Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals.

Marta Borowska

doi:10.3390/e23070832

Abstract

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.

Highlights

Electroencephalography (EEG) is a non-invasive procedure to measure the bioelectric activity of the brain
Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%
Electroencephalographic signals from epilepsy patients are of interest to researchers in particular

Summary

Introduction

Electroencephalography (EEG) is a non-invasive procedure to measure the bioelectric activity of the brain. Electroencephalographic signals from epilepsy patients are of interest to researchers in particular. If an appropriate diagnosis and treatment are made, 70% of the patients with disease can live without seizures. In 2017, the current seizure classification was published [2], which includes the division into focal seizures, generalized seizures, and seizures with unknown onset. In the focal-onset seizures category, there are seizures with and without the state of consciousness, with the coexistence of motor symptoms and the development of focal seizures to bilateral tonic-clonic seizures. In the case of seizures with unknown onset, there are seizures with or without the coexistence of motor symptoms as well as unclassified seizures. The new classification noted that some tonic and flexion seizures may have a focal onset.

Methods

Results

Conclusion