@inProceedings{Björkner-Eva2017-256522, title = {Voice acoustic parameters for detecting signs of early cognitive impairment}, abstract = {Aiding the detection of very early cognitive impairment in Alzheimer's disease (AD) and assessing the disease progression are essential foundations for effective psychological assessment, diagnosis and planning. Efficient tools for routine dementia screening in primary health care, particularly non-invasive and cost-effective methods, are desirable. The aim of this study is to find out if voice acoustic analysis can be a useful tool for detecting signs of early cognitive impairment.}, booktitle = {PEVOC (PanEuropean Voice Conference) 12, August 30th - September 1st 2017, Ghent, Belgium}, author = {Björkner, Eva and Lundholm Fors, Kristina and Kokkinakis, Dimitrios and Nordlund, Arto}, year = {2017}, } @inProceedings{Kokkinakis-Dimitrios2017-256955, title = {Data Collection from Persons with Mild Forms of Cognitive Impairment and Healthy Controls - Infrastructure for Classification and Prediction of Dementia}, abstract = {Cognitive and mental deterioration, such as difficulties with memory and language, are some of the typical phenotypes for most neurodegenerative diseases including Alzheimer’s disease and other dementia forms. This paper describes the first phases of a project that aims at collecting various types of cognitive data, acquired from human subjects in order to study relationships among linguistic and extra-linguistic observations. The project’s aim is to identify, extract, process, correlate, evaluate, and disseminate various linguistic phenotypes and measurements and thus contribute with complementary knowledge in early diagnosis, monitor progression, or predict individuals at risk. In the near future, automatic analysis of these data will be used to extract various types of features for training, testing and evaluating automatic classifiers that could be used to differentiate individuals with mild symptoms of cognitive impairment from healthy, age-matched controls and identify possible indicators for the early detection of mild forms of cognitive impairment. Features will be extracted from audio recordings (speech signal), the transcription of the audio signals (text) and the raw eye-tracking data.}, booktitle = {Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden}, author = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Björkner, Eva and Nordlund, Arto}, year = {2017}, publisher = {Linköping University Electronic Press, Linköpings universitet}, adress = {Linköping}, ISBN = {978-91-7685-601-7}, } @inProceedings{Fraser-Kathleen2017-257840, title = {An analysis of eye-movements during reading for the detection of mild cognitive impairment}, abstract = {We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy.}, booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing}, author = {Fraser, Kathleen and Lundholm Fors, Kristina and Kokkinakis, Dimitrios and Nordlund, Arto}, year = {2017}, }