Speech in Alzheimer's Disease: Can Temporal and Acoustic Parameters Discriminate Dementia?

Meilán, Juan José García; Sánchez, Francisco Martínez; Carro, Juan; López, Dolores E.; Millian-Morell, Lymarie; Martínez, José María Arana

doi:10.1159/000356726

Cited by 104 publications

(95 citation statements)

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“…The current definition provided by the World Health Organisation (WHO) describes "dementia" as a syndrome -Tadeusz Paweł Wasilewski, Hanna Kachaniuk usually of a chronic or progressive nature -in which there is deterioration in the higher cortical functions, such as memory, thinking, orientation, calculation, learning capacity, language, and judgement. The impairment of cognitive function is commonly accompanied, and occasionally preceded, by deterioration in emotional control, social behaviour, or motivation Grossberg 2009a, 2009b;Meilán et al 2014). …”

Section: Cognitive Deficits In Alzheimer's Diseasementioning

confidence: 99%

“…Moreover, they tend to have problems with word choice, and their utterances become "bizarre" and devoid of content (Boye et From the logopaedic point of view, it needs to be borne in mind that the situation of Alzheimer's patients is very difficult when it comes to their communication problems. Indeed, in this group, speech problems affect people who have previously mastered a linguistic system and achieved full communicative (social, situational and pragmatic) competence (Grabias and Kurkowski 2012; Meilán et al 2014).…”

Section: Communication and Speech Impediments In Alzheimer's Diseasementioning

confidence: 99%

“…Patients typically use substitutions, which involve the use of synonyms and words related semantically to the word the individual has in mind, e.g., part -whole. When it comes to vocabulary, patients tend to use low-frequency words, which usually indicates problems in the case of less severe disorders (Boye et al 2014; Leyton et al 2014;Meilán et al 2014).…”

Section: Communication and Speech Impediments In Alzheimer's Diseasementioning

confidence: 99%

“…In their attempts at communication, patients retain a considerable freedom of linguistic behaviour and construct their speech within the limits of their ability (Boye et al 2014;Catricalà et al 2015;Meilán et al 2014).…”

Section: Linguistic-system-mastery Impedimentmentioning

confidence: 99%

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Speech disturbances in Alzheimer’s disease

Wasilewski¹,

Kachaniuk²

2016

nin

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A b s t r a c tAlzheimer's disease (AD) is the most frequent cause of dementia affecting older people. It is estimated that the illness constitutes about 60-70% of all dementing disorders. It is considered to be a degeneration of the nervous system whose causes and mechanism are still unclear. The disease affects 5-10% of people above the age of 60 and nearly 50% of those above 85. Despite its defined clinical and neuropathological criteria, the aetiology and pathogenesis of the disease remain unclear. In terms of mechanisms that cause the disease to develop, a significant role is played by pathological processes triggered by genetic and environmental factors. The factors which contribute to AD development include old age, female gender, poor education, diabetes, and having a relative who has been diagnosed with the disease. Also, the patients show impaired communicative competence, the occurrence of logomania or reticence, perseveration, vocal paraphrases and finally a decline in reading and writing competence. It should be emphasised that no matter how long the process occurs, it results in a gradual loss of productive and receptive skills. The present article describes the disturbances of language communication and articulatory problems, which are progressive and co-exist with disorders of other higher mental functions.Key words: communication, Alzheimer disease, speech disorders. S t r e s z c z e n i eChoroba Alzheimera jest najczęstszą przyczyną otę-pienia u osób w wieku starszym. Szacuje się, że stanowi 60-70% wszystkich zaburzeń otępiennych. Jest chorobą zwyrodnieniową układu nerwowego o nie do końca poznanej przyczynie i mechanizmach powstawania i dotyczy 5-10% osób powyżej 60. roku życia i ok. 50% osób powyżej 85. roku życia. Choroba Alzheimera jest zaburzeniem neurodegeneracyjnym o stosunkowo szybkim przebiegu, związanym z nieodwracalną utratą neuronów w różnych obszarach mózgu. Powoduje postępujące zmiany w zakresie czynności psychicznych i funkcji neurologicznych, prowadząc w ciągu kilku lat do inwalidztwa psychofizycznego i śmierci pacjenta. Liczbę osób cierpiących na chorobę Alzheimera na świecie ocenia się na 15-21 mln. Szacuje się, że w Polsce otępienie typu Alzheimera dotyka 250 tys. osób, a porównywalna liczba pacjentów cierpi na inne postacie otępienia. We Francji liczba ta obecnie wynosi ok. 850 tys. pacjentów, a do 2020 r. wzrośnie do 1,2 mln. Obok wysuwających się na plan pierwszy deficytów pamięci typowych dla procesów otępiennych, pacjenta charakteryzują zaburzenia sfery językowej w postaci anomii, trudności z wykorzystaniem właściwych słów, używanie słów o znaczeniu ogólnym, nadmierne uży-wanie zaimków, neologizmów, obniżenie sprawności leksykalnej i fluencji słownej. W artykule omówiono zaburzenia komunikacji języko-wej oraz problemy artykulacyjne, które mają charakter progresywny i współwystępują z zaburzeniami innych wyższych funkcji psychicznych.

