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首页> 外文会议>International Conference on Inventive Computing and Informatics >Alcoholism detection using support vector machines and centered correntropy features of brain EEG signals
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Alcoholism detection using support vector machines and centered correntropy features of brain EEG signals

机译：使用支持向量机和脑EEG信号的居中的酗酒检测

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The regular excessive consumption of alcohol may leads to alcohol use disorder (AUD). AUD is considered a serious health issue which may hamper physical health and social life of a patient if not detected and treated timely. The screening of AUD patients using physiological characteristics is very difficult task for the doctors. Therefore, brain electroencephalograms (EEGs) signal analysis is popularly used to accurately detect the AUD disorder (or the person is alcoholic or normal). Manual analyses of EEG signals are complicated and time-consuming as it is recorded in microvolt (μv). Therefore, computer-aided diagnosis (CAD) is used by a neurologist to analyze the EEG signals from their frequency sub-bands. The EEG signals recording obtained from the subject are nonlinear and unstable with respect to time. In this paper, support vector machine techniques are used with nonlinear parametric signals in time-frequency domain on features extracted from EEG signals. The features are extracted with the help of continuous wavelet transform, Tuned Q wavelet transform (TQWT) is used for decomposition of the signals. The decomposed subband, Centered Correntropy (CC) features are extracted and used to find out the minute changes in the nonlinear signal with lag time delay, which is very similar to the autocorrelation of the signal. Then these features are reduced by applying principal component analysis (PCA), which is then passed to least squares support vector machine (LS-SVM) for classification between alcoholic and normal EEG signals. Training of the data is done with ten-fold cross-validation to increase the accuracy. Our proposed work is with three different types of kernel functions like linear, RBF and Polynomial kernel. Comparing the result, we found that the RBF kernel gives the highest performance having accuracy is 97.06%, sensitivity is 97.45%, specificity is 97.666%, and Matthews's correlation coefficient is 0.94 by varying the Q parameter of wavelet transform from 3 to 8.
机译：醇的定期过度消耗可能导致酒精使用障碍（AUD）。 DUA被认为是一个严重的健康问题，如果没有检测到并及时治疗，可能会妨碍患者的身体健康和社会生活。使用生理特征的AUD患者的筛查对于医生来说是非常困难的任务。因此，脑脑电图（EEGS）信号分析普遍用于准确检测AUD疾病（或者人是酗酒或正常）。手动分析EEG信号是复杂且耗时的，因为它被记录在微伏（μV）中。因此，神经科医生使用计算机辅助诊断（CAD）来分析来自其频率子带的EEG信号。从受试者获得的EEG信号记录是非线性的，并且相对于时间不稳定。在本文中，支持向量机技术与时频域中的非线性参数信号一起使用从EEG信号中提取的特征中的非线性参数信号。在连续小波变换的帮助下提取特征，调谐Q小波变换（TQWT）用于分解信号。分解的子带以居中的控制（CC）特征提取并用于在滞后时间延迟中找出非线性信号中的微小变化，这与信号的自相关非常相似。然后通过应用主成分分析（PCA）来减少这些特征，然后将其传递给最小二乘支持向量机（LS-SVM），用于饮酒和正常EEG信号之间的分类。数据培训是以十倍的交叉验证完成的，以提高准确性。我们所提出的工作是线性，RBF和多项式内核等三种不同类型的内核功能。比较结果，我们发现RBF内核给出了精度的最高性能为97.06 ％，灵敏度为97.45 ％，特异性为97.666 ％，并且Matthews的相关系数通过从3到3的小波变换的Q参数改变0.94 8。

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