Chapter

Advances in Neuro-Information Processing

Volume 5507 of the series Lecture Notes in Computer Science pp 410-417

Early Detection of the Alzheimer Disease Combining Feature Selection and Kernel Machines

  • J. RamírezAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , J. M. GórrizAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , M. LópezAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , D. Salas-GonzalezAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , I. ÁlvarezAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , F. SegoviaAffiliated withDept. of Signal Theory, Networking and Communications, University of Granada
  • , C. G. PuntonetAffiliated withDept. of Architecture and Computer Technology, University of Granada

Abstract

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.