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

VernacularTitle:基于遗传算法优化C-LSTM模型的心律失常分类方法
Author: Wei WANG ¹ ; Hui DING ; Xu XIA ; Hao WU ; Ying ZHANG ; Jiacheng GUO
Author Information

1. 重庆邮电大学光电工程学院,重庆 400065
Keywords: arrhythmia classification; genetic algorithm; GC-LSTM model; hyper-parameter
From: Chinese Journal of Medical Physics 2024;41(2):233-240
CountryChina
Language:Chinese
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.