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Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks

Artikel i vetenskaplig tidskrift
Författare Charalambos Themistocleous
Marie Eckerström
Dimitrios Kokkinakis
Publicerad i Frontiers in Neurology
Volym 9
ISSN 1664-2295
Publiceringsår 2018
Publicerad vid Institutionen för svenska språket
Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi
Språk en
Länkar https://doi.org/10.3389/fneur.2018....
https://www.frontiersin.org/article...
Ämnesord Mild Cognitive Impairment, Artificial Neural Networks, Speech
Ämneskategorier Datorlingvistik, Lingvistik, Neurovetenskaper

Sammanfattning

While people with mild cognitive impairment (MCI) portray noticeably incipient memory difficulty in remembering events and situations along with problems in decision making, planning, and finding their way in familiar environments, detailed neuropsychological assessments also indicate deficits in language performance. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI; thus, the development of valid tools for identifying early cognitive changes is of great importance. In this study, we provide an automated machine learning method, using Deep Neural Network Architectures, that aims to identify MCI. Speech materials were obtained using a reading task during evaluation sessions, as part of the Gothenburg MCI research study. Measures of vowel duration, vowel formants (F1 to F5), and fundamental frequency were calculated from speech signals. To learn the acoustic characteristics associated with MCI vs. healthy controls, we have trained and evaluated ten Deep Neural Network Architectures and measured how accurately they can diagnose participants that are unknown to the model. We evaluated the models using two evaluation tasks: a 5-fold crossvalidation and by splitting the data into 90% training and 10% evaluation set. The findings suggest first, that the acoustic features provide significant information for the identification of MCI; second, the best Deep Neural Network Architectures can classify MCI and healthy controls with high classification accuracy (M = 83%); and third, the model has the potential to offer higher accuracy than 84% if trained with more data (cf., SD≈15%). The Deep Neural Network Architecture proposed here constitutes a method that contributes to the early diagnosis of cognitive decline, quantify the progression of the condition, and enable suitable therapeutics.

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