國立虎尾科技大學 |

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings/ by Thuy T. Pham.
Author:	Pham, Thuy T.
Description:	XV, 107 p. 35 illus., 32 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Biomedical engineering. -
Online resource:	https://doi.org/10.1007/978-3-319-98675-3
ISBN:	9783319986753

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
Pham, Thuy T.

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings[electronic resource] /by Thuy T. Pham. - 1st ed. 2019. - XV, 107 p. 35 illus., 32 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053. - Springer Theses, Recognizing Outstanding Ph.D. Research,.

Introduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion .

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

ISBN: 9783319986753

Standard No.: 10.1007/978-3-319-98675-3doiSubjects--Topical Terms:

588770
Biomedical engineering.

LC Class. No.: R856-857

Dewey Class. No.: 610.28

Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
LDR:02536nam a22004335i 4500 001 1013219
003 DE-He213
005 20200706030700.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319986753 $9 978-3-319-98675-3
024 7 $a 10.1007/978-3-319-98675-3 $2 doi
035 $a 978-3-319-98675-3
050 4 $a R856-857
050 4 $a HC79.E5
050 4 $a GE220
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
100 1 $a Pham, Thuy T. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1307521
245 1 0 $a Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings $h [electronic resource] / $c by Thuy T. Pham.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XV, 107 p. 35 illus., 32 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5053
505 0 $a Introduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion .
520 $a This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
650 0 $a Biomedical engineering. $3 588770
650 0 $a Data mining. $3 528622
650 0 $a Computational intelligence. $3 568984
650 0 $a Bioinformatics. $3 583857
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Computational Intelligence. $3 768837
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319986746
776 0 8 $i Printed edition: $z 9783319986760
776 0 8 $i Printed edition: $z 9783030075187
830 0 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5053 $3 1253569
856 4 0 $u https://doi.org/10.1007/978-3-319-98675-3
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)