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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

Distribution of (a) Systolic blood pressure (SBP); (b) Diastolic blood pressure (DBP); (c) Pulse pressure (PP).
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sensors-15-14142-f002: Distribution of (a) Systolic blood pressure (SBP); (b) Diastolic blood pressure (DBP); (c) Pulse pressure (PP).

Mentions: The experimental data were obtained from 25 healthy subjects aged 28 ± 5 years (16 females). Four sets of measurements (two from each arm), which contain simultaneous ECG, cuff pressure and Korotkoff sound were acquired from each volunteer, resulting in a total of 100 measurements. Our data were acquired using an automated blood pressure measurement system with a cuff pressure recorder, a stethoscope with a built-in microphone to capture the auscultatory waveform, together with an ECG recorder. All the signals were acquired simultaneously using a data acquisition system with a sampling rate of 1 kHz. To acquire the oscillometric pulse, the cuff pressure was first increased to approximately 180 mmHg, followed by deflation of the cuff pressure using a release valve, which reduced the pressure to approximately 40 mmHg in a linear fashion and with a rate of 2–3 mmHg/s. To investigate the robustness of the BP estimation algorithm, one of the two measurements on each arm was intentionally contaminated with movement artifact during cuff deflation. The movements were selected from the following options: (1) gently lift the ipsilateral arm, then return to a resting position; (2) spontaneously move the ipsilateral arm right and left; (3) bend the ipsilateral arm and then return to a resting position; (4) tap the stethoscope bell three times with the contralateral hand; (5) squeeze and release the ipsilateral fingers; (6) lift and replace a book with the ipsilateral hand; (7) spontaneously shake the ipsilateral arm for a few seconds; and (8) suddenly remove the cuff. The recorded Korotkoff sound was used by two clinical experts as the basis for estimating the reference SBP and DBP as a reference system (RS). Out of the 100 signals, only 81 SBP and 84 DBP were available for this study due to a lack of reference reading in the remaining samples, in which the experts were unable to identify the SBP and DBP accurately due to the presence of a large amount of noise in the Korotkoff sound. Figure 2 shows the distribution of SBP, DBP and pulse pressure (PP) in the collected data. A more detailed description of the experimental protocol as well as equipment configuration are provided in [18].


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)

Distribution of (a) Systolic blood pressure (SBP); (b) Diastolic blood pressure (DBP); (c) Pulse pressure (PP).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-14142-f002: Distribution of (a) Systolic blood pressure (SBP); (b) Diastolic blood pressure (DBP); (c) Pulse pressure (PP).
Mentions: The experimental data were obtained from 25 healthy subjects aged 28 ± 5 years (16 females). Four sets of measurements (two from each arm), which contain simultaneous ECG, cuff pressure and Korotkoff sound were acquired from each volunteer, resulting in a total of 100 measurements. Our data were acquired using an automated blood pressure measurement system with a cuff pressure recorder, a stethoscope with a built-in microphone to capture the auscultatory waveform, together with an ECG recorder. All the signals were acquired simultaneously using a data acquisition system with a sampling rate of 1 kHz. To acquire the oscillometric pulse, the cuff pressure was first increased to approximately 180 mmHg, followed by deflation of the cuff pressure using a release valve, which reduced the pressure to approximately 40 mmHg in a linear fashion and with a rate of 2–3 mmHg/s. To investigate the robustness of the BP estimation algorithm, one of the two measurements on each arm was intentionally contaminated with movement artifact during cuff deflation. The movements were selected from the following options: (1) gently lift the ipsilateral arm, then return to a resting position; (2) spontaneously move the ipsilateral arm right and left; (3) bend the ipsilateral arm and then return to a resting position; (4) tap the stethoscope bell three times with the contralateral hand; (5) squeeze and release the ipsilateral fingers; (6) lift and replace a book with the ipsilateral hand; (7) spontaneously shake the ipsilateral arm for a few seconds; and (8) suddenly remove the cuff. The recorded Korotkoff sound was used by two clinical experts as the basis for estimating the reference SBP and DBP as a reference system (RS). Out of the 100 signals, only 81 SBP and 84 DBP were available for this study due to a lack of reference reading in the remaining samples, in which the experts were unable to identify the SBP and DBP accurately due to the presence of a large amount of noise in the Korotkoff sound. Figure 2 shows the distribution of SBP, DBP and pulse pressure (PP) in the collected data. A more detailed description of the experimental protocol as well as equipment configuration are provided in [18].

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