title: Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG.
abstract: OBJECTIVE
  We explored the ability of specifically designed and trained recurrent neural networks (RNNs), combined with wavelet preprocessing, to discriminate between the electroencephalograms (EEGs) of patients with mild Alzheimer's disease (AD) and their age-matched control subjects.
  
  
  METHODS
  Twomin recordings of resting eyes-closed continuous EEGs (as well as their wavelet-filtered subbands) obtained from parieto-occipital channels of 10 early AD patients and 10 healthy controls were input into RNNs for training and testing purposes. The RNNs were chosen because they can implement extremely non-linear decision boundaries and possess memory of the state, which is crucial for the considered task.
  
  
  RESULTS
  The best training/testing results were achieved using a 3-layer RNN on left parietal channel level 4 high-pass wavelet subbands. When trained on 3 AD and 3 control recordings, the resulting RNN tested well on all remaining controls and 5 out of 7 AD patients. This represented a significantly better than chance performance of about 80% sensitivity at 100% specificity.
  
  
  CONCLUSIONS
  The suggested combined wavelet/RNN approach may be useful in analyzing long-term continuous EEGs for early recognition of AD. This approach should be extended on larger patient populations before its clinical diagnostic value can be established. Further lines of investigation might also require that EEGs be recorded from patients engaged in certain mental (cognitive) activities.
authors.affiliation: Department of Neuropsychiatry, Texas Tech University Health Sciences Center, 3601 4th Street, MS 8321 Lubbock, TX 79430, USA. neuaap@ttuhsc.edu
authors.forename: A A
authors.initials: AA
authors.lastname: Petrosian
authors.forename: D V
authors.initials: DV
authors.lastname: Prokhorov
authors.forename: W
authors.initials: W
authors.lastname: Lajara-Nanson
authors.forename: R B
authors.initials: RB
authors.lastname: Schiffer
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D003937:Diagnosis, Differential; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D011237:Predictive Value of Tests