by
Dafna et al.40 found they could differentiate between
sleep and wake based on breathing sounds, with a sensitivity of 92.2%
and a specificity of 56.6% compared to PSG. In another study, by Akhter
et al.41, breathing sounds were used to differentiate
between REM and non-REM sleep, with a sensitivity of 92% and a
specificity of 81% compared to PSG, indicating that these results could
also be achievable in children.
Limitations
Three out of the 17 studies included in this review were retrieved from
the reference lists of the other studies. This highlights the
heterogeneity of terminology in this field of research. Four studies
compared their method to the gold standard. Across the studies,
different measures for accuracy were used, or no accuracies were
determined. In addition to that, within groups of methods, different
types of sensors and analytical algorithms were used, complicating the
comparison between methods. The development of different sensors and
algorithms, however, is not only logical in the current state of the
art, but also essential for the field’s progress, reflecting the
diversity of use cases, promoting innovation, and allowing for tailored
solutions to meet the unique needs in various clinical scenarios.
This scoping review mainly focused on apnea detection and RR monitoring.
It is important to acknowledge that SDB encompasses a larger spectrum,
including hypoperfusion and decreasing saturations1.
In order to properly diagnose SDB, these aspects should also be taken
into account. Nonetheless, because OSA is the most common and clinically
significant type of SDB in
children42,
this review is an important step towards contactless respiration
monitoring and SDB screening in children.
Future research
To determine the most promising techniques for noncontact respiration
monitoring in children, further research is necessary to compare these
techniques against the gold standard PSG. Ideally, these studies should
be openly accessible and encompass large cohorts of children across
different age groups, encompassing both those with and without
comorbidities. This approach will allow for the identification of the
most effective techniques tailored to specific patient populations.
Additionally, because respiration monitoring techniques can be used for
various objectives, it is important to take into consideration
the types of monitoring techniques for specific use. Common objectives
include diagnosis or screening of SDB, long-term apnea detection, RR
monitoring, or impact assessment of SDB treatment. Future research
should focus on identifying both the appropriate monitoring techniques
for each application and the adequate signal processing algorithms for
those techniques. Techniques should be safe, accurate, reliable and
flexible in terms of environment. Studies presenting new monitoring
techniques should report important accuracy metrics such as ROC curves,
sensitivity, specificity, positive predictive value, negative predictive
value and, in case of machine learning algorithms, F1 score. Lastly, to
ensure generalizability of the results, it is crucial to validate the
techniques.