Advances on automatic speech analysis for early detection of Alzheimer disease: A non-linear multi-task approach
Abstract
Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.
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CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis.
“Recently, the CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis [51,52,53,54,55,56,57,58].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [55]
Lopez et al. achieved accuracy 0.89 on a private dataset using linear and nonlinear speech features.
“| Lopez et al. [55] | Linear features include spectral domain features and time domain features, such as harmonicity, spectrum centroid, formants, etc. Nonlinear characteristics include fractal dimension, permutation entropy, multi-scale permutation entropy, etc. | speech | private dataset | Accuracy: 0.89 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [55]
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- Authors
- Lopez-de-Ipina, K., Martinez-de-Lizarduy, U., Calvo, P. M., Mekyska, J., Beitia, B., Barroso, N., Estanga, A., Tainta, M., & Ecay-Torres, M.
- Affiliations
- Department of Systems Engineering and Automation, Faculty of Engineering-Gipuzkoa, Universidad del Pais Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain
- Department of Electronic Technology, Faculty of Engineering-Gipuzkoa, Universidad del Pais Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain
- University of the Basque Country (ES)
- Department of Telecommunications, Brno University of Technology, Brno, Czech Republic
- Fundacion CITA Alzheimer (ES)
- Department of Neurology, CITA-Alzheimer Foundation, Donostia, Spain
- Venue
- Current Alzheimer Research
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-01-03
- Volume
- 15
- Issue
- 2
- Pages
- 139-148
- ISSN
- 1567-2050 (ISSN-L)
- OpenAlex
- W2768138179
- Cited by
- 65
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 14 · 2022: 9 · 2021: 9 · 2020: 12 · 2019: 3 · 2018: 3 · 2017: 1
- FWCI
- 4.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Alzheimer Research (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- Alzheimer's disease, early detection, automated speech analysis, cognitive screening, fractal dimension, multiscale permutation entropy, convolutional neural networks, multilayer perceptron, dementia diagnosis
- MeSH terms
- Deep Learning, Adult, Aged, Alzheimer Disease, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, Neuropsychological Tests, Pattern Recognition, Automated, Speech, Speech Production Measurement, Cohort Studies, Nonlinear Dynamics, Early Diagnosis, Speech Recognition Software, Cognitive Dysfunction
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