Percorrer por autor "Azami, Hamed"
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- EEG low conventional bands non‐linear machine learning‐based analysis for classifying MCI and sleep quality as a function of brain complexityPublication . Penalba‐Sánchez, Lucía; Ribeiro, Pedro Baptista; Crook‐Rumsey, Mark; Sumich, Alexander; Howard, Christina; Sanei, Saeid; Zandbagleh, Ahmad; Azami, Hamed; Düzel, Emrah; Hämmerer, Dorothea; Rodrigues, Pedro MiguelBACKGROUND: 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. METHOD: 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). RESULTS: 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). CONCLUSION: 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.
- Intra- and inter-regional complexity in multi-channel awake EEG through multivariate multiscale dispersion entropy for assessing sleep quality and agingPublication . Zandbagleh, Ahmad; Sanei, Saeid; Penalba-Sánchez, Lucía; Rodrigues, Pedro Miguel; Crook-Rumsey, Mark; Azami, HamedAging and poor sleep quality are associated with altered brain dynamics, yet current electroencephalography (EEG) analyses often overlook regional complexity. This study addresses this gap by introducing a novel integration of intra- and inter-regional complexity analysis using multivariate multiscale dispersion entropy (mvMDE) from awake resting-state EEG for the first time. Moreover, assessing both intra- and inter-regional complexity provides a comprehensive perspective on the dynamic interplay between localized neural activity and its coordination across brain regions, which is essential for understanding the neural substrates of aging and sleep quality. Data from 58 participants—24 young adults (mean age = 24.7 ± 3.4) and 34 older adults (mean age = 72.9 ± 4.2)—were analyzed, with each age group further divided based on Pittsburgh Sleep Quality Index (PSQI) scores. To capture inter-regional complexity, mvMDE was applied to the most informative group of sensors, with one sensor selected from each brain region using four methods: highest average correlation, highest entropy, highest mutual information, and highest principal component loading. This targeted approach reduced computational cost and enhanced the effect sizes (ESs), particularly at large scale factors (e.g., 25) linked to delta-band activity, with the PCA-based method achieving the highest ESs (1.043 for sleep quality in older adults). Overall, we expect that both inter- and intra-regional complexity will play a pivotal role in elucidating neural mechanisms as captured by various physiological data modalities—such as EEG, magnetoencephalography, and magnetic resonance imaging—thereby offering promising insights for a range of biomedical applications.