Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study.
JMIR Aging
; 7: e50537, 2024 Apr 29.
Article
em En
| MEDLINE
| ID: mdl-38386279
ABSTRACT
BACKGROUND:
The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults.OBJECTIVE:
This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline.METHODS:
A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability.RESULTS:
In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets.CONCLUSIONS:
This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
Problema de saúde:
1_doencas_nao_transmissiveis
Assunto principal:
Fala
/
Disfunção Cognitiva
Limite:
Aged
/
Aged80
/
Female
/
Humans
/
Male
País/Região como assunto:
Europa
Idioma:
En
Revista:
JMIR Aging
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Itália