Early Diagnosis of Alzheimer’s Disease Based on Face Recognition Using M-Health Technology

Authors

  • Tiexing He General Education Department, Hangzhou Medical College, Hangzhou 310053, China
  • Xueping Zhang Geriatrics Department, Hangzhou Seventh People’s Hospital, Hangzhou 310013, China
  • Limin Zhu The First Ward of Leaders, Hangzhou Third People’s Hospital, Hangzhou 310009, China

Abstract

The traditional methods used for the diagnosis of early Alzheimer’s disease have high technical requirements, are expensive, require invasive diagnosis,
and cannot be widely used in clinical practice. Therefore, a method for the diagnosis of early Alzheimer’s disease based on face recognition by means
of medical technology is proposed. Firstly, an M-health app for the diagnosis of early Alzheimer’s disease is designed and applied to the elderly Then,
the facial features of elderly people with sleep disorders are extracted, and then are compared with all the feature vectors stored in the database, so
as to determine whether the elderly subject has a sleep disorder. If this is not the case, it is concluded that the person does not suffer from Alzheimer’s
disease, and there is no need to carry out the next step. If there is indication of a sleep disorder, it is preliminarily diagnosed that there may be early
Alzheimer’s disease. Hence, further diagnosis is needed through the early Alzheimer’s disease diagnostic system which mainly includes the checklist
test module, the data analysis module and the query analysis and feedback module. In the data analysis module, the Bayesian network is used to
make the early prediction and early diagnosis of the patients before the onset of the disease. The experimental results show that the proposed method
has high diagnostic accuracy and strong practicability

Keywords: M-health technology; face recognition; early; diagnosis; Alzheimer’s disease

Cite As

T. He, X. Zhang, L. Zhum, “Early Diagnosis of Alzheimer’s Disease Based on Face Recognition Using
M-Health Technologyâ€, Engineering Intelligent Systems, vol. 28 no. 2, pp. 99-108, 2020.


Published

2020-06-01