Hall Aidan O, Shinkawa Kaoru, Kosugi Akihiro, Takase Toshiro, Kobayashi Masatomo, Nishimura Masafumi, Nemoto Miyuki, Watanabe Ryohei, Tsukada Eriko, Ota Miho, Higashi Shinji, Nemoto Kiyotaka, Arai Tetsuaki, Yamada Yasunori
IBM Research, Tokyo, Japan.
Pitzer College, CA, USA.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:34-43. eCollection 2019.
Early detection of dementia as well as improvement in diagnosis coverage has been increasingly important. Previous studies involved extracting speech features during neuropsychological assessments by humans, such as medical pro- fessionals, and succeeded in detecting patients with dementia and mild cognitive impairment (MCI). Enabling such assessment in an automated fashion by using computer devices would extend the range of application. In this study, we developed a tablet-based application for neuropsychological assessments and collected speech data from 44 Japanese native speakers including healthy controls (HCs) and those with MCI and dementia. We first extracted acoustic and phonetic features and showed that several features exhibited significant difference between HC vs. MCI and HC vs. dementia. We then constructed classification models by using these features and demonstrated that these models could differentiate MCI and dementia from HC with up to 82.4 and 92.6% accuracy, respectively.
痴呆症的早期检测以及诊断覆盖率的提高变得越来越重要。先前的研究涉及由医学专业人员等人类在神经心理学评估期间提取语音特征，并成功检测出痴呆症和轻度认知障碍（MCI）患者。通过使用计算机设备以自动化方式进行此类评估将扩大应用范围。在本研究中，我们开发了一种基于平板电脑的神经心理学评估应用程序，并从44名以日语为母语的人中收集了语音数据，包括健康对照者（HCs）以及患有MCI和痴呆症的人。我们首先提取了声学和语音特征，并表明几个特征在HC与MCI以及HC与痴呆症之间存在显著差异。然后，我们使用这些特征构建了分类模型，并证明这些模型能够分别以高达82.4%和92.6%的准确率将MCI和痴呆症与HC区分开来。
