Abstract RecPad th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th A. Coito 1, D. Belo 1, T. Paiva 2, J.M. Sanches 1 1 Institute for Systems and Robotics / Instituto Superior Técnico, Lisboa, Portugal 2 Centro de Electroencefalografia e Neurofisiologia Clínica / Faculdade de Medicina da Universidade de Lisboa Lisbon, Portugal Problem Formulation Experimental Results  Obstructive Sleep Apnea Syndrome (OSAS) is a very common sleep disorder associated with several neurocognitive impairments.  This study aims to assess the power spectral density (PSD) of the electroencephalographam (EEG) before (pre), during (dur) and after (post) obstructive apnea episodes, in four frequency bands: delta (δ), theta (θ), alpha (α) and beta (β).  An Autoregressive (AR) model was used to make spectral analysis of 21 EEG signals obtained with polysomnography (PSG).  The results show a significant decrease of the EEG δ power during OSA that does not totally recover immediately after the episode.  Since δ waves are linked to learning and plasticity processes, it is hypothesized that decreased δ power during the obstructive event may contribute to the cognitive deterioration in patients with OSAS. Mean Power Computation, Statistical analysis EEG extraction  Signals were recorded on a computerized PSG system during the patient sleep time (about 8h);  EEG electrodes were positioned according to the International System with 21 electrodes and 2 references;  The EEG signals were acquired at a sampling frequency of 100 Hz.  The nasal airflow was recorded as well. (c) α power (a) δ power (b) θ power (d) β power AR Modeling  The mean δ, θ, α and β powers were computed as well as the standard deviation.  Powers corresponding to pre, dur and post for each EEG frequency domain were compared by a two-sample t-test. A p-value < 0.05 was considered statistically significant. Fig. 1- Normalized mean EEG delta spectral power for 171 apnea events, for 5 brain regions - central, frontal, occipital, parietal and temporal – and for 4 frequency bands (a) δ (b) θ (c) α and (d) β. The white bars represent the mean pre power, the black bars the mean power during apnea and grey bars the mean post power spectral density. There is a statistically significant δ power decrease during OSA, which is not fully recovered after the episode for all the brain regions. The difference between θ power during OSA and pre was found to be statistically significant only in frontal region. Mean α power during and after OSA was not statistically significant in any region, however there is a tendency to increase after the episode in frontal, parietal and temporal regions. Dur mean β power was significantly different from pre only in occipital region. The difference between pre and post power is not statistically significant in any region. Conclusions  Since δ waves are linked to learning and plasticity processes [5], the decrease of δ spectral power during apneic episodes may contribute to the patient’s daytime cognitive dysfunction.  Decreases in δ power preceded arousal and termination of apneic events in both REM and NREM sleep are reported in literature [2, 3, 7]. However, these studies were more interested in the detection of non-visible arousals (subcortical arousals, related to autonomic nervous system), than investigate the PSD changes during OSA events.  This is a preliminar study. There were no statistical increases in α or β bands after apnea, specially in frontal (motor area) and occipital (visual area) as it was previously expected, due to the cortical arousals. References [1] R. Takalo, H. Hytti, and H. Ihalainen. Tutorial on univariate autoregressive spectral analysis. J. Clinical Monitoring and Computing, vol. 19, pp. 401–410, [2] H. Bandla and D. Gozal. Dynamic changes in EEG spectra during obstructive apnea in children. Pediatric Pulmonology,29:359–365, [3] K. Dingli, T. Assimakopoulos, I. Fietze, C. Witt, P. Wraith, and N. Douglas. Electroencephalographic spectral analysis: detection of cortical activity changes in sleep apnoea patients. Eur Respir J, 20:1246–1253, [4] E. Kandel, J. Schwartz, and T. Jessel. Principles of Neural Science. McGraw-Hill, [5] M.Massimini, G. Tononi, and R. Huber. Slow waves, synaptic plasticity and information processing: insights from transcranial magnetic stimulation and high-density EEG experiments. Eur J Neurosci., 29(9):1761–1770, [6] V. Somers, D. White, R. Amin, W. Abraham, F. Costa, A. Culebras, S. Daniels, J. Floras, C. Hunt, L. Olson, T. Pickering, R. Russel, M.Woo, and T. Young. Sleep apnea and cardiovascular disease. J Am College of Cardiology, 52(8):313–323, [7] J. Walsleben, E. O’Malley, K. Bonnet, R. Norman, and D. Rapoport. The utility of topographic EEG mapping in obstructive sleep apnea syndrome. Sleep, 16:76–78, [8] T. Young, M. Palta, J. Dempsey, J. Skatrud, S. Weber, and S. Badr. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med, 328:1230–1235, EEG Air Flow Episode selection  The cessation of the air flow during more than 10 s in NREM-2 sleep stage and more than 60 s isolation were the criteria of selecting an apnea episode;  Each episode was divided into three segments: pre, dur and post.  Pre and post periods have a duration of 30 seconds.  Dur have a variable duration between 10 and 20 seconds.  Bursts of K-complex and δ activity (subcortical arousals) occurring at the end of the apneic episodes were removed.  The resulting dataset includes epochs (171 episodes x 3 phases x 21 channels). An Yule-Walker AR model was fitted to each assembled EEG signal to compute the PSD in the four referred EEG frequency bands at each period - pre, dur and post.  Each value of a time series is regressed on its past values. The number of past values used is the order of the model.  The used method for determining model order was the Akaike Information Criterion (AIC), where o is the model order and σ 2 is the prediction error variance associated with o. The selected o minimizes the value of the criterion [1].  To estimate the AR coefficients of each EEG epoch, the Yule-Walker method was used. The coefficients of the AR model, a = {a1, a2,..., a o }, were estimated by minimizing the following energy function: Which minimize the forward prediction error in the least-squares sense. y(k) is the current value of the time series.  The spectral estimate used to obtain the power is the squared magnitude of the frequency response of this AR model. EEG spectral power changes before, during and after obstructive sleep apneas