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Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System

Authors
Lee, Young-ChanKo, Doo-HyeonSon, Min-HyeongYang, Se-HwanUm, Ji-Yong
Issue Date
Jun-2024
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Probes; Ultrasonic imaging; Monitoring; Support vector machines; Biomedical monitoring; Blood pressure; Field programmable gate arrays; Arterial distension monitoring; field programmable gate array (FPGA); sequential support vector machine (SVM); ultrasound probe positioning; ultrasound scanner
Citation
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, v.18, no.3, pp 702 - 713
Pages
12
Journal Title
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
Volume
18
Number
3
Start Page
702
End Page
713
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28783
DOI
10.1109/TBCAS.2024.3363134
ISSN
1932-4545
1940-9990
Abstract
This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.
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