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Feature Engineering and Computational Intelligence in ECG Monitoring

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Feature Engineering and Computational Intelligence in ECG Monitoring/ edited by Chengyu Liu, Jianqing Li.
other author:	Liu, Chengyu.
Description:	X, 268 p. 101 illus., 77 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Biomedical engineering. -
Online resource:	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:

588770
Biomedical engineering.

LC Class. No.: R856-R857

Dewey Class. No.: 610.28

Feature Engineering and Computational Intelligence in ECG Monitoring
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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.
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