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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

Bland–Altman plot of possible SBP between RS and conventional MAA algorithm (a) before and (b) after outlier removal.
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sensors-15-14142-f006: Bland–Altman plot of possible SBP between RS and conventional MAA algorithm (a) before and (b) after outlier removal.

Mentions: Figure 6 and Figure 7 are the Bland-Altman plots demonstrating the performance of estimated SBP and DBP using the conventional MAA algorithm, with and without using the outlier removal algorithm before the OWE curve fitting process. On the other hand, cumulative percentage of blood pressure readings which fall within absolute differences of 5, 10 and 15 mmHg from RS (required for evaluation using the BHS standard) as well as mean ± SD difference between RS and conventional MAA algorithm (required for evaluation using the AAMI standard) were presented in Table 4. Based on the Bland–Altman plots for SBP (illustrated in Figure 6), the errors between the estimated pressure and the RS were large without outlier removal (up to 125 mmHg at low SBP), and substantially reduced upon elimination of the outlier points, with most data points lying within ±20 mmHg errors from the RS. Similar observations were found for the DBP (Figure 7). As shown in Table 4, the outlier removal method proposed in this study significantly improved the accuracy of the estimated pressures, with an improvement in BHS grades from D to B and A for SBP and DBP respectively. With regards to the AAMI standard, although a significant improvement was found in both mean and SD difference for SBP after outlier removal, the conventional MAA method failed to satisfy the AAMI standard (with a mean ± SD of −1.6 ± 8.6 mmHg, refer to Table 4).


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)

Bland–Altman plot of possible SBP between RS and conventional MAA algorithm (a) before and (b) after outlier removal.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-14142-f006: Bland–Altman plot of possible SBP between RS and conventional MAA algorithm (a) before and (b) after outlier removal.
Mentions: Figure 6 and Figure 7 are the Bland-Altman plots demonstrating the performance of estimated SBP and DBP using the conventional MAA algorithm, with and without using the outlier removal algorithm before the OWE curve fitting process. On the other hand, cumulative percentage of blood pressure readings which fall within absolute differences of 5, 10 and 15 mmHg from RS (required for evaluation using the BHS standard) as well as mean ± SD difference between RS and conventional MAA algorithm (required for evaluation using the AAMI standard) were presented in Table 4. Based on the Bland–Altman plots for SBP (illustrated in Figure 6), the errors between the estimated pressure and the RS were large without outlier removal (up to 125 mmHg at low SBP), and substantially reduced upon elimination of the outlier points, with most data points lying within ±20 mmHg errors from the RS. Similar observations were found for the DBP (Figure 7). As shown in Table 4, the outlier removal method proposed in this study significantly improved the accuracy of the estimated pressures, with an improvement in BHS grades from D to B and A for SBP and DBP respectively. With regards to the AAMI standard, although a significant improvement was found in both mean and SD difference for SBP after outlier removal, the conventional MAA method failed to satisfy the AAMI standard (with a mean ± SD of −1.6 ± 8.6 mmHg, refer to Table 4).

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