東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements./
作者:	Leong, Christopher James.
面頁冊數:	1 online resource (77 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30491261click for full text (PQDT)
ISBN:	9798379895020

Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements.
Leong, Christopher James.

Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements. - 1 online resource (77 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--University of Hawai'i at Manoa, 2023.

Includes bibliographical references

Detecting abnormal excessive buildup of fluid in the lungs, or pulmonary edema, is crucial in preventing conditions such as heart failure, kidney failure, and acute respiratory distress syndrome (ARDS). Most existing methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. To provide continuous and non-invasive monitoring of lung water status, Hawaii Advanced Wireless Technologies Institute (HAWTI) invented the Cardio-Pulmonary Stethoscope (CPS), a low-cost device with chest patch radio frequency (RF) sensors that was proven to be able to detect heart rate, respiration rate, and changes in lung water content from a single RF measurement. The CPS measurement procedure and the accuracy of results have been verified in a National Institute of Health (NIH) sponsored clinical trial conducted in collaboration with The Queen's Medical Center in Honolulu.This thesis presents recent advances in expanding the capability of the CPS for assessing lung water status, in addition to monitoring the change in lung water, using artificial intelligence (AI). An important first step in our AI pipeline is to build a database of a diverse patient population. To this end, we utilize an NIH dataset consisting of CT-scans of patients of various genders, ages, and body fat compositions. We then develop an automatic workflow that reads the CT-scans and creates 3-D models for high-fidelity simulation in Ansys High Frequency Structure Simulator (HFSS). From HFSS, we obtain scattering parameters (S-parameters) measured by the CPS at various lung water levels. Compared to data collection from clinical trials, this "Virtual Clinical Trial" approach is low-cost, less time-consuming, and risk-free.Using the database we built, we develop AI models which use the patient metadata, namely gender, age, fat thickness, and S-parameters from the CPS as input, and output its assessment of the lung water status (i.e., normal, edematous, and severely edematous statuses). For a cohort of over 200 diverse individuals, our AI models achieve above 70% accuracy in assessing the lung water status. Furthermore, our AI models are interpretable and simple to explain.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379895020Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

CardiopulmonaryIndex Terms--Genre/Form:

542853
Electronic books.

Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements.
LDR:03587nmm a2200385K 4500 001 2362735
005 20231102122812.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798379895020
035 $a (MiAaPQ)AAI30491261
035 $a AAI30491261
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Leong, Christopher James. $3 3703474
245 1 0 $a Toward Assessment of Lung Water Content Using Wireless Cardiopulmonary Stethoscope Measurements.
264 0 $c 2023
300 $a 1 online resource (77 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Iskander, Magdy F.
502 $a Thesis (M.S.)--University of Hawai'i at Manoa, 2023.
504 $a Includes bibliographical references
520 $a Detecting abnormal excessive buildup of fluid in the lungs, or pulmonary edema, is crucial in preventing conditions such as heart failure, kidney failure, and acute respiratory distress syndrome (ARDS). Most existing methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. To provide continuous and non-invasive monitoring of lung water status, Hawaii Advanced Wireless Technologies Institute (HAWTI) invented the Cardio-Pulmonary Stethoscope (CPS), a low-cost device with chest patch radio frequency (RF) sensors that was proven to be able to detect heart rate, respiration rate, and changes in lung water content from a single RF measurement. The CPS measurement procedure and the accuracy of results have been verified in a National Institute of Health (NIH) sponsored clinical trial conducted in collaboration with The Queen's Medical Center in Honolulu.This thesis presents recent advances in expanding the capability of the CPS for assessing lung water status, in addition to monitoring the change in lung water, using artificial intelligence (AI). An important first step in our AI pipeline is to build a database of a diverse patient population. To this end, we utilize an NIH dataset consisting of CT-scans of patients of various genders, ages, and body fat compositions. We then develop an automatic workflow that reads the CT-scans and creates 3-D models for high-fidelity simulation in Ansys High Frequency Structure Simulator (HFSS). From HFSS, we obtain scattering parameters (S-parameters) measured by the CPS at various lung water levels. Compared to data collection from clinical trials, this "Virtual Clinical Trial" approach is low-cost, less time-consuming, and risk-free.Using the database we built, we develop AI models which use the patient metadata, namely gender, age, fat thickness, and S-parameters from the CPS as input, and output its assessment of the lung water status (i.e., normal, edematous, and severely edematous statuses). For a cohort of over 200 diverse individuals, our AI models achieve above 70% accuracy in assessing the lung water status. Furthermore, our AI models are interpretable and simple to explain.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 649834
650 4 $a Electromagnetics. $3 3173223
650 4 $a Biomedical engineering. $3 535387
653 $a Cardiopulmonary
653 $a Radio frequency
653 $a Kidney failure
653 $a Lung water
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0544
690 $a 0607
690 $a 0541
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Hawai'i at Manoa. $b Electrical Engineering. $3 3341974
773 0 $t Masters Abstracts International $g 85-01.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30491261 $z click for full text (PQDT)