Abstract—Alzheimer’s disease is one of the most increasing neurodegenerative disorder which mainly affects the memory, brain functioning and thinking of elders. Since the cure for this disease is yet to be found, it’s vital to diagnose Alzheimer’s disease in the early stages and to delay the progress of the disease as much as possible. There have been many researches conducted to diagnose Alzheimer’s disease using different brain imaging techniques and computational methods. The main aim of this paper is to review brain imaging techniques, preprocessing algorithms and classification algorithms to identify the most suitable approach to diagnose Alzheimer’s disease. Specifically this paper consists of following sections: (i) A brief description of the disease and the case; (ii) Review of brain imaging techniques (EEG, MEG, MRI and FMRI); (iii) Review and comparison of preprocessing algorithms (FFT, Wavelet transform and TFD); (iv) Review and comparison of classification algorithms (SVM, decision tree, neural network and random forest).
 

Index Terms—AD, EEG, FMRI, MRI, MEG, FFT, wavelet transform, SVM, decision tree, random forest, neural network

Cite: Ahila Arumugam Annakutty and Achala Chathuranga Aponso, "Review of Brain Imaging Techniques, Feature Extraction and Classification Algorithms to Identify Alzheimer’s Disease," International Journal of Pharma Medicine and Biological Sciences, Vol. 5, No. 3, pp. 178-183, July 2016. 10.18178/ijpmbs.5.3.178-183