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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
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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 2008
Published 7 May 2008
Online at stacks.iop.org/PM/29/615
Adviser: Huang Ji-Jer
Presenter:Syu Hao-Yi
Date:2013/3/6
Outline
 Review1
•
•
•
•
Introduction
Methods
Results
Discussion and Conclusions
 Review2
•
•
•
•
•
•
Introduction
Multiscale Mathematical Morphology Theory
Proposed Implementation Scheme
Discussions on Structure Elements
Experimental Results
Conclusion
 References
Introduction
• 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
Methods
1) Confounding factor—HR
2) Confounding factor—arterial stiffness
3) Experiments
1) Confounding factor—HR
– Blood pressure is related to heart rate as well as to
PAT in the cardiovascular system
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.
Methods
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
Comparable parameters of arterial stiffness in PPG.
Methods
• shows the results of correlation analysis between these 16 parameters
and BP for five individual subjects
Methods
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)
Results
1) Correlation of blood pressure with
confounding factors
2) Single and multiple regression analysis
3) Reproducibility
Results
1) Correlation of blood pressure with
confounding factors
Correlation between BP and BP estimating parameters for patient A
Results
2) Single and multiple regression analysis
3) (BP = a + b∗PAT + c∗HR + d∗TDB)
(BP = a + b∗PAT + c∗HR + d∗TDB)
Results
(BP = a + b∗PAT + c∗HR + d∗TDB)
Results
3) Reproducibility
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
Discussion and Conclusion
1)
2)
3)
4)
Correlation with blood pressure
Waveform analysis of PPG
Limitation of the study
Application to home health care
Review2
QRS Detection Based on Multiscale
Mathematical Morphology for Wearable
ECG Devices in Body Area Networks
This paper appears in: Biomedical Circults and System,IEEE
Transactions on
Date of Publication: Aug. 2009
Author(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
Introduction
• Introducing the multiscale mathematical
morphology(3M) filtering concept into QRS
detection
Multiscale Mathematical
Morphology Theory
Proposed Implementation Scheme
1) Multiscale Mathematical Morphology
Filtering
2) Differential Operation
3) Enhancing ECG by Modulus and
Combination
4) Threshold and Decision
Proposed Implementation Scheme
• 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)
K
T
j
1
B
 K j  K j  ( ) J 1 J
2
Proposed Implementation Scheme
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 bottomhat transformations at the scale from 1 to J
Proposed Implementation Scheme
1) Multiscale Mathematical Morphology
Filtering
Implementation scheme of the proposed 3M filter for J=3
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
Proposed Implementation Scheme
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
The value of q should correspond to the possible maximum
duration of the normal QRS complex
Proposed Implementation Scheme
4) Threshold and Decision
– The detection of a QRS complex is accomplished
by comparing the feature against a threshold
Experimental Results
• The MIT/BIH Arrhythmia Database is used to
evaluate our algorithm
y (n)  F ( f (n))  Hat ( f (n))
s ( n)  
q
2
q
n
2
n
v(i )
Experimental Results
y (n)  F ( f (n))  Hat ( f (n))
s ( n)  
q
2
q
n
2
n
v(i )
Experimental Results
• False Negative(FN)、False Positive (FP)、
Sensitivity (Se)、Positive Prediction(+P)、
Detection error (DER) 、True positive (TP)
Conclusion
• 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%
References
• 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
Thank you for your
attention