Thammasat-NECTEC-Chula’s Thai Language and Cognition Assessment (TLCA): The Thai Alzheimer’s and Mild Cognitive Impairment Screening Test

Munthuli, Adirek; Vongsurakrai, S; Anansiripinyo, Thanaporn; Ellermann, V; Sroykhumpa, K; Onsuwan, Chutamanee; Chutichetpong, P; Hemrungrojn, Solaphat; Kosawat, Krit; Tantibundhit, Charturong

doi:10.1109/embc46164.2021.9630779

Cited by 1 publication

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Munthuli et al [64] devised a new speech task called Thai Language and Cognition Assessment (TLCA) that is specified for the Thai language and culture for screening of MCI and AD from HC. The TLCA contains 21 tasks out of which 16 are speech tasks.…”

Section: Application Of Logistic Regressionmentioning

confidence: 99%

“…Munthuli et al [64] utilized openSMILE's Emobase feature set, comprising 988 features derived from 19 functionals of 26 low-level descriptors extracted from 1,440 speech signal files, encompassing attributes such as intensity, loudness, MFCCs, and Zero Crossing Rate. König et al [59] incorporated vocal features extracted from speech recordings, including low-level markers such as silence and voice duration, and selected relevant features using the Mann-Whitney test based on their significance in discriminating between cognitive different impairment groups (SCI, MCI, AD, Mixed) Themistocleous et al [47] used both acoustic and Sociophonetic (age and gender) Features.…”

Section: Acoustic Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

Al-Hammadi,

Fleyeh,

Åberg

et al. 2024

JAD

View full text Add to dashboard Cite

Background: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer’s disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

show abstract

Section: Application Of Logistic Regressionmentioning

confidence: 99%