Objective : There has been a constant increase in life expectancy with the advancement of modern medicine. Likewise, dementia has also increased and projected to elevate in the coming decades with the higher expenditure on healthcare. Consequently, it is essential to identify early dementia, e.g., a patient suffering from mild cognitive impairment who is highly vulnerable to developing dementia soon. Methods : Through this work, we brought forward an approach by fusing cognitive task and EEG signal processing. Continuous EEG of 16 dementia, 16 early dementia and 15 healthy subjects recorded under two resting states; eye open and eye closed, and two cognitive states; finger tapping test (FTT) and the continuous performance test (CPT). The present approach introduced iterative filtering (IF) as a decomposition technique for dementia diagnosis along with four significant EEG features power spectral density, variance, fractal dimension and Tsallis entropy. Multi-class classification conducted to compare the decision tree, k nearest neighbour ( ${k}$ NN), support vector machine, and ensemble classifiers. Results : The proposed approach deeply checked for their capability of prediction using cognitive scores and EEG measures. The highest accuracies obtained by ${k}$ NN with 10-fold cross-validation for dementia, early dementia and healthy are 92.00%, 91.67% and 91.87%, respectively. Conclusion : The essential findings of this study are: 1) Experimental results indicate that ${k}$ NN is superior over other classifier algorithms for dementia diagnosis. 2) CPT is the best predictor for healthy subjects. 3) FTT can be an essential test to diagnose significant dementia. Significance : IF decomposition technique enhances the diagnostic accuracy even with a limited dataset.

中文翻译：

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基于迭代过滤分解的早期痴呆诊断与脑电图测试

目的 随着现代医学的发展，人们的预期寿命不断增加。同样，随着医疗保健支出的增加，痴呆症也将增加，并预计在未来几十年中会升高。因此，至关重要的是要识别早期的痴呆症，例如患有轻度认知障碍的患者，他们很快就容易患上痴呆症。方法 ：通过这项工作，我们提出了一种融合认知任务和脑电信号处理的方法。在两个静止状态下记录的16名痴呆，16名早期痴呆和15名健康受试者的连续脑电图；睁眼和闭眼，以及两种认知状态；手指敲击测试（FTT）和连续性能测试（CPT）。本方法引入了迭代过滤（IF）作为痴呆诊断的分解技术，并具有四个重要的EEG特征，即功率谱密度，方差，分形维数和Tsallis熵。进行多类分类以比较决策树，k最近邻居（ $ {k} $ NN），支持向量机和集成分类器。 结果 ：提出的方法使用认知评分和脑电图测量方法深度检查了其预测能力。获得的最高精确度 $ {k} $ 对痴呆症，早期痴呆症和健康痴呆症进行十倍交叉验证的NN分别为92.00％，91.67％和91.87％。 结论 ：这项研究的基本发现是：1）实验结果表明 $ {k} $ NN在痴呆症诊断方面优于其他分类器算法。2）CPT是健康受试者的最佳预测指标。3）FTT可能是诊断重大痴呆症的一项基本检查。 意义 ：IF分解技术即使在数据集有限的情况下也可以提高诊断准确性。