Results
The database search yielded 1414 articles, of which 1058 were left after
removal of duplicates. For an overview of the selection process, see the
PRISMA figure in Figure 1 . After assessment of relevance and
eligibility, 14 studies were included. After checking the references and
citations, three additional studies were included (E-text 3).
The methods researched in the included studies were bed-based methods,
ultra-wideband (UWB) radar, Doppler radar, video, infrared (IR) cameras,
garment-embedded with sensors, and sound analysis. Some studies used a
combination of sensors. The main study characteristics are summarized in
Table 1 . The accuracies of the researched techniques are
summarized in Table 2 and the advantages and limitations of the
techniques are summarized in Table3 .
Bed-based methods
The techniques that monitor breathing using sensors embedded in a bed
were based on pressure sensors, vibration sensors, load cells,
microphones, electromagnetic sensors, piezoelectric sensors, and
electromechanical film. Arimoto et al.13, embedded
these sensors in a sheet-like device, the SD-101(Kenzmedico co. Ltd.,
Saitama, Japan). Norman et
al.14,15 and
Collaro et al.16 evaluated the Sonomat (Sonomat,
Balmain, Australia), a thin mattress overlay with embedded sensors and
microphones. and So et al.17 used the TaidoSensor
(Sumitomo Riko Company Limited, Nagoya, Japan), a rubber sheet made of
piezoelectric material. Lee et al.18 embedded load
cells in a bed frame and used their sensors to obtain a
ballistocardiogram (BCG), from which the cardiac and respiration cycle,
and thereby the RR, could be obtained. So et al.17Norman et
al.14,15 and
Collaro et al.16 aimed to detect apneas/hypopneas, and
So et al.17 aimed to continuously measure RR and were
also able to detect characteristic breathing patterns such as deep
breathing and apneas. Lee et al.18 and Arimoto et
al.13 used analytical software for automatic
respiration monitoring, although the latter performed a manual
correction since software was not yet available for measurements in
children. Norman et al.14, 15researched the validity of the Sonomat (Sonomat) in children, and
Collaro et al.16 aimed to do so for children with Down
Syndrome. The Sonomat (Sonomat) contains four vibration sensors and two
room sound microphones. Both Norman et al.14 and
Collaro et al.16 used the combination of signals to
differentiate between obstructive, central, and mixed apneas and
compared these results with PSG.
UWB Radar
Kim et al.19, de Goederen et al.5,
Huang et al.20, and Ziganshin et
al.21 researched ultra-wideband (UWB) radar for
respiration monitoring. Huang et al.20 and Ziganshin
et al.21 aimed to detect apneas, while de Goederen et
al.5 and Kim et al.19 used their
system to measure RR. De Goederen et al.5 used the RR
for sleep stage classification.
Doppler radar
Fox et al.22 used Doppler radar with analytical
software for actimetry and compared their method with an actimetry
watch.
Video
Al-Naji and Chahl23 used a video camera to monitor RR
and used video magnification technique to do so.
IR camera
Both Al-Naji et al.24 and Bani Amer et
al.25 used an IR camera in their research. Al-Naji et
al.24 used the Kinect v2 (Microsoft, Redmond, WA)
sensor, which has three optical sensors: an RGB camera, IR sensor, and
IR projector, that provide an RGB image, IR image and depth image. These
signals were used to measure RR. Simulated apneas were detected as well.
Bani Amer et al.25 aimed to detect central,
obstructive and mixed apneas. Both authors used software for signal
analysis.
Garments
Two studies embedded sensors inside a garment; Gramse et
al.26 in the MamaGoose (MMG) pajamas (Verhaert Design
and Development, Kruibeke, Belgium), Ranta et al.27 in
the NAPping PAnts (NAPPA) diaper cover (BABA Center, Helsinki, Finland).
Both used an algorithm for RR monitoring, and Gramse et
al.26 detected apneas based on visual examination of
the abdominal and thoracic respiration signals. No reference standard
was used for respiration signals.
Sound
analysis
Emoto et al.28 used an artificial neural network to
determine snoring/breathing episodes (SBEs) based on microphone
recordings. Norman et al.15 compared the signals from
the Sonomat (Sonomat) microphones with the mat as a whole. Breathing
sounds and pattern prior to body movement were used to differentiate
between spontaneous and respiration-induced body movement.
Quality assessment
Figure 2 shows the risk of bias and applicability concerns about
each domain for the individual studies. In the figure, the colors green,
yellow and red correspond to low, unclear and high risk of bias,
respectively.
Eight studies included less than ten study participants. This, and the
lack of information about the enrollment of study participants or
exclusion criteria, increased the risk of bias in the patient selection.
Only Arimoto et al.13, Norman et
al.14 and Collaro et al.16 compared
their techniques with PSG and Ranta et al.27 compared
their garment with capnography. These authors all described the process
of patient sampling and of index and reference test interpretation.
Both Fox et al.22 and So et al.17included children and adults in their study population. In both of these
studies, children <12 years old were the minority of the study
population, which raises applicability concerns. These concerns also
apply to the study by Kim et al.19 who evaluated their
UWB radar device at the NICU. Although the neonates were clinically
stable and full-term, this also raises concerns regarding applicability
to our review question.