S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test

Background: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. Methodology: In this study, we...

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Vydané v:	IEEE journal of biomedical and health informatics Ročník 28; číslo 8; s. 4456 - 4470
Hlavní autori:	Tang, Zeshen, Tang, Shiyu, Wang, Haoran, Li, Renren, Zhang, Xiaochen, Zhang, Wei, Yuan, Xiao, Zang, Yaning, Li, Yanping, Zhou, Tian, Li, Yunxia
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2024
Predmet:	Aged Aged, 80 and over Algorithms Cognitive Dysfunction - diagnosis Cognitive impairment computer-aided diagnosis Correlation Deep Learning Diagnosis, Computer-Assisted - methods Feature extraction Female Hospitals Humans Male Neurology Older adults semi-supervised Signal Processing, Computer-Assisted Supervised Machine Learning Task analysis Trail Making Test trail making test (TMT) Trajectory variational autoencoder (VAE) vector quantization (VQ)
ISSN:	2168-2194, 2168-2208, 2168-2208
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