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Section: Cognitive Deficits In Alzheimer's Diseasementioning

confidence: 99%

Section: Communication and Speech Impediments In Alzheimer's Diseasementioning

confidence: 99%

Section: Communication and Speech Impediments In Alzheimer's Diseasementioning

confidence: 99%

Section: Linguistic-system-mastery Impedimentmentioning

confidence: 99%

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Speech disturbances in Alzheimer’s disease

Wasilewski¹,

Kachaniuk²

2016

nin

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“…Pause frequency has been identified as a feature differentiating spontaneous speech in patients with AD from control groups (Gayraud et al, 2011), and may also be used to distinguish between mild, moderate and severe AD (Hoffman et al, 2010). Meilán et al (2014) used AD subjects and spoken data (read loud and clear sentences on a screen). They used acoustic measures such as pitch, volume and spectral noise measures.…”

mentioning

confidence: 99%

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

2016

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an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. Following the six previous edition of the workshop which were co-located with SMBM 2008 in Turku, Finland, with NAACL 2010 in Los Angeles, California, with Artificial Intelligence in Medicine (AIME 2011) in Bled, Slovenia, during NICTA Techfest 2013 in Sydney, Australia, co-located with EACL 2014 in Gothenburg, Sweden, and with EMNLP 2015 in Lisbon, Portugal, this workshop is co-located this year with EMNLP 2016 in Austin, Texas.The aim of the LOUHI 2016 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas:• Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection• Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)• Text representation, including dealing with data sparsity and dimensionality issues• Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain• Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation• Unsupervised methods, including distributional semantics• Evaluation, gold/reference standard construction and annotation• Syntactic, semantic and pragmatic analysis of health documents• Anonymization/de-identification of health records and ethics• Supporting the development of medical terminologies and ontologies• Individualization of content, consumer health vocabularies, summarization and simplification of text• NLP for supporting documentation and decision making practices iii• Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infectionsThe call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.We received 21 submissions. Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 13 papers. The overall acceptanc...

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Automatic classification of AD pathology in FTD phenotypes using natural speech

Cho,

Olm,

Ash

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONScreening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech‐based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD).METHODSWe trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC = 36, FTLD = 60, healthy controls [HC] = 89). Patients’ pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network‐based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features.RESULTSOur classifier showed 0.88 0.03 area under the curve (AUC) for ADNC versus FTLD and 0.93 0.04 AUC for patients versus HC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively.DISCUSSIONBrief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD.Highlights We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech‐based neuropathology screening tool.

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Speech in Alzheimer's Disease: Can Temporal and Acoustic Parameters Discriminate Dementia?

Cited by 104 publications

References 58 publications

Speech disturbances in Alzheimer’s disease

Speech disturbances in Alzheimer’s disease

Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis

Automatic classification of AD pathology in FTD phenotypes using natural speech

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