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首页> 外文会议>2017 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：使用支持向量机和大脑EEG信号的中心熵特征进行酒精中毒检测

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摘要

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：经常过量饮酒可能会导致饮酒障碍（AUD）。澳元被认为是严重的健康问题，如果不及时发现和治疗，可能会妨碍患者的身体健康和社会生活。对于医生来说，利用生理特征筛查AUD患者是非常困难的任务。因此，脑电图（EEGs）信号分析广泛用于准确检测AUD疾病（或该人是酒精中毒者或正常人）。手动分析EEG信号非常复杂且耗时，因为它以微伏（μv）记录。因此，神经科医生使用计算机辅助诊断（CAD）来分析其频率子带中的EEG信号。从对象获得的脑电信号记录是非线性的并且相对于时间是不稳定的。在本文中，支持向量机技术用于从EEG信号中提取的特征在时频域中的非线性参数信号。借助连续小波变换提取特征，使用调谐Q小波变换（TQWT）分解信号。提取分解后的子带，中心熵（CC）特征并用于找出具有滞后时间延迟的非线性信号的微小变化，这与信号的自相关非常相似。然后，通过应用主成分分析（PCA）减少这些特征，然后将其传递到最小二乘支持向量机（LS-SVM）进行酒精和正常EEG信号之间的分类。通过十倍交叉验证来完成数据训练，以提高准确性。我们提出的工作是使用三种不同类型的内核函数，例如线性，RBF和多项式内核。比较结果发现，通过改变小波变换的Q参数，RBF内核的性能最高，准确度为97.06％，灵敏度为97.45％，特异性为97.666％，Matthews相关系数为0.94。从3到8

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