Analysis of Biomedical Signals by the Lempel-Ziv Complexity: the Effect of Finite Data Size

Author

Jing Hu ; Jianbo Gao ; Principe, J.C.

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL

Volume

53

Issue

12

fYear

2006

Firstpage

2606

Lastpage

2609

Abstract

The Lempel-Ziv (LZ) complexity and its variants are popular metrics for characterizing biological signals. Proper interpretation of such analyses, however, has not been thoroughly addressed. In this letter, we study the the effect of finite data size. We derive analytic expressions for the LZ complexity for regular and random sequences, and employ them to develop a normalization scheme. To gain further understanding, we compare the LZ complexity with the correlation entropy from chaos theory in the context of epileptic seizure detection from EEG data, and discuss advantages of the normalized LZ complexity over the correlation entropy

Keywords

chaos; diseases; electroencephalography; entropy; medical signal processing; EEG; Lempel-Ziv complexity; biomedical signal analysis; chaos theory; correlation entropy; epileptic seizure detection; finite data size; normalization; random sequences; regular sequences; Bars; Biomedical measurements; Chaos; Electroencephalography; Entropy; Epilepsy; Length measurement; Random sequences; Signal analysis; Biomedical signal analysis; Lempel-Ziv complexity; epileptic seizure detection; Algorithms; Artificial Intelligence; Biomedical Engineering; Diagnosis, Computer-Assisted; Electroencephalography; Humans; Pattern Recognition, Automated; Sample Size; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2006.883825

Filename

4015609