It is generally believed that early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This chapter presents a strategic framework for the early diagnosis of neurodegenerative disease from gait discrimination to neural synchronization. Here, we propose and present a new classifier fusion strategy that combines classification algorithms and rules (voting, product, mean, median, maximum, and minimum) to measure specific behaviors in people with neurodegenerative diseases. On the other hand, it is now evident that electroencephalographic (EEG) signals of patients with Alzheimer disease usually have less synchronization than those of healthy subjects. Computing neural synchronization of EEG signals to detect any perturbation will help diagnose this fatal disease at an earlier stage. Three neural synchrony measurement techniques, phase synchrony, magnitude-squared coherence, and cross-correlation, are applied to analyze three different databases of mild Alzheimer disease patients and healthy subjects to compare the right and left temporal lobe of the brain with the rest of the brain area. Results are compared using Mann–Whitney U statistical test.
|Title of host publication||Applied Computing in Medicine and Health|
|Subtitle of host publication||A volume in Emerging Topics in Computer Science and Applied Computing|
|Editors||Dhiya Al-Jumeily, Abir Hussain, Conor Mallucci, Carol Oliver|
|Number of pages||26|
|Publication status||Published - 2016|
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- Department of Computer Science - Senior Lecturer
- School of Computing and Engineering
- Centre for Industrial Analytics - Member