Study on Automatic Classification of Arrhythmias

  • Runnan HeEmail author
  • Yang Liu
  • Henggui Zhang


Electrocardiogram (ECG) signals reveal the electrical activity of the heart and can be used to diagnose heart abnormalities. In the past few decades, ECG signals have been utilized for automatic arrhythmia detection owing to the noninvasive nature and convenience of electrocardiography. However, it is difficult to extract and select reliable features or design robust and generic classifiers because of the complexity and diversity of ECG signals. Consequently, improving the classification rate of arrhythmias still remains a considerable challenge. To resolve this pressing issue, we have proposed a model composed of preprocessing, feature extraction, and classification, where the correct implementation of each part is crucial for final arrhythmia identification. In this chapter, the literature on existing algorithms is comprehensively reviewed according to the aforementioned primary aspects.


ECG Arrhythmia Classification Machine learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and Technology, Harbin Institute of TechnologyHarbinChina
  2. 2.Biological Physics Group, Department of Physics and AstronomyUniversity of ManchesterManchesterUK
  3. 3.Peng Cheng LaboratoryShenzhenChina
  4. 4.Pilot National Laboratory of Marine Science and TechnologyQingdaoChina
  5. 5.International Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical ImageMinistry of Education, Northeastern UniversityShenyangChina

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