Prabhakar Sunil Kumar, Won Dong-Ok
Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Gangwon-do, South Korea.
Heliyon. 2023 Jul 22;9(8):e18466. doi: 10.1016/j.heliyon.2023.e18466. eCollection 2023 Aug.
The human respiratory systems can be affected by several diseases and it is associated with distinctive sounds. For advanced biomedical signal processing, one of the most complex issues is automated respiratory sound classification. In this research, five Hybrid Interpretable Strategies with Ensemble Techniques (HISET) which are quite interesting and robust are proposed for the purpose of respiratory sounds classification. The first approach is termed as an Ensemble GSSR technique which utilizes Granger Analysis and the proposed Supportive Ensemble Empirical Mode Decomposition (SEEMD) technique and then Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is used for feature selection and followed by classification with Machine Learning (ML) classifiers. The second approach proposed is the implementation of a novel Realm Revamping Sparse Representation Classification (RR-SRC) technique and third approach proposed is a Distance Metric dependent Variational Mode Decomposition (DM-VMD) with Extreme Learning Machine (ELM) classification process. The fourth approach proposed is with the usage of Harris Hawks Optimization (HHO) with a Scaling Factor based Pliable Differential Evolution (SFPDE) algorithm termed as HHO-SFPDE and it is classified with ML classifiers. The fifth or the final approach proposed analyzes the application of dimensionality reduction techniques with the proposed Gray Wolf Optimization based Support Vector Classification (GWO-SVC) and another parallel approach utilizes a similar kind of analysis with the Grasshopper Optimization Algorithm (GOA) based Sparse Autoencoder. The results are examined for ICBHI dataset and the best results are shown for the 2-class classification when the analysis is carried out with Manhattan distance-based VMD-ELM reporting an accuracy of 95.39%, and for 3-class classification Euclidean distance-based VMD-ELM reported an accuracy of 90.61% and for 4-class classification, Manhattan distance-based VMD-ELM reported an accuracy of 89.27%.
人类呼吸系统会受到多种疾病影响，且与独特声音相关。对于先进的生物医学信号处理而言，最复杂的问题之一就是自动呼吸音分类。在本研究中，为了进行呼吸音分类，提出了五种兼具趣味性和稳健性的混合可解释策略与集成技术（HISET）。第一种方法被称为集成广义自回归条件异方差（GSSR）技术，它利用格兰杰分析和所提出的支持性集成经验模态分解（SEEMD）技术，然后使用基于支持向量机的递归特征消除（SVM-RFE）进行特征选择，接着用机器学习（ML）分类器进行分类。所提出的第二种方法是实施一种新颖的领域改进稀疏表示分类（RR-SRC）技术，第三种方法是基于距离度量的变分模态分解（DM-VMD）与极限学习机（ELM）分类过程。所提出的第四种方法是使用带有基于缩放因子的灵活差分进化（SFPDE）算法的哈里斯鹰优化（HHO），即HHO-SFPDE，并使用ML分类器进行分类。所提出的第五种也是最后一种方法分析了降维技术与所提出的基于灰狼优化的支持向量分类（GWO-SVC）的应用，另一种并行方法利用基于蚱蜢优化算法（GOA）的稀疏自动编码器进行类似分析。对国际生物医学和健康信息学大会（ICBHI）数据集的结果进行了检验，当使用基于曼哈顿距离的VMD-ELM进行分析时，2类分类显示出最佳结果，准确率为95.39%；对于3类分类，基于欧几里得距离的VMD-ELM报告的准确率为90.61%；对于4类分类，基于曼哈顿距离的VMD-ELM报告的准确率为89.27%。
