Background

The identification of alcoholism is of prime importance because of its adverse effects on the central nervous system. Moreover, people suffering from alcoholism are susceptible to various health problems such as cardiomyopathy, immune system disorder, high blood pressure, cirrhosis, brain anomalies, and heart problems.

New method

This study presents a novel approach, based on Fourier theory, known as Fourier decomposition method (FDM) for automatic identification of alcoholism using electroencephalogram (EEG) signals. The FDM approach is employed to decompose EEG signals into a set of desired orthogonal components, commonly referred as Fourier intrinsic band functions (FIBFs), obtained by dividing the complete bandwidth of EEG signal under analysis into equal frequency bands. Time-domain features such as Hjorth parameters, kurtosis, inter-quartile range, and median frequency are extracted from FIBFs. To reduce the complexity, Kruskal–Wallis (KW) statistical test, is performed to adopt the most significant features.

Results

Simulation results are obtained using different classification methods, namely, k-nearest neighbor (kNN), support vector machine (SVM), and linear discriminant analysis (LDA). The proposed approach with the SVM classifier using radial basis function provides average accuracy of 99.98%, sensitivity of 99.99% and specificity of 99.97%. Performance is also tested in the presence of noise.

Comparison with existing method(s)

Classification results highlight the superior performance of our method in comparison to existing works.

Conclusions

The proposed scheme provides an efficient approach and can be employed in real-time alcoholism detection.

中文翻译：

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一种使用傅里叶分解法自动检测酒精中毒的新方法。

背景

酒精中毒的鉴定是最重要的，因为它对中枢神经系统有不良影响。此外，患有酒精中毒的人易患各种健康问题，例如心肌病，免疫系统疾病，高血压，肝硬化，脑部异常和心脏问题。

新方法

这项研究提出了一种基于傅立叶理论的新颖方法，称为傅立叶分解方法（FDM），可使用脑电图（EEG）信号自动识别酒精中毒。FDM方法用于将EEG信号分解为一组所需的正交分量，通常称为傅立叶固有带函数（FIBF），该正交分量是通过将正在分析的EEG信号的整个带宽划分为相等的频带而获得的。从FIBF中提取了时域特征，例如Hjorth参数，峰度，四分位间距和中位数频率。为了降低复杂性，执行了Kruskal-Wallis（KW）统计测试以采用最重要的功能。

结果

仿真结果是使用不同的分类方法获得的，即k最近邻（kNN），支持向量机（SVM）和线性判别分析（LDA）。带有径向基函数的SVM分类器的拟议方法可提供99.98％的平均准确度，99.99％的灵敏度和99.97％的特异性。在噪声的情况下也对性能进行了测试。

与现有方法的比较

分类结果突出了我们的方法与现有作品相比的优越性能。

结论

提出的方案提供了一种有效的方法，可用于实时酒精中毒检测。