1. 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 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

2. 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

3. 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

4. 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

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

6. 1)Confounding factor—HR – Blood pressure is related to heart rate as well as to PAT in the cardiovascular system Methods 1)Confounding factor—HR 2)Confounding factor—arterial stiffness 3)Experiments

7. 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.

8. 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

9. Amplitude parameters Time parameters Slope parameters Methods Comparable parameters of arterial stiffness in PPG.

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

11. 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

12. Results 1)Correlation of blood pressure with confounding factors 2)Single and multiple regression analysis 3)Reproducibility

13. 1)Correlation of blood pressure with confounding factors Results Correlation between BP and BP estimating parameters for patient A

14. 2)Single and multiple regression analysis 3)(BP = a + b ∗ PAT + c ∗ HR + d ∗ TDB) Results (BP = a + b ∗ PAT + c ∗ HR + d ∗ TDB)

15. Results (BP = a + b ∗ PAT + c ∗ HR + d ∗ TDB)

16. 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

17. 1)Correlation with blood pressure 2)Waveform analysis of PPG 3)Limitation of the study 4)Application to home health care Discussion and Conclusion

18. 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

19. Introducing the multiscale mathematical morphology(3M) filtering concept into QRS detection Introduction

20. Multiscale Mathematical Morphology Theory

21. Proposed Implementation Scheme 1)Multiscale Mathematical Morphology Filtering 2)Differential Operation 3)Enhancing ECG by Modulus and Combination 4)Threshold and Decision

22. 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

23. 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

24. 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

25. 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

26. 2)Differential Operation - After 3M filtering, the output ECG sequence is differentiated in order to remove motion artifacts and baseline drifts Proposed Implementation Scheme

27. 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 maximum duration of the normal QRS complex

28. 4)Threshold and Decision – The detection of a QRS complex is accomplished by comparing the feature against a threshold Proposed Implementation Scheme

29. The MIT/BIH Arrhythmia Database is used to evaluate our algorithm Experimental Results

31. False Negative(FN) 、 False Positive (FP) 、 Sensitivity (Se) 、 Positive Prediction(+P) 、 Detection error (DER) 、 True positive (TP) Experimental Results

32. 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

33. 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

34. Thank you for your attention