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Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

Healthcare professionals constantly face situations that reflect ecosystems' degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are the following: to characterize and explore the relationship between hope, resilient coping and mental health in Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (retrospective, analytical observational, cross-sectional, descriptive and correlational research design). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M = 43.46, SD = 11.97); 29.3% revealed low resilience (M = 14.93, SD = 4.05); and the average of four of the five Mental Health scores (exception: Severe depression) indicates the probability of a psychiatric case. Hope correlated with Social dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of four of the five GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals' mental health. Prior research (e.g., on therapies to enhance hope, resilience and, thus, mental health), to which the current study contributes, supports optimism towards the necessary internal sustainability transition. [ABSTRACT FROM AUTHOR]

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