Mobile smart helmet for brain stroke early detection through neural network-based signals analysis

The treatments for brain stroke are strongly timedependent. The medical literature highlights the need of a quick diagnosis in order to guarantee the most effective therapy. An important target for strokes is trying to achieve a Door-to-Needle (DTN) time of less than 60 minutes, which is called Golden Hour [1]. This paper proposes a mobile Smart Helmet (SH) thought to be worn by a patient when the first aid medical team arrives and the aim is to efficiently recognize and detect a brain stroke, on site. While similar solutions in the literature employ the (usually computationally heavy) electromagnetic field inversion problem and image analysis, the proposal of this paper is an NN-based SH. It uses signal analysis to recognize the presence of a stroke with a limited computational burden. In the reported preliminary experiments, carried out via simulations, we have employed a MultiLayer Perceptron (MLP) model that implements a 4-layer NN. Numerical results show that proposed signal analysis, applied to a single brain model, is able to efficiently detect the stroke presence with an accuracy around 90%.