Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, India.
Thiruvananthapuram, Kerala, India.
Neurosci Lett. 2021 Nov 20;765:136269. doi: 10.1016/j.neulet.2021.136269. Epub 2021 Sep 25.
Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neuro-pathological condition called encephalopathy. Feature combination is accomplished with the help of feature selection using Gini impurity score that improves discriminative power and keeps redundancy minimal. Further, three widely used non-parametric classifiers which are known to be effective with wavelet features on EEG signals - Support Vector Machine, Random Forest, Multilayer Perceptron - are employed for disease classification. The models created by the combination of aforementioned stages are analysed and evaluated with performance scores, leading to an optimal model for automated diagnostic applications.
脑电图 (EEG) 信号描绘了大脑中隐藏的神经元相互作用，并指示了大脑的动态。这些信号是动态的、复杂的、混沌的和非线性的，其性质可以通过分形维数、熵和混沌特征来表示。本研究旨在检查单个特征及其组合在诊断一种称为脑病的神经病理状况中的区分能力。特征组合是通过使用基尼杂质分数进行特征选择来完成的，这可以提高区分能力并使冗余最小化。此外，还使用了三种广泛使用的非参数分类器，它们在 EEG 信号上的小波特征方面被证明是有效的 - 支持向量机、随机森林、多层感知机 - 用于疾病分类。通过上述阶段的组合创建的模型使用性能分数进行分析和评估，从而为自动化诊断应用程序创建了最佳模型。
