Figure 9 illustrates the break-down of the data processing
flow. With normalization, the Bragg wavelengths are
removed from the acquired raw wavelengths. In doing so,
it returns the relative wavelength differences, instead of the
absolute values. In the following, the normalized signal
will undergo some filtering. The filtered values will be
used to construct the respiratory signal.
Next, wavelet decomposition is used to break down the
signal into several levels. The purpose of the decomposition
is to remove unwanted frequencies from the signal
(arising from other movements in the bed). Subsequently,
auto-correlation is applied to observe how the signal
changes over time, which amplifies the periodic components, if any. Subsequently, the respiratory frequency
will be determined from the pool of periodic
components. Lastly, the respiration rate will be tracked so
as to observe the change over longer time durations. Some
of the screenshots from different stages of data processing
flow are depicted in Fig. 10.
Figure 9 illustrates the break-down of the data processingflow. With normalization, the Bragg wavelengths areremoved from the acquired raw wavelengths. In doing so,it returns the relative wavelength differences, instead of theabsolute values. In the following, the normalized signalwill undergo some filtering. The filtered values will beused to construct the respiratory signal.Next, wavelet decomposition is used to break down thesignal into several levels. The purpose of the decompositionis to remove unwanted frequencies from the signal(arising from other movements in the bed). Subsequently,auto-correlation is applied to observe how the signalchanges over time, which amplifies the periodic components, if any. Subsequently, the respiratory frequencywill be determined from the pool of periodiccomponents. Lastly, the respiration rate will be tracked soas to observe the change over longer time durations. Someof the screenshots from different stages of data processingflow are depicted in Fig. 10.
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