Effect of confounding factors on blood pressure estimation using pulse arrival time使用脈衝到達時間估計血壓對於混雜因素效果

This article has been downloaded from IOPscience. Please scroll down to see the full text article.2008 Physiol. Meas. 29 615 Jung Soo Kim1, Ko Keun Kim1, Hyun Jae Baek2 and Kwang Suk Park3 Received 7 January 2008, accepted for publication 4 April 2008Published 7 May 2008Online at stacks.iop.org/PM/29/615

Adviser: Huang Ji-JerPresenter:Syu Hao-Yi

Date:2013/3/6

Review1• Introduction• Methods• Results• Discussion and Conclusions

Review2• Introduction• Multiscale Mathematical Morphology Theory• Proposed Implementation Scheme• Discussions on Structure Elements• Experimental Results• Conclusion

References

Outline

• With the increasing need for non-intrusive measurement of blood pressure (BP), blood pressure estimation with pulse arrival time (PAT) was recently developed, replacing conventional constrained measurement by auscultatory and oscillometric methods using a mechanical cuff

Introduction

• The method needs to be calibrated for each individual using a regression process. This was presented as inter- and intra-subject analyses in our previous study .PAT was obtained from ECG and photoplethysmogram (PPG) measured non-intrusively

Introduction

• The purpose of this study is to evaluate the effect of heart rate (HR) and arterial stiffness in BP estimation with PAT

Introduction

1) Confounding factor—HR– Blood pressure is related to heart rate as well as to

PAT in the cardiovascular system

Methods

1) Confounding factor—HR2) Confounding factor—arterial stiffness3) Experiments

Methods

Correlation coefficients of SBP and DBP with the HR or RR interval. HR shows a slightly higher correlation with both SBP and DBP than with the RR interval.

2) Confounding factor—arterial stiffness– Arterial stiffness is known to be related to BP– Pulse wave velocity (PWV)、 Augmentation

index (AI) (Using a catheter or a tonometer)

– another robust and noninvasive method for assessing arterial stiffness is needed

Methods

• Amplitude parameters• Time parameters• Slope parameters

Methods

Comparable parameters of arterial stiffness in PPG.

Methods• shows the results of correlation analysis between these 16 parameters and BP for five individual subjects

3) Experiments– Experiments for parameter selection and

evaluation of the results were performed using ten male subjects with an average age of 28 years (25–32 years)

Methods

Results

1) Correlation of blood pressure with confounding factors

2) Single and multiple regression analysis3) Reproducibility

1) Correlation of blood pressure with confounding factors

Results

Correlation between BP and BP estimating parameters for patient A

2) Single and multiple regression analysis3) (BP = a + b PAT + c HR + d TDB)∗ ∗ ∗

Results

(BP = a + b PAT + c HR + d TDB)∗ ∗ ∗

Results

(BP = a + b PAT + c HR + d TDB)∗ ∗ ∗

3) Reproducibility

Results

Reproducibility of multiple regression analysis for BP estimation. The test was conducted for a week. The estimated BP from the regression equation of the training set was compared with the measured BP. The correlation coefficients decreased a little with 0.7714 and 0.8432 for SBP and DBP. However, such a level of correlation should still be enough for the estimation of BP

1) Correlation with blood pressure2) Waveform analysis of PPG3) Limitation of the study4) Application to home health care

Discussion and Conclusion

Review2

QRS Detection Based on Multiscale Mathematical Morphology for Wearable

ECG Devices in Body Area NetworksThis paper appears in: Biomedical Circults and System,IEEE Transactions onDate of Publication: Aug. 2009Author(s): Fei Zhang Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore  Yong Lian Volume: 3  , Issue: 4 Page(s): 220 - 228 Product Type: Journals & Magazines

• Introducing the multiscale mathematical morphology(3M) filtering concept into QRS detection

Introduction

Multiscale Mathematical Morphology Theory

Proposed Implementation Scheme

1) Multiscale Mathematical Morphology Filtering

2) Differential Operation3) Enhancing ECG by Modulus and

Combination4) Threshold and Decision

• The structure element plays an important role in the 3M filter. Its shape, amplitude, and length affect the output of the morphology filter

Proposed Implementation Scheme

1) Multiscale Mathematical Morphology Filtering

-The top-hat operator produces an output consisting of the signal peaks -the bottom-hat operator extracts the valleys

(negative peaks)

Proposed Implementation Scheme

JJj

B

j

T

j KKK 1)21(

1) Multiscale Mathematical Morphology Filtering – J is the largest filtering scale – The multiscale opening and closing filtering– Thethe weighted sum of the top-hat and bottom-

hat transformations at the scale from 1 to J

Proposed Implementation Scheme

Implementation scheme of the proposed 3M filter for J=3

1) Multiscale Mathematical Morphology Filtering

Proposed Implementation Scheme

Power consumption is an important consideration in the design of wearable devices. The ideal QRS detection solution should avoid the use of multiplier(s) in order to reduce the power

2) Differential Operation -After 3M filtering, the output ECG sequence

is differentiated in order to remove motion artifacts and baseline drifts

Proposed Implementation Scheme

3) Enhancing ECG by Modulus and Combination– The absolute value of the differential output is

combined by multiple-frame accumulation

Proposed Implementation Scheme

The value of q should correspond to the possible maximumduration of the normal QRS complex

4) Threshold and Decision– The detection of a QRS complex is accomplished

by comparing the feature against a threshold

Proposed Implementation Scheme

• The MIT/BIH Arrhythmia Database is used to evaluate our algorithm

Experimental Results

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2

2

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Experimental Results

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2

2

)()(qnqn

ivns

• False Negative(FN)、 False Positive (FP)、 Sensitivity (Se)、 Positive Prediction(+P)、 Detection error (DER) 、 True positive (TP)

Experimental Results

• We have presented a computationally efficient QRS detection algorithm for the resting and exercise ECG

• Using Differential modulus accumulation to reduce the noise in the ECG signal

• The algorithm is evaluated against the MIT/BIH database and achieves a detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%

Conclusion

• Effect of confounding factors on blood pressure estimation using pulse arrival time Jung Soo Kim1, Ko Keun Kim, Hyun Jae Baek and Kwang Suk Park

• QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices inBody Area Networks Fei Zhang and Yong Lian, Fellow, IEEE

References

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