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  • Book
    edited by Chengyu Liu, Jianqing Li.
    Summary: 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.

    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
    Whats behind ECG?
    Chapter 15. A questionnaire study on artificial intelligence and its effects on individual health and wearable device.
    Digital Access Springer 2020