@inProceedings{Kokkinakis-Dimitrios2018-262851, title = {A Swedish Cookie-Theft Corpus}, abstract = {Language disturbances can be a diagnostic marker for neurodegenerative diseases, such as Alzheimer’s disease, at earlier stages, and connected speech analysis provides a non-invasive and easy-to-assess measure for determining aspects of the severity of language impairment. In this paper we focus on the development of a corpus consisting of audio recordings of picture descriptions of the Cookie-theft, produced by Swedish speakers, and accompanying transcriptions. The speech elicitation procedure provides an established method of obtaining highly constrained samples of connected speech that can allow us to study the intricate interactions between various linguistic levels and cognition. We chose the Cookie-theft picture since it is a standardized test that has been used in various studies in the past, and therefore comparisons can be made based on previous results. This type of picture description task might be useful for detecting subtle language deficits in patients with subjective and mild cognitive impairment. The resulting corpus is a new, rich and multi-faceted resource for the investigation of linguistic characteristics of connected speech and a unique data set that provides a rich resource for (future) research and experimentation in many areas, and of language impairment in particular. The information in the corpus can also be combined and correlated with other collected data about the speakers, such as neuropsychological tests, imaging and brain physiology markers and cerebrospinal fluid markers.}, booktitle = {LREC 2018, 11th edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan) / Editors: Nicoletta Calzolari (Conference chair), Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga}, author = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Fraser, Kathleen and Nordlund, Arto}, year = {2018}, publisher = {European Language Resources Association}, ISBN = {979-10-95546-00-9}, } @inProceedings{LundholmFors-Kristina2018-263790, title = {Automated Syntactic Analysis of Language Abilities in Persons with Mild and Subjective Cognitive Impairment}, abstract = {In this work we analyze the syntactic complexity of transcribed picture descriptions using a variety of automated syntactic features, and investigate the features’ predictive power in classifying narratives from people with subjective and mild cognitive impairment and healthy controls. Our results indicate that while there are no statistically significant differences, syntactic features can still be moderately successful at distinguishing the participant groups when used in a machine learning framework.}, booktitle = {Building continents of knowledge in oceans of data : the future of co-created eHealth: proceedings of MIE2018, 24-26 April 2018, Gothenburg, Sweden / edited by Adrien Ugon, Daniel Karlsson, Gunnar O. Klein and Anne Moen.}, author = {Lundholm Fors, Kristina and Fraser, Kathleen and Kokkinakis, Dimitrios}, year = {2018}, publisher = {IOS Press}, adress = {Amsterdam}, ISBN = {978-1-61499-851-8}, } @inProceedings{LundholmFors-Kristina2018-264400, title = {Eye-voice span in adults with mild cognitive impairment (MCI) and healthy controls. }, abstract = {Objectives: This study is part of a larger project focused on developing new techniques for identification of early linguistic and extra-linguistic signs of cognitive impairment, with the overall goal of identifying dementia in the preclinical stage. In a previous study, we found that eye movements during reading can be used to distinguish between subjects with mild cognitive impairment (MCI) and healthy controls with up to 86% accuracy. In this study, we are investigating the process of reading aloud, by exploring the eye-voice span in subjects with and without cognitive impairment. The aim of the study is to identify differences in the reading processes and evaluate whether these differences can be used to discriminate between the two groups. Methods: The eye-voice span is a measurement of the temporal and spatial organization between the eye and the voice, and is affected by for example working memory and automaticity, but also by the familiarity and length of words. In previous work, differences between eye movements when reading in healthy controls and subjects with cognitive impairments have been identified, and it has been shown that subjects with Alzheimer’s disease show impairments when reading aloud, specifically with regards to speech and articulation rate. Results: We present a quantitative and qualitative analysis of the reading process in the subjects, focusing both on general measures of eye-voice span, but also specifically on instances of hesitation and mistakes in the speech, and the correlated eye movements. Conclusions/Take home message: Early detection of dementia is important for a number of reasons, such as giving the person access to interventions and medications, and allowing the individual and families time to prepare. By expanding the knowledge about reading processes in subjects with MCI, we are adding to the potential of using reading analysis as an avenue of detecting early signs of dementia.