TY - JOUR
T1 - Exploratory Research on Key Technology of Human-Computer Interactive 2.5-Minute Fast Digital Early Warning for Mild Cognitive Impairment
AU - Li, Nan
AU - Yang, Xiaotong
AU - Du, Wencai
AU - Ogihara, Atsushi
AU - Zhou, Siyu
AU - Ma, Xiaowen
AU - Wang, Yujia
AU - Li, Shuwu
AU - Li, Kai
N1 - Publisher Copyright:
© 2022 Nan Li et al.
PY - 2022
Y1 - 2022
N2 - Objective. As the preclinical stage of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects' educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70-90 in a medical institution in Hangzhou, the two indexes of "correlation degree"and "drift rate"with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.
AB - Objective. As the preclinical stage of Alzheimer's disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects' educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70-90 in a medical institution in Hangzhou, the two indexes of "correlation degree"and "drift rate"with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.
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U2 - 10.1155/2022/2495330
DO - 10.1155/2022/2495330
M3 - Article
C2 - 35392035
AN - SCOPUS:85128481049
SN - 1687-5265
VL - 2022
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 2495330
ER -