Internet of Things for Diagnosis of Alzheimer’s Disease: A Multimodal Machine Learning Approach Based on Eye Movement Features-Reference-Cited by-同舟云学术

Internet of Things for Diagnosis of Alzheimer’s Disease: A Multimodal Machine Learning Approach Based on Eye Movement Features

Published:2023-07-01 Issue:13 Volume:10 Page:11476-11485
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Yin Yunpeng¹^ORCID,Wang Han¹^ORCID,Liu Shuai²,Sun Jinglin¹^ORCID,Jing Peiguang³^ORCID,Liu Yu¹^ORCID

Affiliation:

1. School of Microelectronics, Tianjin University, Tianjin, China

2. Department of Neurology, Tianjin Key Laboratory of Cerebrovascular and Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin Huanhu Hospital, Tianjin, China

3. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10159464/10044924.pdf?arnumber=10044924

Reference35 articles.

1. Animal Spotting in Alzheimer's Disease: An Eye Tracking Study of Object Categorization

2. AI for next generation computing: Emerging trends and future directions;gill;Internet of the Things,2022

3. Patients with Mild Alzheimer’s Disease Fail When Using Their Working Memory: Evidence from the Eye Tracking Technique

4. Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images

5. Visual Search Efficiency in Mild Cognitive Impairment and Alzheimer’s Disease: An Eye Movement Study

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