}, booktitle = {Book of Abstracts 10th CPLOL Congress 10-12 May 2018, Cascais, Portugal / editor : Trinite, Baiba }, author = {Lundholm Fors, Kristina and Fraser, Kathleen and Kokkinakis, Dimitrios}, year = {2018}, } @inProceedings{Fraser-Kathleen2018-264397, title = {Improving the Sensitivity and Specificity of MCI Screening with Linguistic Information.}, abstract = {The Mini-Mental State Exam (MMSE) is a screening tool for cognitive impairment. It has been extensively validated and is widely used, but has been criticized as not being effective in detecting mild cognitive impairment (MCI). In this study, we examine the utility of augmenting MMSE scores with automatically extracted linguistic information from a narrative speech task to better differentiate between individuals with MCI and healthy controls in a Swedish population. We find that with the addition of just four linguistic features, the F score (measuring a trade-off between sensitivity and specificity) is improved from 0.67 to 0.81 in logistic regression classification. These preliminary results suggest that the accuracy of traditional screening tools may be improved through the addition of computerized language analysis.}, booktitle = {Proceedings of the LREC workshop: Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments (RaPID-2). 8th of May 2018, Miyazaki, Japan / Dimitrios Kokkinakis (ed.)}, author = {Fraser, Kathleen and Lundholm Fors, Kristina and Eckerström, Marie and Themistocleous, Charalambos and Kokkinakis, Dimitrios}, year = {2018}, ISBN = {979-10-95546-26-9}, } @inProceedings{Kokkinakis-Dimitrios2018-265113, title = {Kan textforskning bidra till tidigare och säkrare demensdiagnostik?}, abstract = {Tidigare forskning har visat att subtila språkstörningar kan finnas vid de tidigaste förstadierna till demens, flera år innan en klinisk diagnos kan ställas. Inom ramen för projektet ”Språkliga och extra-lingvistiska parametrar för tidig upptäckt av kognitiv svikt” (finansierat av Riksbankens Jubileumsutlysning, 2016-19) undersöker vi med hjälp av språkteknologi och språkanalysstudier hur dessa språkstörningar yttrar sig. Kan språkteknologi användas för att upptäcka dessa tidiga språkrelaterade symtom och därmed bidra med nyanserad, komplementär och användbar kunskap? Kan användning av språkteknologi särskilja personer med de allra tidigaste kognitiva avvikelserna från personer med mer godartad, åldersrelaterad kognitiv svikt? Vilka språkliga förmågor drabbas? Hur yttrar sig dessa förändringar och vilka slags empiriska material finns att tillgå? Dessa är några av de frågor vi söker svar på. Vi gör inspelningar som vi analyserar för att kunna ta fram ny kunskap om subtila språkliga kännetecken som kan föregå demensutveckling. Denna kunskap kan användas för att eventuellt kunna förutsäga vilka individer som befinner sig i riskzonen för att utveckla demens, och kan vara användbar som komplementerande beslutsunderlag till domänexperter. Vi utvinner, analyserar och undersöker om det finns samband mellan olika språkrelaterade parametrar från spontan talinteraktion, transkriptioner men även ögonrörelser och neuropsykologiska tester från personer med subjektiv eller lindrig kognitiv nedsättning och friska kontrollpersoner. Många gånger är det svårt att avgöra huruvida lindriga kognitiva symtom är en del av det normala åldrandet eller början på en neurodegenerativ process. Vi förväntar oss inte heller att varje enskild person med kognitiv nedsättning kommer att uttrycka sig eller läsa på samma sätt utan snarare att dessa personer tidigt i sjukdomsförloppet kommer att börja uppvisa olika slags avvikande läsmönster, eller göra fonologiska, lexikala, syntaktiska eller semantiska fel. I studien utvecklar vi verktyg för att automatiskt hitta dessa avvikelser, och målet är att detta sedan ska kunna användas som komplement till tidig diagnostik samt som prognostiskt eller screeningverktyg. Deltagarna i vår studie har rekryterats från en pågående