Automated Aortic Doppler Flow Tracing for Reproducible Research and Clinical Measurements

Zolgharni, Massoud; Dhutia, Niti M.; Cole, Graham D.; Bahmanyar, Mohammad Reza; Jones, Siana; Sohaib, S.M. Afzal; Tai, Sarah B.; Willson, Keith; Finegold, Judith A.; Francis, Dárrel P.

doi:10.1109/tmi.2014.2303782

Cited by 24 publications

(28 citation statements)

References 27 publications

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“…Lastly, our program is not entirely automated as the user is still required to manually export the raw video files, input scaling factors, and potentially alter the envelope threshold in the program. Even so, it is much quicker than manual analysis and similar to other non-coronary flow TTDE programs that have reduced time of analysis by 7.5-25 fold 14 , 20 , 22 . However, creating a fully automated program would reduce the analysis time even further.…”

Section: Discussionmentioning

confidence: 99%

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Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow

Sunyecz

McCallinhart

Patel

et al. 2018

Sci Rep

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The coronary microcirculation (CM) plays a critical role in the regulation of blood flow and nutrient exchange to support the viability of the heart. In many disease states, the CM becomes structurally and functionally impaired, and transthoracic Doppler echocardiography can be used as a non-invasive surrogate to assess CM disease. Analysis of Doppler echocardiography is prone to user bias and can be laborious, especially if additional parameters are collected. We hypothesized that we could develop a MATLAB algorithm to automatically analyze clinically-relevant and non-traditional parameters from murine PW Doppler coronary flow patterns that would reduce intra- and inter-operator bias, and analysis time. Our results show a significant reduction in intra- and inter-observer variability as well as a 30 fold decrease in analysis time with the automated program vs. manual analysis. Finally, we demonstrated good agreement between automated and manual analysis for clinically-relevant parameters under baseline and hyperemic conditions. Resulting coronary flow velocity reserve calculations were also found to be in good agreement. We present a MATLAB algorithm that is user friendly and robust in defining and measuring Doppler coronary flow pattern parameters for more efficient and potentially more insightful analysis assessed via Doppler echocardiography.

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Section: Discussionmentioning

confidence: 99%

“… 14 and Zholgharni et al . 20 . The final resulting images were the full Doppler CFP sequence from the zero-velocity baseline to the maximum velocity and the full ECG recording ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow

Sunyecz

McCallinhart

Patel

et al. 2018

Sci Rep

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“…The two extracted velocity profiles were then used for detection of end‐diastolic and end‐systolic frames. Further details are provided in and elsewhere . The location of detected frames from the regional approach (auto‐septum) is plotted in Figure as blue square markers on the velocity profiles spanning three cardiac cycles.…”

Section: Methodsmentioning

confidence: 99%

“…Further details on this estimation method which we have also applied to the long strips of spectral Doppler traces can be found elsewhere. 15 To filter out the high-frequency noise, a low-pass first-order Butterworth digital filter was applied to the crude velocity profiles.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Automatic detection of end‐diastolic and end‐systolic frames in 2D echocardiography

et al. 2017

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“…They are Bland-Altman, Q-Q plot, and Density Function [6], [7]. Specifically and in contrast to the work presented by Zolgharni et al [8], the Bland-Altman method is utilized to validate the results of the new scheme or system. In this method, a check for the agreement between the data of the peak and time integral of the velocity in the context of clinical measurement will be conducted.…”

Section: Related Workmentioning

confidence: 99%

Design of Industrial Wireless Sensor System for Real-Time Data Collection in Steam Turbines

Lilo¹,

Latiff²,

Abu³

et al. 2016

IJEEE

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Wireless Sensors (WS) are utilized in industries, medical, greenhouses, and other applications to sense and monitor parameters required to control and utilize data. There are many challenges to implement WS networks such as collision, end-to-end delay, dead nodes and power consumption to name a few; however, the main challenge that face the implementation of WS are the sensing and transferring the data between the sensor and the system and the time taken. The time taken of a wired sensor to transfer data is undoubtedly slightly shorter than the time taken in WS. This paper intends to design WS that could reduce the data transfer time and compare the amount of transferred data and the time taken by the wired system. The WS design will be validated first by Bland-Altman method and data will be collected using MATLAB and the PLX-DAQ program. The results show that the amount and time taken to transfer data in WS is comparable to the wired system. The validated results suggested by this work are more than 95% in similarity to Bland-Altman. This led us to conclude that WS can be utilized with a multitude of applications in place of wired sensors.  Index Terms-industrial wireless sensor, real time processing, Bland-Altman plot, wireless speed sensor Aminudin Bin Haji Abu was born in Malaysia, in 1972. He received the Ph.D. in. Engineering Automotive Eng. from

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Automated Aortic Doppler Flow Tracing for Reproducible Research and Clinical Measurements

Cited by 24 publications

References 27 publications

Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow

Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow

Automatic detection of end‐diastolic and end‐systolic frames in 2D echocardiography

Design of Industrial Wireless Sensor System for Real-Time Data Collection in Steam Turbines

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