Graduate School of Science and Technology, School of Integrated Design Engineering, Keio University, Yokohama 223-8522, Japan.
Department of System Design Engineering, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.
Sensors (Basel). 2020 Jun 26;20(12):3599. doi: 10.3390/s20123599.
Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.
认知能力的丧失通常与痴呆症有关，痴呆症是一大类进行性脑部疾病。然而，重度抑郁症也可能导致认知能力暂时下降，称为假性痴呆。即使是经验丰富的临床医生，区分真正的痴呆症和假性痴呆症仍然很困难，必须进行广泛而仔细的检查。尽管已经研究了精神障碍，如抑郁和痴呆症，但仍然没有针对短暂和简单的假性痴呆症的筛查方法。本研究检查了痴呆症患者和抑郁症患者的分布和统计特征，并进行了比较。结果发现，痴呆症和抑郁症患者存在一些共同的声学特征，尽管它们的相关性相反。在比较这些特征时也发现了统计学意义。此外，还探讨了利用机器学习进行自动假性痴呆症筛查的可能性。机器学习部分包括使用 LASSO 算法进行特征选择和支持向量机（SVM）与线性核作为预测模型，以年龄匹配的有症状抑郁症患者和痴呆症患者为数据库。在训练和测试阶段都获得了较高的准确性、敏感性和特异性。该模型还在未包含的其他数据集上进行了测试，仍然表现得相当好。这些结果表明，仅基于声学特征就可以同时检测和区分痴呆症和抑郁症。基于机器学习结果的高准确性，也可以实现自动筛查。
