Ⅴ. Implementation of continuous blood pressure monitoring systems
5.2 Stationary continuous BP monitoring system
5.2.2 Experimental results
As shown in figure 5.8, The pulse transit time and blood pressure were measured by four subjects under various conditions such as rest and exercise conditions in order to find out the relationship between the PTT and BP. The BP was measured using a commercial device, and PTT was obtained from the ECG and PPG peaks which are extracted from their waveforms using the digital processing in Matlab. The measured relationship between the PTT and BP are different because the four subjects have different physical characteristics. Compared to each result of the four subjects in the figure 5.3, the results in the figure 5.8 which is obtained using the PC based continuous BP monitoring system have similar trends.
Figure 5.7. Real-time operation of the continuous BP monitoring system with significant digital signal processing in PC Matlab interface.
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In the same way as the analysis method in figure 5.4, figure 5.9 shows correlation plots and their Bland-Altman plots between the measured and estimated systolic blood pressure for the four subjects.
In each result, correlation coefficients have the values from 0.8898 to 0.9294, and standard deviations have the values from 2.97mmHg to 6.85mmHg. Compared to the results in the figure 5.4, it can be seen that the results in figure 5.9 have slightly higher correlation coefficients and lower standard deviations than the BP estimation using the peak detector. The table Ⅵ shows a summary of the BP estimation using the stationary BP monitoring system.
Figure 5.8. Relationship between the measured systolic blood pressure and pulse transit time using the stationary BP monitoring system for four subjects.
Table Ⅵ
Summary of BP estimation results using the stationary BP monitoring system.
PTT calculation Subject PTT-BP equation
(4.1) (4.2) (4.3) (4.4)
Using the peaks obtained by
DSP
Subject 1 R = 0.9294 STD = 3.45
R = 0.9402 STD = 3.18
R = 0.9405 STD = 3.18
R = 0.9403 STD = 3.18 Subject 2 R = 0.8898
STD = 2.97
R = 0.8944 STD = 2.91
R = 0.8934 STD = 2.93
R = 0.8940 STD = 2.72 Subject 3 R = 0.9127
STD = 6.85
R = 0.9387 STD = 5.78
R = 0.9381 STD = 5.81
R = 0.9387 STD = 5.78 Subject 4 R = 0.9057
STD = 4.52
R = 0.8764 STD = 5.14
R = 0.8910 STD = 4.85
R = 0.8662 STD = 5.36
* Correlation coefficient has no dimension.
** Standard deviation (STD) has a dimension of mmHg.
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Figure 5.9. Relationship between the measured SBP and estimated SBP, and their Bland-Altman plots for four subjects using the stationary BP monitoring system.
[Correlation plot : Subject 1]
(a)
[Bland-Altman plots : Subject 1][Correlation plot : Subject 2]
(b)
[Bland-Altman plots : Subject 2][Correlation plot : Subject 3]
(c)
[Bland-Altman plots : Subject 3][Correlation plot : Subject 4]
(d)
[Bland-Altman plots : Subject 4]R = 0.9294
+2· STD
-2· STD
STD = 3.45mmHg
R = 0.8898 STD = 2.97mmHg
R = 0.9127 STD = 6.85mmHg
R = 0.9057 STD = 4.52mmHg
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Chapter Ⅵ
Conclusion
This doctoral thesis aims to design a readout integrated circuit which supports an electrocardiogram and photoplethysmogram signal acquisition, and develop a continuous blood pressure monitoring system using the designed ROIC. Blood pressured is estimated from a pulse transit time which is the time difference of the ECG and PPG, and therefore, the simultaneously measuring ECG and PPG, detecting their peaks, and calculating the pulse transit time should be performed in real time.
There are various requirements for biopotential and PPG signal acquisition, but among them, it is essential to eliminate DC-artifacts such as input DC offset voltage and current. Conventional analog DSL which removes the DC artifacts by implementing a HPF characteristic is widely used, but there is a trade-off to implement a low HPF cutoff frequency and wide IDO tolerance range. To solve the issue, the attenuator-assisted hybrid DSL was proposed. The attenuator-assisted DSL has an effect of lowering the HPF cutoff frequency proportional to the attenuation coefficient, which enables to detect an accurate peak position because it prevents peak shifting and signal distortion. Hybrid DSL is a combined analog and digital DSL, where the digital DSL helps to enlarge the IDO tolerance without the trade-off.
In order to operate the BP monitoring system for a long time, the power consumption of the entire sensor module should be minimized. The PPG AFE output voltage takes a long time to be settled by the photodiode junction capacitance, which causes an increase of LED turn-on time. Therefore, the light- to-digital converter for measuring PPG was additionally designed, and this structure can use a short LED turn-on time regardless of the photodiode junction capacitance, but an offset error occurs instead.
Therefore, a pre-charging scheme was proposed, which removes the offset error by charging the junction capacitor with a reference voltage just before the input sampling phase.
The MCU occupies a significant amount of power consumption of the developed sensor module excluding the bluetooth module for wireless communication. Because the power consumption is proportional to the amount of DSP computation, so the ROIC structure was optimized to minimize the DSP for multi-channel detection and peak detection in MCU. The proposed auto-switching multiplexer replaces a role of the MCU for channel switching for a multi-channel signal acquisition, and the analog peak detector detects ECG and PPG peaks directly from the biopotential and PPG AFE output voltage.
As a result, the MCU was operated with minimized power consumption by reducing the DSP requirements.
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Using the proposed ROIC, portable and stationary continuous BP monitoring systems were implemented using a smartphone and PC Matlab interface, and it was confirmed that both systems support real-time signal acquisition and blood pressure estimation using the pulse transit time. For the system validation, PTT-BP measurements were conducted on four subjects, and the correlation and difference between the measured and estimated BP were analyzed using various mathematical models for PTT to BP conversion.
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ACKNOWLEDGEMENTS
I would like to thank my advisor Prof. Jae Joon Kim, who has given me a lot of advice and guidance.
Thanks to these, I was able to conduct my research successfully during a period of my combined master’s and PhD programs. Also, I would like to thank Prof. Yunsik Lee, Prof. Dong Pyo Jang, Prof.
Myunghee Lee, and Prof. Hoon Eui Jeong for their advice and feedback about my doctoral thesis and defence presentation.
I would like to thank all the members and alumni of my laboratory who study and discuss about my research. Especially, thanks to Kyung Hwan, Subin, Hee Young, Chan Sam, and Hyun Joong for helping and technical discussing a lot.
In particular, I thank my parents and my wife Han Eun for supporting and encouraging me.