Performance analysis of the FB-PLL applied to real time monitoring of deep sleep brainwaves

Repose is indispensable for the human body, one of its main functions being to replenish energy. During sleep, the body's important restorative activities are performed by the organism, which is a basic human need. With awareness of the dimension that sleep represents and the need to improve its quality, the importance of monitoring in real time becomes evident. Based on this, the fast fourier transform is a technique capable of obtaining characteristics of a signal, such as its frequency, as well as being able to be applied to sleep waves. However, to have a good use, a considerable number of sample experimental cycles is necessary, which demands a certain computational effort. In this sense, this work we sought to apply the Phase-Locked Loop technique. Thus, a Fourier-based synchronization algorithm is proposed to track low-frequency brain waves during sleep. It is also worth noting that this method is capable of performing the estimates in just one cycle. Therefore, the objective is to obtain the estimated curve referring to the frequency and an efficient synchronization algorithm that is able to track deep sleep in real time.