Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.

Diagnosis of sleep disorders is still a challenging issue for a large number of nerve diseases. In this sense, EEG is a powerful tool due to its non-invasive and real-time catacteristics. This modality is being more and more used for diagnosis such as for epilepsy. It is also becoming widely used for many redictive, Preventive and Personalized Medicine (PPPM) applications. To understand sleep disorders, we propose a method to classify EEG signals in order to detect abnormal behaviours that could reflect a specific modification of the sleep pattern. Our method consists of extracting the characteristics based on temporal and spectral analyses with different descriptors. A classification is then performed based on these features. Validation on a public available database show promizing results with high accuracy levels.