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Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform.

Lim PK, Ng SC, Jassim WA, Redmond SJ, Zilany M, Avolio A, Lim E, Tan MP, Lovell NH - Sensors (Basel) (2015)

Bottom Line: Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal.Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg).Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. veronicalimpooikhoon@gmail.com.

ABSTRACT
We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

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Related in: MedlinePlus

Description of features extracted from the OWE.
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sensors-15-14142-f005: Description of features extracted from the OWE.

Mentions: In the present study, a total of 10 features were extracted from the OWE, as illustrated in Figure 5 and defined in Table 1, in which six have been used in a previous study [15], whereas the remaining features were newly proposed in this study. These features can be classified into five different classes: (I) Amplitude; (II) Duration; (III) Area; (IV) Ratio; and (V) MAP estimated using the MAA approach.


Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform.

Lim PK, Ng SC, Jassim WA, Redmond SJ, Zilany M, Avolio A, Lim E, Tan MP, Lovell NH - Sensors (Basel) (2015)

Description of features extracted from the OWE.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507654&req=5

sensors-15-14142-f005: Description of features extracted from the OWE.
Mentions: In the present study, a total of 10 features were extracted from the OWE, as illustrated in Figure 5 and defined in Table 1, in which six have been used in a previous study [15], whereas the remaining features were newly proposed in this study. These features can be classified into five different classes: (I) Amplitude; (II) Duration; (III) Area; (IV) Ratio; and (V) MAP estimated using the MAA approach.

Bottom Line: Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal.Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg).Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. veronicalimpooikhoon@gmail.com.

ABSTRACT
We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = -0.3 ± 5.8 mmHg; SVR and -0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = -1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity.

Show MeSH
Related in: MedlinePlus