Title:Research Progress of Eye Movement Analyses and its Detection
Algorithms in Alzheimer’s Disease
Volume: 21
Issue: 2
Author(s): Xueying He*, Ivan Selesnick*Ming Zhu
Affiliation:
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, CN, USA
- Tandon School of Engineering, New York University, Brooklyn, NY, USA
- Tandon School of Engineering, New York University, Brooklyn, NY, USA
Keywords:
Alzheimer’s disease, mild cognitive impairment, eye movements, biomarker, early diagnosis, algorithms.
Abstract: Alzheimer's disease (AD) has been considered one of the most challenging forms of dementia.
The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment.
Based on the previous literature summarizing the research results on the relationship between
eye movement and AD before 2013, this paper reviewed 34 original eye movements research
papers only closely related to AD published in the past ten years and pointed out that the
prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search
tasks (3 papers) are still the research objects of many researchers, Some researchers have looked
at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began
to use combinations of different saccade tasks to detect the relationship between eye movement
and AD, which had not been done before. These reflect the diversity of eye movement tasks and
the complexity and difficulty of the relationship between eye movement and AD. On this basis,
the current processing and analysis methods of eye movement datasets are analyzed and discussed
in detail, and we note that certain key data that may be especially important for the early diagnosis
of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally,
we note that the development of methods that can accurately denoise and classify and quickly process
massive eye movement data is quite significant for detecting eye movements in early diagnosis
of AD.