A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set

  • Haoren Wang
  • Haotian Shi
  • Xiaojun Chen
  • Liqun Zhao
  • Yixiang Huang
  • Chengliang LiuEmail author
Mobile & Wireless Health
Part of the following topical collections:
  1. Precision Medicine with Big Data


This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction.


Electrocardiogram (ECG); Heartbeat classification Complex feature set Precision medicine Human-computer interaction MIT database 



Our research is supported by the National Key R&D Program of China (No. 2018YFB1307005).

Compliance with ethical standards

Conflict of interest

All authors declare that there is no conflict of interest in this work.

Ethical approval

All the procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We have obtained the ethical approval for ECG data from Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Haoren Wang
    • 1
  • Haotian Shi
    • 1
  • Xiaojun Chen
    • 1
  • Liqun Zhao
    • 2
  • Yixiang Huang
    • 1
  • Chengliang Liu
    • 1
    Email author
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Department of CardiologyShanghai First People’s Hospital Affiliated to Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

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