Title :
Complexity analysis of electroencephalogram signal based on Jensen-Shannon divergence
Author :
Lingjun Gong ; Jun Wang
Author_Institution :
Coll. of Telecomm & Inf. Eng, Nanjing Univ. of Posts & Telecomm, Nanjing, China
Abstract :
In this paper, complexity measure based on Jensen-Shannon Divergence was used to compute statistical complexity of the electroencephalogram signals, which include the electroencephalogram of younger and elder subjects from Nanjing General Hospital of Nanjing Military Command. The results show that two groups of signals have different statistical complexity measures. The electroencephalogram of elder subjects has the higher statistical complexity. The independent samples T test indicated that above-mentioned analysis could disclose significant differences among these two signals´ complexity. It is demonstrated that statistical complexity based on Jensen-Shannon Divergence could effectively distinguish the electroencephalogram in 2 various age groups.
Keywords :
computational complexity; electroencephalography; medical signal processing; statistical analysis; Jensen-Shannon divergence; Nanjing General Hospital; Nanjing Military Command; complexity analysis; electroencephalogram signals; independent sample T test; signal complexity; signal groups; statistical complexity measures; Algorithm design and analysis; Complexity theory; Dynamic range; Electroencephalography; Probability distribution; Standards; Jensen-Shannon Divergence; complexity;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-2760-9
DOI :
10.1109/BMEI.2013.6746937