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  1. Article: A novel speech analysis algorithm to detect cognitive impairment in a Spanish population.

    Kaser, Alyssa N / Lacritz, Laura H / Winiarski, Holly R / Gabirondo, Peru / Schaffert, Jeff / Coca, Alberto J / Jiménez-Raboso, Javier / Rojo, Tomas / Zaldua, Carla / Honorato, Iker / Gallego, Dario / Nieves, Emmanuel Rosario / Rosenstein, Leslie D / Cullum, C Munro

    Frontiers in neurology

    2024  Volume 15, Page(s) 1342907

    Abstract: Objective: Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study ... ...

    Abstract Objective: Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.
    Method: Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN,
    Results: Mean logit algorithm scores were significantly different across groups in the testing sample (
    Conclusion: Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.
    Language English
    Publishing date 2024-04-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564214-5
    ISSN 1664-2295
    ISSN 1664-2295
    DOI 10.3389/fneur.2024.1342907
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.

    García-Gutiérrez, Fernando / Alegret, Montserrat / Marquié, Marta / Muñoz, Nathalia / Ortega, Gemma / Cano, Amanda / De Rojas, Itziar / García-González, Pablo / Olivé, Clàudia / Puerta, Raquel / García-Sanchez, Ainhoa / Capdevila-Bayo, María / Montrreal, Laura / Pytel, Vanesa / Rosende-Roca, Maitee / Zaldua, Carla / Gabirondo, Peru / Tárraga, Lluís / Ruiz, Agustín /
    Boada, Mercè / Valero, Sergi

    Alzheimer's research & therapy

    2024  Volume 16, Issue 1, Page(s) 26

    Abstract: Background: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting ... ...

    Abstract Background: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum.
    Methods: Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information.
    Results: The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability.
    Conclusions: In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.
    MeSH term(s) Humans ; Alzheimer Disease/diagnosis ; Alzheimer Disease/psychology ; Speech ; Neuropsychological Tests ; Cognitive Dysfunction/diagnosis ; Cognitive Dysfunction/psychology ; Cognition ; Machine Learning ; Disease Progression
    Language English
    Publishing date 2024-02-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2506521-X
    ISSN 1758-9193 ; 1758-9193
    ISSN (online) 1758-9193
    ISSN 1758-9193
    DOI 10.1186/s13195-024-01394-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment.

    García-Gutiérrez, Fernando / Marquié, Marta / Muñoz, Nathalia / Alegret, Montserrat / Cano, Amanda / de Rojas, Itziar / García-González, Pablo / Olivé, Clàudia / Puerta, Raquel / Orellana, Adelina / Montrreal, Laura / Pytel, Vanesa / Ricciardi, Mario / Zaldua, Carla / Gabirondo, Peru / Hinzen, Wolfram / Lleonart, Núria / García-Sánchez, Ainhoa / Tárraga, Lluís /
    Ruiz, Agustín / Boada, Mercè / Valero, Sergi

    Frontiers in neuroscience

    2023  Volume 17, Page(s) 1221401

    Abstract: Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of ... ...

    Abstract Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of A
    Language English
    Publishing date 2023-09-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2023.1221401
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Feed3: A Strategy for a 3-Direction Connection Among AT Consumers and Developers.

    Ortega-Moral, Manuel / Rivero, Jesica / Gutiérrez, José Antonio / Iglesias, Andrés / Suárez, Pablo / Peinado, Ignacio / de Lera, Eva / Zaldua, Carla / Vanderheiden, Gregg

    Studies in health technology and informatics

    2017  Volume 242, Page(s) 1055–1058

    Abstract: The Feed3 strategy aims to provide AT consumers, developers and manufacturers with Feedback, Feedforwards and FeedPeer mechanisms to collaborate in the development of novel accessible solutions. This strategy was developed as part of the GPII and it is ... ...

    Abstract The Feed3 strategy aims to provide AT consumers, developers and manufacturers with Feedback, Feedforwards and FeedPeer mechanisms to collaborate in the development of novel accessible solutions. This strategy was developed as part of the GPII and it is currently adopted by the Unified Listing and DeveloperSpace infrastructure components.
    Language English
    Publishing date 2017
    Publishing country Netherlands
    Document type Journal Article
    ISSN 0926-9630
    ISSN 0926-9630
    Database MEDical Literature Analysis and Retrieval System OnLINE

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