Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response–based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

中文翻译：

---

熵：一个有希望的脑电图生物标志物对阿片类药物使用障碍和健康控制的受试者进行二分法

众所周知，脑电图 (EEG) 信号是不稳定的，通常是包含有关大脑状况信息的多分量信号。由于脑电信号具有复杂的、非线性的、非平稳的和高度随机的行为，许多与短时加窗技术相关的线性特征提取方法不能满足更高的分类精度。由于生物信号是高度主观的，症状可能在时间尺度上随机出现，并且脑电信号的非常小的变化可能描述了一种确定类型的大脑异常，因此使用计算机提取和分析脑电信号参数是有价值和重要的。挑战在于设计和开发信号处理算法，提取这些微妙的信息并将其用于诊断、监测、和治疗患有精神障碍的受试者。为此，采用了基于有限脉冲响应的滤波过程，而不是传统的时域和频域方法。进一步分析有限脉冲响应子带以获得不同熵标记的特征向量，并将这些特征输入分类器，即多层感知器。最后比较分类器的性能，考虑整体分类准确度、接受者操作特征曲线下的面积得分。我们的结果强调了引入的方法的潜在好处是有希望的，并且将被视为二分物质使用障碍受试者和其他医学数据分析研究的临床接口。结果还表明，熵估计器可以区分正常和阿片类药物使用障碍受试者。EEG 数据和 theta 频带对几乎所有类型的熵都具有独特的能力，而与其他类型的熵相比，非扩展性 Tsallis 熵的表现优于其他类型的熵。