A Study on Signal Complexity Measurement for Epileptic Seizure Detection

Epilepsy is a sudden brain malfunction that represented by an exaggerated, high, and hypersynchronous neuron activity inside the brain. Electroencephalogram (EEG) is a tool that can be used to measure the electrical changes of the brain by using electrodes placed on the scalp. An ictal pattern in the EEG signal can be used to identify seizure condition. Neurologist search for the ictal state by determining slow, spike, and sharp wave in the EEG recording manually. However, the manual method is very vulnerable to human errors. That is why many research has been conducted to develop an automatic seizure detection system. Since the EEG signal is a nonlinear, nonstationary, and chaotic, it is considered as a complex signal. This paper discusses a brief study on signal complexity measurement in the EEG signal in the term of epileptic seizure detection. Complexity measurement based on statistic, entropy, and chaos analysis is explored in six processing domain.