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A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Authors :
Alyssa N. Kaser
Laura H. Lacritz
Holly R. Winiarski
Peru Gabirondo
Jeff Schaffert
Alberto J. Coca
Javier Jiménez-Raboso
Tomas Rojo
Carla Zaldua
Iker Honorato
Dario Gallego
Emmanuel Rosario Nieves
Leslie D. Rosenstein
C. Munro Cullum
Source :
Frontiers in Neurology, Vol 15 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

ObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p

Details

Language :
English
ISSN :
16642295
Volume :
15
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neurology
Publication Type :
Academic Journal
Accession number :
edsdoj.b37f7a3898fa436eb2dc65d95f6eaa37
Document Type :
article
Full Text :
https://doi.org/10.3389/fneur.2024.1342907