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

Feature engineering and computationa...

Liu, Chengyu.

FindBook

Google Book

Amazon

博客來

Feature engineering and computational intelligence in ECG monitoring

紀錄類型:	書目-電子資源 : 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:	Liu, Chengyu.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	x, 268 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Biomedical engineering. -
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. - Singapore :Springer Singapore :2020. - x, 268 p. :ill., digital ;24 cm.

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:

535387
Biomedical engineering.

LC Class. No.: R856 / .F438 2020

Dewey Class. No.: 610.28

Feature engineering and computational intelligence in ECG monitoring
LDR:02950nmm a2200325 a 4500 001 2220982
003 DE-He213
005 20201023171302.0
006 m d
007 cr nn 008maaau
008 201216s2020 si s 0 eng d
020 $a 9789811538247 $q (electronic bk.)
020 $a 9789811538230 $q (paper)
024 7 $a 10.1007/978-981-15-3824-7 $2 doi
035 $a 978-981-15-3824-7
040 $a GP $c GP
041 0 $a eng
050 4 $a R856 $b .F438 2020
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
090 $a R856 $b .F288 2020
245 0 0 $a Feature engineering and computational intelligence in ECG monitoring $h [electronic resource] / $c edited by Chengyu Liu, Jianqing Li.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a x, 268 p. : $b ill., digital ; $c 24 cm.
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 0 $a Biomedical engineering. $3 535387
650 0 $a Computational intelligence. $3 595739
650 0 $a Bioinformatics. $3 553671
650 1 4 $a Biomedical Engineering/Biotechnology. $3 2162071
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Liu, Chengyu. $3 3458919
700 1 $a Li, Jianqing. $3 3458920
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-3824-7
950 $a Biomedical and Life Sciences (Springer-11642)