Empirical Mode Decomposition as a feature extraction method for Alzheimer's Disease Diagnosis

Abstract

Alzheimer and Parkinson Diseases are the two most common neurodegenerative disorders. As the number of AD and PD patients has increased, its early diagnosis has received more attention for both social and medical reasons. Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. In this paper a new method for brain SPECT image feature extraction is shown. This novel Computer Aided Diagnosis (CAD) system is based on the Empirical Mode Decomposition (EMD) in combination with Gaussian filters, intensity normalization, Principal Component Analysis feature extraction and a Support Vector Machines Classification method. Yielding up to 85.87% accuracy in separating AD and normal controls, and up to 95% accuracy in PD, which greatly improves the baseline Voxel-As-Feature (VAF) approach (80.21 % and 87.5% accuracy respectively).