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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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 28; H. 8; S. 4456 - 4470
Hauptverfasser:	Tang, Zeshen, Tang, Shiyu, Wang, Haoran, Li, Renren, Zhang, Xiaochen, Zhang, Wei, Yuan, Xiao, Zang, Yaning, Li, Yanping, Zhou, Tian, Li, Yunxia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2024
Schlagworte:	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
Online-Zugang:	Volltext
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