Cognitive radio system for interference reduction in BANETs focused on epilepsy diagnosis

Epilepsy is considered a disorder in which a person has episodes of disturbed brain activity characterized by increased and abnormal synchronization of electrical neuronal activity and this electrical behavior is manifested with physical changes in the overall behavior of the body. As such, epilepsy can be diagnosed analyzing the electroencephalographic changes and their effects monitoring the other body signals. There are different clinical tools that allow monitoring physiological signals associated with a possible epileptic episode. However, they require wired connections and therefore monitoring is restricted to factors such as limited physical movements and loss of information when sensors are disconnected. This work proposes and designs a body area network (BANET) for continuous monitoring and event detection and we study the system behavior for different parameters associated with the transmission of information. The continuous monitoring signals are form the electroencephalogram (EEG) and electrocardiogram (ECG) sensors since these signals provide the most important data for the epilepsy detection while the event detection system is composed mainly by the electrogastrogram (EGG) sensors but other sensors are also considered. Also, we propose a system to reduce interference and energy consumption by making a more efficient use of the channels using a cognitive radio system. This system is mathematically studied.