Voice quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls

Themistocleous, Charalambos; Eckerström, Marie; Kokkinakis, Dimitrios

doi:10.1371/journal.pone.0236009

Cited by 78 publications

(59 citation statements)

References 60 publications

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“…A growing body of work is highlighting the ongoing clinical validation of speech-based measures in a variety of clinical contexts. Speech has been demonstrated to have diagnostic validity for Alzheimer's disease (AD) and mild cognitive impairment (MCI) in studies using machine-learning classification models to differentiate individuals with AD/MCI from healthy individuals based on speech samples [34][35][36][37][38][39][40][41]. Additionally, speech analysis has been shown to be able to detect individuals with depression [42][43][44][45], schizophrenia [46][47][48][49], autism spectrum disorder [50], and Parkinson's disease [51,52], and can differentiate the subtypes of primary progressive aphasia and frontotemporal dementia [53][54][55].…”

Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

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Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.

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Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

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“…The reduction in speech expressiveness during picturedescription tasks has also been quantified by measuring speech rate as well as the reduction in relevant information (18,(24)(25)(26)(27). By using a combination of these features, previous studies have succeeded in differentiating AD and MCI patients from healthy controls (20,21,24,25,(28)(29)(30)(31). However, they mainly investigated speech data obtained while participants took part in neuropsychological tasks, typically conducted by clinicians.…”

Section: Introductionmentioning

confidence: 99%

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Yamada

Shinkawa

Kobayashi

et al. 2021

Front. Digit. Health

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Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

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“…The recordings were conducted in an isolated environment at the University of Gothenburg. The recordings were analyzed acoustically, using advanced acoustic analysis and signal processing algorithms (see for the methodological analysis (Themistocleous et al, 2020)).…”

Section: Methodsmentioning

confidence: 99%

“…These results suggested that the accuracy of traditional screening tools may be improved through the addition of computerized language analysis. In this study, our goal is to determine whether cognitive decline can be estimated using acoustic information, as we have shown that speech production is impaired in patients with MCI , 2020. Thus, contributing to the work of identifying automatic diagnostic markers that have the potential to facilitate clinical evaluation and therapy.…”

Section: Introductionmentioning

confidence: 99%

Automated speech analysis enables MCI diagnosis

Themistocleous¹,

Eckerström²,

Kokkinakis³

2020

Proceedings of 11th International Conference of Experimental Linguistics

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Mild Cognitive Impairment (MCI) is a condition characterized by cognitive decline greater than expected for an individual's age and education level. In this study, we are investigating whether acoustic properties of speech production can improve the classification of individuals with MCI from healthy controls augmenting the Mini Mental State Examination, a traditional screening tool, with automatically extracted acoustic information. We found that just one acoustic feature, can improve the AUC score (measuring a trade-off between sensitivity and specificity) from 0.77 to 0.89 in a boosting classification task. These preliminary results suggest that computerized language analysis can improve the accuracy of traditional screening tools.

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Voice quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls

Cited by 78 publications

References 60 publications

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Automated speech analysis enables MCI diagnosis

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