MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary.
Department of Psychiatry, University of Szeged, Szeged, Hungary.
Clin Linguist Phon. 2021 Aug 3;35(8):727-742. doi: 10.1080/02699206.2020.1827043. Epub 2020 Sep 30.
This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software. The temporal speech-related parameters successfully differentiate MCI from mAD and C, such as speech rate, number and length of pauses, the rate of pause and signal. Parameters pauses/duration and silent pauses/duration linearly decreased among the groups, in other words, the percentage of pauses in the total duration of speech continuously grows as dementia progresses. Thus, the proposed approach may be an effective tool for screening MCI and mAD.
本研究提出了一种新的方法，用于早期检测老年人的轻度认知障碍（MCI）和轻度阿尔茨海默病（mAD）。参与者包括 25 名老年对照组（C）、25 名临床诊断为 MCI 和 25 名 mAD 患者，这些患者经过 CT 或 MRI 和认知测试的临床诊断后被纳入研究。我们的语言协议包括三个连接的言语任务，这些任务刺激了不同的记忆系统，然后使用 PRAAT 软件进行语言分析。言语相关的时间参数，如语速、停顿次数和长度、停顿率和信号率，成功地区分了 MCI 和 mAD 与 C，参数停顿/持续时间和无声停顿/持续时间在各组之间线性下降，换句话说，随着痴呆症的进展，言语总持续时间中的停顿百分比不断增加。因此，所提出的方法可能是筛选 MCI 和 mAD 的有效工具。
