| Summary: | Pulse oximetry has been extensively used to estimate oxygen saturation in
blood, a vital physiological parameter commonly used when monitoring a
subject’s health status. However, accurate estimation of this parameter is
difficult to achieve when the fundamental signal from which it is derived, the
photoplethysmograph (PPG), is contaminated with noise artifact induced by
movement of the subject or the measurement apparatus. This study presents a
novel method for automatic rejection of artifact contaminated pulse oximetry
waveforms, based on waveform morphology analysis. The performance of
the proposed algorithm is compared to a manually annotated gold standard.
The creation of the gold standard involved two experts identifying sections
of the PPG signal containing good quality PPG pulses and/or noise, in 104
fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal
as a reference signal. The fingertip PPG signals were each 1 min in duration
and were acquired from 13 healthy subjects (10 males and 3 females). Each
signal contained approximately 20 s of purposely induced artifact noise from
a variety of activities involving subject movement. Some unique waveform
morphology features were extracted from the PPG signals, which were believed
to be correlated with signal quality. A simple decision-tree classifier was
employed to arrive at a classification decision, at a pulse-by-pulse resolution,
of whether a pulse was of acceptable quality for use or not. The performance
of the algorithm was assessed using Cohen’s kappa coefficient (κ), sensitivity,
specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained,
while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19%
and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from
uncontaminated sections of PPG, as identified by the algorithm, was compared
with the heart rate derived from an uncontaminated simultaneous ECG signal.
The mean error between both heart rate readings was 0.49 ± 0.66 beats perminute (BPM), in comparison to an error value observed without using the
artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that
automated identification of signal artifact in the PPG signal through waveform
morphology analysis is achievable. In addition, a clear improvement in the
accuracy of the derived heart rate is also evident when such methods are
employed.
|
|---|