Abstract

Info

Title:: Arrhythmia classification method based on genetic algorithm optimization of C-LSTM model

Author(s):: WANG Wei; DING Hui; XIA Xu; WU Hao; ZHANG Ying; GUO Jiacheng; School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Keywords:: Keywords: arrhythmia classification genetic algorithm GC-LSTM model hyper-parameter

Abstract:: Abstract: A GC-LSTM model is proposed based on the characteristics of global optimization of genetic algorithm. The model automatically and iteratively searches the optimal hyper-parameter configuration of the C-LSTM model through the genetic algorithm of a specific genetic strategy, and it is configured using the genetic iteration results and validated on the MIT-BIH arrhythmia database according to the classification criteria of the Association for the Advancement of Medical Instrumentation. The testing shows that the classification accuracy, sensitivity, accuracy and F1 value of GC-LSTM model are 99.37%, 95.62%, 95.17% and 95.39%, respectively, higher than those of the manually established model, and it is also advantageous over the existing mainstream methods. Experimental results demonstrate that the proposed method can achieve better classification performance while avoiding a large number of experimental parameters.