EEG Analysis via Multiscale Lempel-Ziv Complexity for Seizure Detection

Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.