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@INPROCEEDINGS{PenalbaSanchez:283073,
      author       = {Penalba Sanchez, Lucía and Ribeiro, Pedro Baptista and
                      Crook-Rumsey, Mark and Sumich, Alexander and Howard,
                      Christina and Sanei, Saeid and Zandbagleh, Ahmad and Azami,
                      Hamed and Düzel, Emrah and Hämmerer, Dorothea and
                      Rodrigues, Pedro Miguel},
      title        = {{EEG} low conventional bands non‐linear machine
                      learning‐based analysis for {C}lassifying {MCI} and sleep
                      quality as a function of brain complexity},
      journal      = {Alzheimer's and dementia},
      volume       = {21},
      number       = {Suppl 7},
      issn         = {1552-5260},
      reportid     = {DZNE-2025-01480},
      pages        = {e108401},
      year         = {2025},
      abstract     = {Good sleep quality is essential for both physiological and
                      mental health. It helps in clearing TAU and beta-amyloid
                      aggregates and consolidating memory, key processes in
                      delaying dementia. Poor sleep is linked to reduced cognitive
                      flexibility in daily life, likely due to decreased brain
                      complexity, reflecting a reduced range of adaptive
                      spatiotemporal brain dynamics. This study introduces a novel
                      approach using non-linear EEG analysis focused on low
                      conventional bands to classify sleep quality in individuals
                      with mild cognitive impairment (MCI), based on brain
                      complexity.Resting-state EEG was collected from 22
                      participants with MCI aged 60+, grouped by sleep quality
                      (Pittsburgh Sleep Quality Index): 11 MCI with good sleep,
                      and 11 MCI with poor sleep (Table 1). EEG data (128
                      channels, 5-minute recordings) were normalized and
                      decomposed using the Discrete Wavelet Transform to reach
                      delta (1-4 Hz) and theta (4-8 Hz) bands. Ten non-linear
                      complexity features, namely approximate entropy, correlation
                      dimension, detrended fluctuation analysis, energy, Higuchi
                      fractal dimension, Hurst exponent, Katz fractal dimension,
                      Boltzmann Gibbs entropy, Lyapunov exponent and Shannon
                      entropy, were extracted from 5 second segments. Statistical
                      measures (mean, standard deviation, 95th percentile,
                      variance, median, kurtosis) were computed from these
                      time-distribution features. These statistics were then used
                      for training and testing a set of classic machine learning
                      classifiers, employing leave-one-out cross-validation
                      (Figure 2).Brain complexity successfully classified sleep
                      quality in MCI, achieving an accuracy and area under the
                      curve (AUC) of 1 in channel D13 (delta subband) using
                      Quadratic Discriminant Analysis (QDA), and an accuracy of
                      0.94 and an AUC of 0.95 in channel B17 (theta subband) using
                      the Extra Trees Classifier (ETC) (Figure 3).Specific machine
                      learning classifiers distinguish excellently sleep quality
                      in MCI using spatiotemporal complexity features from slow
                      EEG subbands. The most relevant channels for group
                      discrimination were primarily located in bilateral temporal
                      regions of the neocortex known to be among the first
                      affected in amnestic MCI, as previously shown in
                      neuroimaging studies. Future longitudinal studies could
                      investigate whether changes in brain complexity within these
                      slow-frequency temporal regions, influenced by sleep
                      quality, are associated with an earlier or faster onset of
                      dementia.},
      month         = {Jul},
      date          = {2025-07-27},
      organization  = {Alzheimer’s Association
                       International Conference, Toronto
                       (Canada), 27 Jul 2025 - 31 Jul 2025},
      keywords     = {Humans / Cognitive Dysfunction: physiopathology /
                      Electroencephalography: methods / Male / Female / Aged /
                      Brain: physiopathology / Middle Aged / Sleep Quality /
                      Sleep: physiology / Aged, 80 and over},
      cin          = {AG Düzel},
      ddc          = {610},
      cid          = {I:(DE-2719)5000006},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)1 / PUB:(DE-HGF)16},
      pubmed       = {pmid:41434901},
      pmc          = {pmc:PMC12725889},
      doi          = {10.1002/alz70861_108401},
      url          = {https://pub.dzne.de/record/283073},
}