Mutual information entropy research on dementia EEG signals

Proceedings - The Fourth International Conference on Computer and Information Technology (CIT 2004)

Volumn , Issue , 2004, Pages 885-889

  Hongzhi, Q I ^a   Wan, Baikun ^a   Zhao, Li ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Alzheimer's disease; Approximate entropy; EEG; Information entropy; Mutual entropy

Indexed keywords

COMPUTATIONAL COMPLEXITY; DIAGNOSIS; DISEASES; ELECTROENCEPHALOGRAPHY; MORPHOLOGY; NONLINEAR SYSTEMS; SIGNAL PROCESSING; STATISTICAL METHODS; APPROXIMATION THEORY; ENTROPY; INFORMATION ANALYSIS; PARAMETER ESTIMATION; PROBABILITY;

ALZHEIMER'S DISEASES; APPROXIMATE ENTROPY; INFORMATION ENTROPY; MUTUAL ENTROPY; ALZHEIMER'S DISEASE (AD);

INFORMATION ANALYSIS; ELECTROENCEPHALOGRAPHY;

EID: 10444223107     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bird T.D. Alzheimer's disease and other primary dementias. In: Fauci AS, Braunwald E, Isselbacher KJ, et al, eds. Harrison's Principles of Internal Medicine. 14th ed. New York, NY: McGraw-Hill. PP: 2348-2352, 1998.
2. Schreiter-Gasser U, Gasser Th, Ziegler P. Quantitative EEG-analysis in early onset Alzheimer's disease: a controlled study. Electroenceph clin Neurophysiol, 86:15-22, 1993;
3. Schreiter-Gasser U, Gasser T, Ziegler P. Quantitative EEG analysis in early onset Alzheimer's disease: correlations with severity, clinical characteristics, visual EEG and CCT. Electroenceph Clin Neurophysiol. 90(4): 267-272, 1994.
4. Davide V. Moretti, Claudio Babiloni, Giuliano Binetti, Emanuele Cassetta, Individual analysis of EEG frequency and band power in mild Alzheimer's disease, Clinical Neurophysiology. 115:299-308, 2004.
5. Bennys K, Rondouin G, Bergnes C et al. Diagnosis value of quantitative EEG in Alzheimer's disease, Clinical Neurophysiology. 31:153-160, 2001.
6. Jelles B., van Birgelen J., Slaets J., Hekster R., Jonkman E. y Stam C. Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls. Clinical Neurophysiology. 110: 1159-67, 1999.
7. Khinchin A. I. Mathematical Foundations of Information Theory. New York: Dover, 1957.
8. Vastino J. A., Swinney H. L. Information transport in spatial-temporal system. Physical Review Letters. 60:1773-1776, 1988.
9. Chen, F., Xu, J. H., Gu, F. J., Yu, X. H., Meng, X., