Convolutional Neural Network for Drowsiness Detection Using EEG Signals

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.

Imaging features of spinal epidural cavernous malformations

Cavernous angioma or cavernoma is a vascular malformation that may affect any area in the neuraxis. Epidural location is very rare and therefore seldom considered in the differential diagnosis of spinal cord compression. We report two cases of epidural cavernous angiomas. The first case is a solitary and purely epidural dorsal cavernous angioma without foraminal expansion or bone modification causing spinal cord compression in a 35 year old woman. The second case is a solitary epidural dorsal cavernous angioma with foraminal extension causing spinal cord compression in a 56 year old woman. Histological confirmation is available for both cases. We describe the MRI features of this lesion insisting on its differential diagnosis on imaging.