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- The effects of 24-hour sleep deprivation on the human brain: a multimodal neurophysiological approachPublication . Gonçalves, Alice; Pinto, Sara; Ferreira, Simão; Borges, Daniel FilipeIntroduction: Sleep is an important aspect of human health and well-being and influences various physiological and cognitive functions such as learning and attention. On the other hand, sleep deprivation activates the sympathetic nervous system, negatively impacting blood pressure, heart rate, glucose metabolism, cortisol, and hormones. It alters mood, behaviour, and reduces awareness leading to a poor performance, hence the importance of studying the neurophysiological and neurocognitive effects of 24h sleep deprivation. Objective: Our main goal is to investigate the effects of acute sleep deprivation on the cerebral cortex in healthy university students using a multimodal approach and neurocognitive scales. Methods: This study will use a magnetic stimulator to assess neurophysiological changes, while actigraphy will verify sleep deprivation in the intervention group. Neuropsychological assessments include the Trail Making Test (TMT), measuring visual processing speed and cognitive flexibility, the Psychomotor Vigilance Task (PVT), assessing sustained attention through reaction times to unpredictable stimuli and N-Back test measuring working memory by requiring participants to recall objects presented a few steps earlier. EEG data will be recorded using a cap with 32 channels during. This integrated approach provides a comprehensive analysis of the effects of sleep deprivation on cognitive and neurophysiological functions. Expected Results: Hypothetically, there will be a significant decline in cognitive performance, with impairments in key areas such as visual attention, processing speed and cognitive flexibility. These results will likely manifest in longer reaction times, more frequent errors and overall lower task execution.
- Sleep stage detection: a clinical validation study of a custom-built single-channel in-ear EEG sensorPublication . Borges, Daniel Filipe; Soares, Joana I.; Silva, Heloísa; Felgueiras, João; Batista, Carla; Ferreira, Simão; Rocha, Nuno; Leal, AlbertoIntroduction:Sleep is vital for health. It has regenerative and protective functions, and its disruption reduces the quality of life and increases susceptibility to disease. During sleep, there is a cyclicity of distinct phases that are studied using polysomnography (PSG), a costly and technically demanding method that compromises the quality of natural sleep. The search for simpler devices for recording biological signals at home addresses some of these issues. Objective: To clinically validate a custom-built single-channel in-ear EEG sensor for sleep classification by assessing various sleep metrics and staging decisions with simultaneously recorded PSG. Methods: Prospective cross-sectional study with 28 participants, divided into two groups: healthy volunteers and clinical patients. In both groups, PSG, individual in-ear EEG- with two different electrode configurations- and actigraphic recordings (only in the healthy group) were performed simultaneously for a whole night. Statistical analysis focussed on the four main sleep metrics: TRT (total recording time), TST (total sleep time), SE (sleep efficiency), SL (sleep latency) and the 5-class classifications (wakefulness, N1, N2, N3 and REM sleep). This included correlation analyses between methods and Bland-Altman plots, Cohen’s K coefficient, and confusion matrices aiming 30-second epoch-wise agreement with an automatic sleep classification algorithm using visual sleep classification by an ERSR-certified human expert as the gold standard according to current AASM guidelines. Results: The analysed sleep data comprised 30960 epochs. The correlation analysis revealed strong positive correlations (0.90) for all variables for the in-ear sensor. The Bland-Altman plots show a high level of agreement and consistency (+- 1.87 SD), with minimal bias between methods. The average kappa values (0.75) and the confusion matrices with each method's sensitivity and specificity also show a very high level of concordance.Conclusions: In both groups, the in-ear EEG sensor showed strong correlation, agreement and reliability with the gold standard, supporting accurate sleep classification.