On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis
Open accessAbstract
The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.
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In Alzheimer's disease, ASLA markers differentiate patients from healthy persons and other patient groups, predict overall cognitive status, outperform certain cognitive tests in predicting dementia onset, and correlate with volume of the hippocampus and other core atrophy regions.
“In Alzheimer's disease, they differentiate patients from healthy persons 13,58-62 and other patient groups, 63 predict overall cognitive status, 64 outperform certain cognitive tests in predicting dementia onset, 65 and correlate with volume of the hippocampus and other core atrophy regions. 66,67”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [61]
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References 47 · Cited by 190
Research works printed but not linked: 1
- [29]Martinez F. Garcia J. Perez E. Carro J. Anara J.M. Patrones de Prosodia expresiva en pacientes con enfermedad de Alzheimer Psicothema 2012 24 16 21 22269358
- Authors
- López-de-Ipiña, K., Alonso, J.-B., Travieso, C., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., Ezeiza, A., Barroso, N., Ecay-Torres, M., Martinez-Lage, P., & Lizardui, U.
- Affiliations
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain
- Signal and Communication Departament (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Campus of Tafira, Las Palmas de Gran Canaria 35017, Spain
- Universidad de Las Palmas de Gran Canaria (ES)
- Digital Technologies Group, University of Vic, Sagrada família 7, Vic 08500, Spain
- Escola Universitaria Politècnica de Mataró (UPC), Tecnocampus, Mataró, Barcelona 08302, Spain
- University of the Basque Country (ES)
- CITA-Alzheimer Foundation, San Sebastian 20009, Spain
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2013-05-21
- Volume
- 13
- Issue
- 5
- Pages
- 6730-6745
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3690078
- OpenAlex
- W2121282288
- Cited by
- 190
- References
- 47
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 15 · 2023: 20 · 2022: 19 · 2021: 20 · 2020: 20 · 2019: 11 · 2018: 18 · 2017: 10 · 2016: 12 · 2015: 15 · 2014: 13
- FWCI
- 9.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, cc-by-nc-nd)
- Hispana (publishedVersion, cc-by-nc-nd)
- RIUVic (acceptedVersion, other-oa)
- Acceda (Universidad de Las Palmas de Gran Canaria) (submittedVersion)
- Communities in ADDI (Universidad del Pais Vasco) (submittedVersion, other-oa)
- Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Hispana (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by-nc-nd)
- Funders
- Eusko Jaurlaritza
- Euskal Herriko Unibertsitatea
- Ikerbasque, Basque Foundation for Science
- Ministerio de Ciencia e Innovación (TEC2012-38630-C04-03)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease, early diagnosis, speech analysis, non-invasive biomarkers, artificial neural networks, feature selection, spontaneous speech, emotional responses, fractal dimension, disease severity classification
- MeSH terms
- Adult, Aged, Aged, 80 and over, Alzheimer Disease, Automation, Emotions, Female, Humans, Male, Middle Aged, Pilot Projects, Signal Processing, Computer-Assisted, Speech, Temperature, Fractals, Diagnostic Techniques and Procedures, Young Adult
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