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Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Published on: July 23, 2020

An FPGA-based rapid wheezing detection system.

Bor-Shing Lin¹, Tian-Shiue Yen²

¹Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Road, Sanshia District, New Taipei 23741, Taiwan. bslin@mail.ntpu.edu.tw.

International Journal of Environmental Research and Public Health

|February 1, 2014

Summary

This summary is machine-generated.

This study introduces a rapid wheezing detection system using a field-programmable gate array (FPGA). The portable system accurately identifies wheezing in respiratory sounds, aiding long-term patient monitoring for obstructive pulmonary diseases.

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Area of Science:

Biomedical Engineering
Signal Processing
Respiratory Medicine

Background:

Wheezing is a key indicator for diagnosing obstructive pulmonary diseases.
Long-term patient monitoring is essential for managing these conditions.
Existing systems may lack the speed and portability for effective real-time analysis.

Purpose of the Study:

To develop a rapid, portable wheezing detection system using FPGA technology.
To improve the efficiency of wheezing sound analysis for clinical applications.
To create a system adaptable for standalone use or integration into existing biomedical setups.

Main Methods:

A field-programmable gate array (FPGA) based system was designed for real-time wheezing detection.
Sound signals were processed using short-time Fourier transform and spectrogram analysis.
Image processing techniques including 2D bilateral filtering, edge detection, and segmentation were applied.
Features were extracted based on computerized respiratory sound analysis (CORSA) standards and used to train a support vector machine (SVM) classifier.

Main Results:

The developed system achieved a high wheezing recognition performance of 0.912.
The detection process operates efficiently at a clock frequency of 51.97 MHz.
The system demonstrated rapid classification capabilities for wheezing sounds.

Conclusions:

The proposed FPGA-based system offers an effective solution for rapid wheezing detection.
This technology can significantly aid physicians in long-term patient monitoring for respiratory diseases.
The system's portability and speed make it a valuable tool in clinical settings.