Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 1, 2015

Abstract

Premature ventricular contraction (PVC) is a common type of abnormal heartbeat. Without early diagnosis and proper treatment, PVC may result in serious harms. Diagnosis of PVC is of great importance in goal-directed treatment and preoperation prognosis. This paper proposes a novel diagnostic method for PVC based on Lyapunov exponents of electrocardiogram (ECG) beats. The methodology consists of preprocessing, feature extraction and classification integrated into the system. PVC beats can be classified and differentiated from other types of abnormal heartbeats by analyzing Lyapunov exponents and training a learning vector quantization (LVQ) neural network. Our algorithm can obtain a good diagnostic result with little features by using single lead ECG data. The sensitivity, positive predictability, and the overall accuracy of the automatic diagnosis of PVC is 90.26%, 92.31%, and 98.90%, respectively. The effectiveness of the new method is validated through extensive tests using data from MIT-BIH database. The experimental results show that the proposed method is efficient and robust.

Authors

  • Xiuling Liu
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071000, Hebei, China.
  • Haiman Du
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China.
  • Guanglei Wang
    Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China.
  • Suiping Zhou
    School of Science and Technology, Middlesex University, UK.
  • Hong Zhang
    Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.

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