longitudinell studie, ”Demens i Tidigt Skede”, (eng. ”The Gothenburg MCI study”) på Minnesmottagningen i Göteborg, och vårt projekt har godkänts av den lokala etiknämnden. Alla deltagare i studien (kontrollgruppen [HC], personer med subjektiv kognitiv nedsättning [SCI] och personer med mild kognitiv nedsättning [MCI]) har genomgått baslinjeundersökning och gett informerat skriftligt samtycke (demografisk information finns i tabell 1). Vårt projekt är f.n. pågående och vi kommer presentera resultat baserade på inspelningstillfälle nr ett (aug. 2016-mars 2017). En ny inspelningsomgång, med samma deltagare, började i februari 2018 och förväntas vara avslutat i december 2018. Under presentationen kommer vi ge exempel på olika tal-, text- och ögonrörelseanalyser vi har genomfört och diskutera metodval och resultat baserade på studiens första fas. Vi kommer vidare ge en kort inblick i den nya, pågående inspelningsomgången och de nya testmoment vi använder. Vi vill med vårt arbete visa hur språkteknologisk analys kan bidra till att utöka vår kunskap inom området så att den kan vara användbar för tidig diagnostik och optimal omvårdnad. Enligt Socialstyrelsen (2017) finns det i Sverige över 160 000 personer med någon demenssjukdom. Våra resultat kan ha en betydelse för vårdpersonal som snabbare vill diagnostisera och identifiera individer med olika former av kognitiv funktionsnedsättning innan allvarliga symtom blir påtagliga. Utvecklingsmöjligheterna är många: nya eller förbättrade kognitiva screeningtester som skulle kunna användas inom primär- och specialistvården, samt utveckling och tillämpning av insatser som kan påverka beteendemönster och träna upp individens kommunikativa förmåga, kan på sikt leda till positiva konsekvenser som minskade vårdköer samt effektivare behandling avseende kostnader och behandlingsutfall.}, booktitle = {Forum för textforskning 13 , Lund 7 – 8 juni 2018}, author = {Kokkinakis, Dimitrios and Lundholm Fors, Kristina and Eckerström, Marie and Themistocleous, Charalambos}, year = {2018}, } @inProceedings{Themistocleous-Charalambos2018-265112, title = {THEMIS-SV: Automatic classification of language disorders from speech signals}, abstract = {Background and Aims: Brain injuries resulting from stroke can affect the production of speech resulting in different types of language impairments, such as aphasia. Studying these productions manually is an extremely cumbersome and time consuming process. The aim of this paper is to present THEMIS-SV: a system that enables the automatic transcription of speech signals and the segmentation of vowels and consonants in Swedish. Method: The input of the system are recordings of speech. The system processes the recordings and returns an output with three tiers: the utterance tier, the word tier, and the vowels/consonants tier. Results: The output of the system is a fast and reliable transcription and segmentation of speech, which is very close to transcriptions and segmentations performed manually. The automatic segmentation of speech enables targeted acoustic measurements, such as measurements of consonant spectra, formant frequencies of vowels, fundamental frequency, pauses, speech rate, etc. and other acoustic measurements that have been known to differentiate between the different types of language disorders. Conclusion: The method proposed here can be employed for the analysis of speech of individuals with post-stroke aphasia and other language disorders and constitutes a promising step towards a fully automated differential diagnostic tool for language disorders. }, booktitle = {Abstracts of the 4th European Stroke Organisation Conference ​(ESOC 2018). Gothenburg, Sweden, 16-18 May, 2018. }, author = {Themistocleous, Charalambos and Kokkinakis, Dimitrios}, year = {2018}, } @misc{Kokkinakis-Dimitrios2018-265118, title = {Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments (RaPID-2)}, abstract = {Proceedings of the second RaPID: "Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments". An LREC workshop. 