Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Lei Wu; Shuli Guo; Lina Han; Xiaowei Song; Zhilei Zhao; Anil Baris Cekderi

doi:10.1007/s13755-023-00237-8

Autonomous detection of myocarditis based on the fusion of improved quantum genetic algorithm and adaptive differential evolution optimization back propagation neural network

Health Inf Sci Syst. 2023 Aug 1;11(1):33. doi: 10.1007/s13755-023-00237-8. eCollection 2023 Dec.

Authors

Lei Wu^#¹, Shuli Guo^#¹, Lina Han², Xiaowei Song¹, Zhilei Zhao¹, Anil Baris Cekderi¹

Affiliations

¹ National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China.
² Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

^# Contributed equally.

PMID: 37538261
PMCID: PMC10393931 (available on 2024-12-01)
DOI: 10.1007/s13755-023-00237-8

Abstract

Myocarditis is cardiac damage caused by a viral infection. Its result often leads to a variety of arrhythmias. However, rapid and reliable identification of myocarditis has a great impact on early diagnosis, expedited treatment, and improved patient survival rates. Therefore, a novel strategy for the autonomous detection of myocarditis is suggested in this work. First, the improved quantum genetic algorithm (IQGA) is proposed to extract the optimal features of ECG beat and heart rate variability (HRV) from raw ECG signals. Second, the backpropagation neural network (BPNN) is optimized using the adaptive differential evolution (ADE) algorithm to classify various ECG signal types with high accuracy. This study examines analogies among five different ECG signal types: normal, abnormal, myocarditis, myocardial infarction (MI), and prior myocardial infarction (PMI). Additionally, the study uses binary and multiclass classification to group myocarditis with other cardiovascular disorders in order to assess how well the algorithm performs in categorization. The experimental results demonstrate that the combination of IQGA and ADE-BPNN can effectively increase the precision and accuracy of myocarditis autonomous diagnosis. In addition, HRV assesses the method's robustness, and the classification tool can detect viruses in myocarditis patients one week before symptoms worsen. The model can be utilized in intensive care units or wearable monitoring devices and has strong performance in the detection of myocarditis.

Keywords: Back propagation neural network; Electrocardiogram; Feature classification; Myocarditis detection; Quantum genetic algorithm.

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