Kublanov Vladimir S, Dolganov Anton Yu, Belo David, Gamboa Hugo
Research Medical and Biological Engineering Centre of High Technologies, Ural Federal University, Mira 19, Yekaterinburg 620002, Russia.
Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2892-516 Caparica, Portugal.
Appl Bionics Biomech. 2017;2017:5985479. doi: 10.1155/2017/5985479. Epub 2017 Jul 31.
The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, -nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.
本文介绍了应用于心脏活动分析的机器学习方法准确性的结果。该研究通过短期心率变异性信号评估动脉高血压的诊断可能性。研究了两组：30名相对健康的志愿者和40名患有II-III级动脉高血压的患者。研究了以下机器学习方法：线性和二次判别分析、k近邻、径向基支持向量机、决策树和朴素贝叶斯分类器。此外，在该研究中，分析了不同的特征提取方法：统计、频谱、小波和多重分形。总共研究了53个特征。研究结果表明，判别分析实现了最高的分类准确率。所建议的非相关特征集搜索方法比基于主成分的数据集取得了更高的结果。
