國立虎尾科技大學 |

Respiratory Disease Diagnosis for Dolphin Using Breath Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Respiratory Disease Diagnosis for Dolphin Using Breath Data./
Author:	Lai, Chinh Thi Tuyet.
Description:	1 online resource (44 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355190113

Respiratory Disease Diagnosis for Dolphin Using Breath Data.
Lai, Chinh Thi Tuyet.

Respiratory Disease Diagnosis for Dolphin Using Breath Data. - 1 online resource (44 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.Sc.)--Texas A&M University - Corpus Christi, 2017.

Includes bibliographical references

Respiratory disease in marine mammals evokes strong public attention as well as worthwhile scientific interest. Traditional methods for animal disease diagnosis include blood test, ultrasound, and computed tomography scan. These methods require invasive equipment to perform, and cannot be applied to free-swimming animals.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355190113Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Respiratory Disease Diagnosis for Dolphin Using Breath Data.
LDR:03001ntm a2200349K 4500 001 913307
005 20180618102617.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355190113
035 $a (MiAaPQ)AAI10608271
035 $a (MiAaPQ)tamucc:10291
035 $a AAI10608271
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Lai, Chinh Thi Tuyet. $3 1186087
245 1 0 $a Respiratory Disease Diagnosis for Dolphin Using Breath Data.
264 0 $c 2017
300 $a 1 online resource (44 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: 56-06.
500 $a Adviser: Lei Jin.
502 $a Thesis (M.Sc.)--Texas A&M University - Corpus Christi, 2017.
504 $a Includes bibliographical references
520 $a Respiratory disease in marine mammals evokes strong public attention as well as worthwhile scientific interest. Traditional methods for animal disease diagnosis include blood test, ultrasound, and computed tomography scan. These methods require invasive equipment to perform, and cannot be applied to free-swimming animals.
520 $a Breath data, the measurement of lung inflow and outflow while breathing, can be collected from free-swimming animals in a non-invasive way, and so is less stressful for distressed animals. However, because of new features in the data, new statistical methods are required for the breath data analysis. In this thesis, we investigate one potential method for analyzing breath data. Our method begins by decomposing a raw dataset containing a sequence of breath cycles into a set of individual breath cycles. Incomplete cycles are removed from the dataset. In this research, we consider an entire breath cycle to be one unit of observation. Starting and ending points of breath cycles can be difficult to determine, and cause a large amount of variation in size and shape of breath curves. To reduce cycle to cycle variability, we apply curve registration to synchronize a set of breath cycles. Breath cycles are described using magnitude information and geometric shape information. We propose three shape models, namely, simple oval model, quadratic spline model, and piecewise linear model. Furthermore, principal component analysis is applied to the magnitude/shape descriptors to obtain main features of breath cycles. Criteria for disease diagnosis are developed by identifying key differences among these main features between healthy and unhealthy animals. The proposed methods were applied to check if two testing animals are diseased or not. The results were consistent with the status of both animals.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Mathematics. $3 527692
650 4 $a Biology. $3 599573
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0405
690 $a 0306
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Texas A&M University - Corpus Christi. $b Mathematics. $3 1186088
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10608271 $z click for full text (PQDT)