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Frontiers in Neurology


Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurol. | doi: 10.3389/fneur.2018.00975

Identification of Mild Cognitive Impairment from Speech in Swedish using Deep Sequential Neural Networks

  • 1Johns Hopkins Medicine, United States
  • 2University of Gothenburg, Sweden

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., $SD \approx 15\%$). 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.

Keywords: speech production, Deep neural network, Dementia, MCI (mild cognitive impairment), Vowel acoustics

Received: 05 Jul 2018; Accepted: 29 Oct 2018.

Edited by:

Stefano L. Sensi, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy

Reviewed by:

Reinhold Scherer, Graz University of Technology, Austria
Noemi Massetti, Centro Studi sull'Invecchiamento, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy  

Copyright: © 2018 Themistocleous, Eckerström and Kokkinakis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Charalambos Themistocleous, Johns Hopkins Medicine, Baltimore, United States,