Discriminating speech traits of Alzheimer's disease assessed through a corpus of reading task for Spanish language

Ivanova, Olga; Meilán, Juan José García; Martínez‐Sánchez, Francisco; Martínez-Nicolás, Israel; Llorente, Thide E.; González, Nuria Carcavilla

doi:10.1016/j.csl.2021.101341

Cited by 18 publications

(14 citation statements)

References 103 publications

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“…Among others [ 11 ], changes in the percentage of voice breaks, the quantity of periods of voice and the number of voice breaks can be used to distinguish between AD, MCI, and CN subjects [ 91 ]. Increase in reading and phonation times have been reported too, apparently parallel to the cognitive decline [ 92 ].…”

Section: Methodsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

Journal of Alzheimer’s Disease

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Background: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer’s disease (AD), as the most common type of dementia, has become more frequent too. Background: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

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Section: Methodsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

Journal of Alzheimer’s Disease

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“…Consequently, in our opinion, it is not possible to offer any advice on the more effective elicitation task at the moment. A deeper comparative analysis would be desirable, expanding the findings of Clarke et al (2021b), Yamada et al (2021) and Ivanova et al (2022).…”

Section: Patient Enrollment Data Collection and Dlb Computationmentioning

confidence: 99%

Natural language processing techniques for studying language in pathological ageing: A scoping review

Gagliardi

2023

Intl J Lang & Comm Disor

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BackgroundIn the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low‐cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost‐effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria).AimsTo provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non‐intrusive language‐based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach.Methods & ProceduresWe performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews.Main ContributionThe paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders).Conclusions & ImplicationsDespite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.WHAT THIS PAPER ADDSWhat is already known on this subject Speech and languages abilities change due to non‐pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers—DLBs).What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed.What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

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“…As well as informing about several other cognitive functions and processing abilities, there is growing evidence that language, speech and communication traits can sensitively distinguish between ageing trajectories (Alhanai et al., 2017; Lin et al., 2020), including pathological itineraries. This is particularly evident for ageing‐related neurodegenerative diseases, like Parkinson disease (Rusz et al., 2021), frontotemporal dementia (Panman et al., 2021) and more notably, AD (Bose et al., 2021; Cuetos et al., 2007; Fraser et al., 2016; Ivanova et al., 2022; Pistono et al., 2019).…”

Section: Language As a Window To Cognitive Changes In Aging: Achievem...mentioning

confidence: 99%

Language in healthy and pathological ageing: Methodological milestones and challenges

Benítez‐Burraco,

Ivanova

2023

Intl J Lang & Comm Disor

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Discriminating speech traits of Alzheimer's disease assessed through a corpus of reading task for Spanish language

Cited by 18 publications

References 103 publications

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Natural language processing techniques for studying language in pathological ageing: A scoping review

Language in healthy and pathological ageing: Methodological milestones and challenges

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