王荣国,高洁,宋晓飞,屈永涛.机器学习模型k近邻算法分析脑电图对主观性耳鸣的诊断价值.[J].中南医学科学杂志.,2023,(5):696-698.
机器学习模型k近邻算法分析脑电图对主观性耳鸣的诊断价值
The diagnostic value of machine learning model k-nearest neighbor algorithm to analyze EEG for subjective tinnitus
投稿时间:2022-09-23  修订日期:2023-08-20
DOI:10.15972/j.cnki.43-1509/r.2023.05.017
中文关键词:  k近邻算法  脑电图  主观性耳鸣  样本熵  小波包变换 [
英文关键词:k nearest neighbor algorithm  EEG  subjective tinnitus  sample entropy  wavelet packet transform
基金项目:河北省卫生健康委员会项目(20210800)
作者单位E-mail
王荣国 河北省人民医院耳鼻喉科,河北石家庄 050000 e-mail为wangrongguo1976@126.com 
高洁 河北省人民医院耳鼻喉科,河北石家庄 050000  
宋晓飞 河北省人民医院耳鼻喉科,河北石家庄 050000  
屈永涛 河北省人民医院耳鼻喉科,河北石家庄 050000  
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中文摘要:
      目的探讨机器学习模型k近邻算法分析脑电图对主观性耳鸣的诊断价值。 方法纳入主观性耳鸣患者87例(耳鸣组),健康受试者91例(对照组)。使用MATLAB和EEGLAB工具箱、小波包变换和样本熵相结合的方法分析两组δ、θ、α1、α2、β1、β2、β3、γ频段在耳鸣发生网络相关7个区域的样本熵差异。对耳鸣脑电图特征数据使用Python的scikit-learn包进行k近邻算法分析,使用准确率、召回率、精确度和F1得分评估k近邻算法对主观性耳鸣的诊断价值。 结果两组样本熵在左听觉、左额叶、中央、右顶叶和左顶叶等区域差异有显著性(P<0.05)。耳鸣组δ、α2和β1节律平均熵大于对照组,θ、α1、β2、β3和γ节律平均熵小于对照组(P<0.05)。耳鸣组和对照组样本熵在FC5、C1、CP1和P4单通道中差异有显著性(P<0.05)。k近邻算法对主观性耳鸣的诊断准确率为91.98%,召回率为90.24%,准确率为96.28%,F1得分为93.12%。 结论机器学习模型k近邻算法分析脑电图结果可以辅助临床医生对耳鸣进行诊断。
英文摘要:
      AimTo evaluate the diagnostic value of machine learning model k-nearest neighbor algorithm in analyzing EEG for subjective tinnitus. Methods87 subjective tinnitus patients (tinnitus group) and 91 healthy subjects (control group) were included. A combination of MATLAB and EEGLAB toolboxes, wavelet packet transform, and sample entropy were used to analyze sample entropy differences of δ, θ, α1, α2, β1, β2, β3, γ frequency band in 7 regions related to tinnitus occurrence network. The characteristic data of tinnitus electroencephalogram were analyzed by using Python's scikit-learn package for k-neighbor algorithm analysis, and analyzing accuracy, recall, accuracy, and F1 score for subjective tinnitus by using k-neighbor algorithm to evaluate the diagnostic value. ResultsThere was a significant difference in entropy between the two groups of samples in left auditory, left frontal, central, right parietal, and left parietal lobes (P<0.05). Tinnitus group δ, α 2 and β1 average entropy of rhythm was greater than that of the control group, and θ, α1, β2, β3 and γ average entropy of the rhythm was lower than that of the control group (P<0.05). There was a significant difference in sample entropy between the tinnitus group and the control group in FC5, C1, CP1, and P4 single channels (P<0.05). The k-nearest neighbor algorithm has a diagnostic accuracy of 91.98%, a recall rate of 90.24%, an accuracy rate of 96.28%, and an F1 score of 93.12% for subjective tinnitus. ConclusionMachine learning model k-nearest neighbor algorithm analysis of EEG can assist clinical doctors in diagnosing tinnitus.
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