Feature Engineering and Computational Intelligence in ECG Monitoring

1. Front Matter

   Pages i-x

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Feature Engineering and Computational Intelligence in ECG Monitoring: An Introduction

      • Chengyu Liu, Jianqing Li

      Pages 3-9

3. Databases Available for Research

   1. Front Matter

      Pages 11-11

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   2. Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing

      • Hongxiang Gao, Chengyu Liu, Qin Shen, Jianqing Li

      Pages 13-29

4. Relevance of Signal Quality

   1. Front Matter

      Pages 31-31

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   2. An Overview of Signal Quality Indices on Dynamic ECG Signal Quality Assessment

      • Feifei Liu, Shoushui Wei, Fei Lin, Xinge Jiang, Chengyu Liu

      Pages 33-54

   3. Signal Quality Features in Dynamic ECGs

      • Yixuan Li, Chengyu Liu, Yuwei Zhang, Jianqing Li

      Pages 55-71

   4. Motion Artefact Suppression Method for Wearable ECGs

      • Huanqian Zhang, Jianlong Zhao

      Pages 73-88

5. Latest Techniques for Machine Leaning

   1. Front Matter

      Pages 89-89

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   2. Data Augmentation for Deep Learning-Based ECG Analysis

      • Qing Pan, Xinyi Li, Luping Fang

      Pages 91-111

   3. Study on Automatic Classification of Arrhythmias

      • Runnan He, Yang Liu, Henggui Zhang

      Pages 113-141

   4. ECG Interpretation with Deep Learning

      • Wenjie Cai, Danqin Hu

      Pages 143-156

   5. Visualizing ECG Contribution into Convolutional Neural Network Classification

      • Yaowei Li, Frédéric Precioso, Chengyu Liu

      Pages 157-174

6. Practical Applications

   1. Front Matter

      Pages 175-175

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   2. Atrial Fibrillation Detection in Dynamic Signals

      • Caiyun Ma, Shoushui Wei, Chengyu Liu

      Pages 177-195

   3. Applications of Heart Rate Variability in Sleep Apnea

      • Xiaotong Dong, Shoushui Wei, Hongru Jiang, Chengyu Liu

      Pages 197-213

   4. False Alarm Rejection for ICU ECG Monitoring

      • Jian Dai, Zehui Sun, Xianliang He

      Pages 215-226

   5. Respiratory Signal Extraction from ECG Signal

      • Kejun Dong, Li Zhao, Chengyu Liu

      Pages 227-243

   6. Noninvasive Recording of Cardiac Autonomic Nervous Activity: What Is Behind ECG?

      • Yike Zhang, Chang Cui, Minglong Chen

      Pages 245-255

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.

• Electrocardiogram (ECG)
• Feature engineering
• Computational intelligence
• Machine learning
• ECG Monitoring

• The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China

  Chengyu Liu

• School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China

  Jianqing Li

Dr. Chengyu Liu received his B.S. and Ph.D. degrees in Biomedical Engineering from Shandong University, China, in 2005 and 2010 respectively. He completed his postdoctoral training at Shandong University, China; Newcastle University, UK; and Emory University, USA. He is currently the Interim Dean of the School of Instrument Science and Engineering at Southeast University, a Professor of the State Key Laboratory of Bioelectronics, and the founding Director of the Wearable Heart-Sleep-Emotion Intelligent Monitoring Lab at Southeast University. He is also the founding Chair of the China Physiological Signal Challenge (from 2018), which focuses on challenging ECG signal processing issues. He is a member of the journal committee of the International Federation for Medical and Biological Engineering (IFMBE), an international advisory board member for Physiological Measurement and the Journal of Medical and Biological Engineering. His research topics include wearable ECG & vital-sign monitoring, machine learning for medical big data, early detection, and device development for cardiovascular diseases. He has published over 180 journal/conference papers. 


Dr. Jianqing Li received his B.S. and M.S. degrees in Automatic Technology, and his Ph.D. degree in Measurement Technology and Instruments from the School of Instrument Science and Engineering, Southeast University, China, in 1986, 1990 and 2000 respectively. He is currently the Vice-President of Nanjing Medical University, a Professor at the School of Biomedical Engineering and Informatics, Nanjing Medical University, and a Professor at the School of Instrument Science and Engineering at Southeast University. He is the founding Director of the Key Laboratory of Clinical Medical Engineering in Nanjing Medical University, and the deputy Director of the Jiangsu Key Lab of Remote Measurement and Control at Southeast University, where he leads the research on medical-industry, cross-innovation cooperation, medical device development and clinical applications. His research topics include wearable medical sensors and signal processing, rehabilitation robot technology, and robot telepresence technology. He has been awarded funding for more than 20 research projects and holds over 20 patents.

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