A new closed-loop strategy for detection and modulation of epileptiform spikes based on cross approximate entropy

Closed-loop control plays an important role in the treatment of epileptiform spikes by using brain stimulation. In recent years, there have been many analytical methods for determining stimulus protocols and stimulus parameters. However, the analytical method that can start the stimulus protocol when it is needed and stop the stimulus protocol when it is not needed is rather rare. In this work, we design an analytic closed-loop control scheme which can starts control when epileptiform spikes are detected and stops control when no epileptiform spikes are detected. The neural mass model is used to simulate the generation of normal Electroencephalograph signals and epileptiform spikes. The detection of epileptiform spikes is completed via an alarm threshold which is set by using the combination of cross approximate entropy, the Pearson correlation coefficient and the fuzzy theory. If the detection result shows that there are epileptiform spikes in the neural mass model, the fuzzy proportion integration differentiation control works so that the abnormal epileptiform spikes are restored to normal EEG signals, and vice versa. The simulation confirms the effectiveness of the proposed closed-loop control scheme.