國立虎尾科技大學 |

Feature Engineering and Computational Intelligence in ECG Monitoring

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Feature Engineering and Computational Intelligence in ECG Monitoring/ edited by Chengyu Liu, Jianqing Li.
其他作者:	Li, Jianqing.
面頁冊數:	X, 268 p. 101 illus., 77 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Intelligence. -
電子資源:	https://doi.org/10.1007/978-981-15-3824-7
ISBN:	9789811538247

Feature Engineering and Computational Intelligence in ECG Monitoring
Feature Engineering and Computational Intelligence in ECG Monitoring[electronic resource] /edited by Chengyu Liu, Jianqing Li. - 1st ed. 2020. - X, 268 p. 101 illus., 77 illus. in color.online resource.

Chapter 1. Feature engineering and computational intelligence in ECG monitoring – an introduction -- Chapter 2. Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing -- Chapter 3. An Overview of signal quality indices on dynamic ECG signal quality assessment -- Chapter 4. Signal quality features in dynamic ECGs -- Chapter 5. Motion Artifact Suppression Method in Wearable ECG -- Chapter 6. Data Augmentation for Deep Learning based ECG analysis -- Chapter 7. Study on Automatic Classification of Arrhythmias -- Chapter 8. ECG Interpretation with deep learning -- Chapter 9. Visualizing ECG contribution into Convolutional Neural Network classification -- Chapter 10. Atrial fibrillation detection in dynamic signals -- Chapter 11. Applications of Heart rate variability in Sleep Apnea -- Chapter 12. False Alarm Rejection for ICU ECG Monitoring -- Chapter 13. Respiratory Signal Extraction from ECG Signal -- Chapter 14. Noninvasive Recording of Cardiac Autonomic Nervous Activity--What’s behind ECG? -- Chapter 15. A questionnaire study on artificial intelligence and its effects on individual health and wearable device.

This book discusses feature engineering and computational intelligence solutions for ECG monitoring, with a particular focus on how these methods can be efficiently used to address the emerging challenges of dynamic, continuous & long-term individual ECG monitoring and real-time feedback. By doing so, it provides a “snapshot” of the current research at the interface between physiological signal analysis and machine learning. It also helps clarify a number of dilemmas and encourages further investigations in this field, to explore rational applications of feature engineering and computational intelligence in ECG monitoring. The book is intended for researchers and graduate students in the field of biomedical engineering, ECG signal processing, and intelligent healthcare.

ISBN: 9789811538247

Standard No.: 10.1007/978-981-15-3824-7doiSubjects--Topical Terms:

768837
Computational Intelligence.

LC Class. No.: R856-R857

Dewey Class. No.: 610.28

Feature Engineering and Computational Intelligence in ECG Monitoring
LDR:03342nam a22003975i 4500 001 1020432
003 DE-He213
005 20200702075856.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811538247 $9 978-981-15-3824-7
024 7 $a 10.1007/978-981-15-3824-7 $2 doi
035 $a 978-981-15-3824-7
050 4 $a R856-R857
072 7 $a MQW $2 bicssc
072 7 $a MED003040 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
245 1 0 $a Feature Engineering and Computational Intelligence in ECG Monitoring $h [electronic resource] / $c edited by Chengyu Liu, Jianqing Li.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a X, 268 p. 101 illus., 77 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
505 0 $a Chapter 1. Feature engineering and computational intelligence in ECG monitoring – an introduction -- Chapter 2. Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing -- Chapter 3. An Overview of signal quality indices on dynamic ECG signal quality assessment -- Chapter 4. Signal quality features in dynamic ECGs -- Chapter 5. Motion Artifact Suppression Method in Wearable ECG -- Chapter 6. Data Augmentation for Deep Learning based ECG analysis -- Chapter 7. Study on Automatic Classification of Arrhythmias -- Chapter 8. ECG Interpretation with deep learning -- Chapter 9. Visualizing ECG contribution into Convolutional Neural Network classification -- Chapter 10. Atrial fibrillation detection in dynamic signals -- Chapter 11. Applications of Heart rate variability in Sleep Apnea -- Chapter 12. False Alarm Rejection for ICU ECG Monitoring -- Chapter 13. Respiratory Signal Extraction from ECG Signal -- Chapter 14. Noninvasive Recording of Cardiac Autonomic Nervous Activity--What’s behind ECG? -- Chapter 15. A questionnaire study on artificial intelligence and its effects on individual health and wearable device.
520 $a This book discusses feature engineering and computational intelligence solutions for ECG monitoring, with a particular focus on how these methods can be efficiently used to address the emerging challenges of dynamic, continuous & long-term individual ECG monitoring and real-time feedback. By doing so, it provides a “snapshot” of the current research at the interface between physiological signal analysis and machine learning. It also helps clarify a number of dilemmas and encourages further investigations in this field, to explore rational applications of feature engineering and computational intelligence in ECG monitoring. The book is intended for researchers and graduate students in the field of biomedical engineering, ECG signal processing, and intelligent healthcare.
650 2 4 $a Computational Intelligence. $3 768837
650 1 4 $a Biomedical Engineering/Biotechnology. $3 1068811
650 0 $a Bioinformatics. $3 583857
650 0 $a Computational intelligence. $3 568984
650 0 $a Biomedical engineering. $3 588770
700 1 $a Li, Jianqing. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1315902
700 1 $a Liu, Chengyu. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1315901
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811538230
776 0 8 $i Printed edition: $z 9789811538254
776 0 8 $i Printed edition: $z 9789811538261
856 4 0 $u https://doi.org/10.1007/978-981-15-3824-7
912 $a ZDB-2-SBL
912 $a ZDB-2-SXB
950 $a Biomedical and Life Sciences (SpringerNature-11642)
950 $a Biomedical and Life Sciences (R0) (SpringerNature-43708)