BOOK PROPOSAL

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Title:
Feature Engineering and Computational Intelligence in ECG Monitoring

Editors:
Chengyu Liu¹ and Jianqing Li^2
1State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University
²School of Biomedical Engineering and Informatics, Nanjing Medical University

Call for Chapters:
Recent advances in wearables and Internet of Things (IoT) devices has led to an explosion of routinely collected individual electrocardiogram (ECG) data. The use of feature engineering and computational intelligence methods to turn these ever-growing ECG monitoring data into clinical benefits seems as if it should be an obvious path to take. However, this field is still in its infancy, and lots of essential concepts and method solutions should be reviewed and clarified in depth.

The purpose of this book is to summarize the feature engineering and computational intelligence solutions for ECG monitoring, with an emphasis on how these methods can be efficiently used on the emerging need and challenge -- dynamic, continuous & long-term individual ECG monitoring and real-time feedback, aiming to provide a “snapshot” of the state of current research at the interface between physiological signal analysis and machine learning. It could help clarify some dilemmas and encourage further investigations in this field, to explore rational applications of feature engineering and computational intelligence in clinical practices for ECG monitoring. Original, high quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are welcomed, with special emphasis on, but not limited to, the following chapter topics:

• Feature engineering in ECG monitoring: concept, motivation, method and physiological mechanism
• Computational intelligence in individual ECGs: method, opportunity and challenge
• Developments of signal quality features for noisy ECG rejection
• Advance of T wave detection and ST segment identification
• Intra- and inter-subject variability for heart rate variability (HRV) parameters in long-term ECG recordings
• Efficient feature selection can improve the patient care in ICU
• Advance of support vector machine on ECG analysis
• Advance of convolutional neural network on ECG analysis
• Advance of Bayesian network on ECG analysis
• Open source opportunity for ECG monitoring, including open access database, or open toolbox platform
• A survey on artificial intelligence, individual health and wearable device
• Automatic identification of the rhythm/morphology abnormalities in 12-lead ECGs
(We welcome the methodological contributions from the first China Physiological Signal Challenge (CPSC 2018 where the editor acted as the challenge chair, http://2018.icbeb.org/Challenge.html to address the computational intelligence challenging of automatic identification of the rhythm/morphology abnormalities in 12-lead ECGs. The specific titles for these chapters will be determined after the draft submissions.)
• Robust QRS detection and heart rate estimation on challenging ECG data
(We welcome the methodological contributions from the second China Physiological Signal Challenge (CPSC 2019), to address the challenging of robust QRS detection and heart rate estimation for the challenging ECG episodes (usually noisy and/or with arrhythmia situation). The specific titles for these chapters will be determined after the draft submissions.)

Timetable:
Manuscript Due: August, 1st, 2019
First Round of Reviews: October, 1st, 2019
Publication Date: January, 1st, 2020

Manuscript submitted to:
Dr. Chengyu Liu chengyu@seu.edu.cn

Thank you very much for your interest in publishing in this book with Springer Nature.