Signal quality measures for pulse oximetry through waveform morphology analysis
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, th...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7988/1/J5712_6ce2a472a70b9671ed934dee12ca22ec.pdf |
| _version_ | 1796869671280967680 |
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| author | Sukor, J. Abdul Redmond, S. J. Lovell, N. H. |
| author_facet | Sukor, J. Abdul Redmond, S. J. Lovell, N. H. |
| author_sort | Sukor, J. Abdul |
| collection | UTHM |
| description | 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. |
| first_indexed | 2024-03-05T21:58:21Z |
| format | Article |
| id | uthm.eprints-7988 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:58:21Z |
| publishDate | 2011 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | uthm.eprints-79882022-11-08T01:37:31Z http://eprints.uthm.edu.my/7988/ Signal quality measures for pulse oximetry through waveform morphology analysis Sukor, J. Abdul Redmond, S. J. Lovell, N. H. T Technology (General) 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. IOP Publishing 2011 Article PeerReviewed text en http://eprints.uthm.edu.my/7988/1/J5712_6ce2a472a70b9671ed934dee12ca22ec.pdf Sukor, J. Abdul and Redmond, S. J. and Lovell, N. H. (2011) Signal quality measures for pulse oximetry through waveform morphology analysis. Physiological Measurement, 32. pp. 369-384. ISSN 0967-3334 https://doi.org/10.1088/0967-3334/32/3/008 |
| spellingShingle | T Technology (General) Sukor, J. Abdul Redmond, S. J. Lovell, N. H. Signal quality measures for pulse oximetry through waveform morphology analysis |
| title | Signal quality measures for pulse oximetry through
waveform morphology analysis |
| title_full | Signal quality measures for pulse oximetry through
waveform morphology analysis |
| title_fullStr | Signal quality measures for pulse oximetry through
waveform morphology analysis |
| title_full_unstemmed | Signal quality measures for pulse oximetry through
waveform morphology analysis |
| title_short | Signal quality measures for pulse oximetry through
waveform morphology analysis |
| title_sort | signal quality measures for pulse oximetry through waveform morphology analysis |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/7988/1/J5712_6ce2a472a70b9671ed934dee12ca22ec.pdf |
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