8th of May 2018, Miyazaki, Japan}, author = {Kokkinakis, Dimitrios}, year = {2018}, ISBN = {979-10-95546-26-9}, } @inProceedings{Themistocleous-Charalambos2018-265821, title = {A classification study of speech productions produced by healthy speakers and speakers with Mild Cognitive Impairment using Deep Sequential Neural Networks}, abstract = {Mild cognitive impairment (MCI) is a neurological condition, which is characterized by a noticeable decline of cognitive abilities, including communicative and linguistic skills. Nevertheless, evidence from speech production has been inconsistent with respect to features and factors that are most affected. This study employs speech properties from vowels, produced in a reading task by 55 Swedish speakers—30 healthy controls and 25 MCI — and aims to distinguish MCI and healthy productions. The study presents two machine learning classification tasks. The first is a classification of speech productions as MCI or healthy and the second is a classification of speakers as MCI or healthy. The study evaluates several Deep Neural Network Architectures that resulted in high classification accuracy of MCI and healthy speech productions and MCI and healthy speakers. The proposed neural models can be employed in methods of early detection of cognitive decline in order to quantify the progression of the disease and to provide suitable therapeutics. }, booktitle = {The Sixth IEEE International Conference on Healthcare Informatics (IEEE-ICHI 2018), New York, USA.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, } @inProceedings{Edström-Maria2018-267250, title = {Ageism and Swedish news media}, abstract = {Ageism can be seen as a “social disease”, a casual or systematic prejudice, stereotyping and discriminating against individuals or groups on the basis of their age. This is an area of growing concern, particularly the role of mainstream media in relationship to ageism. A valuable and important step is to understand the presence of ageing and older age how different types of online news media. The main objective of this pilot work is to test, collate and produce evidence from Swedish news media representations of older ages and ageing. METHOD(S) Two pilot studies/experiments; first names and their frequencies of the carriers’ age according to Statistics Sweden (SCB) and their presence in 39 online news between 2015 and 2018. ( 4, 7 millions texts). using general pattern matching techniques with regular expressions and applying them to 13 issues (1994, 2001-13) of Göteborgs-Posten (Swedish news corpora). Definition: Older persons ≥60 years. (25 % of the population in Sweden is over 60 yearsRESULTS AND CONCLUSIONS: Clear and consistent differences of how various age spans are represented in the news. 20-50 year olds is highly over represented compared with the Swedish population, while 0-24 and people over 54 are underrepresented, especially women. Pattern matching exhibits similar characteristics with the exception of obituaries where the elderly mentions are much more frequent.Our pilot studies confirm the introspective view of underrepresentation of old age and older people in or trends can be revealed within a larger time span and synchronic media sources. More studies are required and in the near future we plan to improve, scale and apply our methodology on both synchronic and diachronic data using e.g. available text corpora and try to get a solid perspective on whether any differences or trends can be revealed within a larger time span }, booktitle = {24th Nordic Congress of Gerontoloy (NKG). Oslo, Norway: 2-4 May 2018 }, author = {Edström, Maria and Kokkinakis, Dimitrios and Berggren, Max}, year = {2018}, } @inProceedings{Themistocleous-Charalambos2018-270215, title = {Effects of Mild Cognitive Impairment on vowel duration }, abstract = {Mild cognitive impairment (MCI) is a neurological condition, which is characterized by a noticeable decline of cognitive abilities, including communicative and linguistic skills. In this study, we have measured the duration of vowels produced in a reading task by 55 speakers— 30 healthy controls and 25 MCI—. The main results showed that MCI speakers differed significantly from HC in vowel duration as MCI speakers produced overall longer vowels. Also, we found that gender effects on vowel duration were different in MCI and HC. One significant aspect of this finding is that they highlight the contribution of vowel acoustic features as markers of MCI.}, booktitle = {Proceedings of the 9th Tutorial & Research Workshop on Experimental Linguistics, 28 - 30 August 2018, Paris, France / edited by Antonis Botinis}, author = {Themistocleous, Charalambos and Kokkinakis, Dimitrios and Eckerström, Marie and Fraser, Kathleen and Lundholm Fors, Kristina}, year = {2018}, ISBN = {978-960-466-162-6 }, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, } @article{Themistocleous-Charalambos2018-273026, title = {Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}, abstract = {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.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, volume = {9}, pages = {1--10}, }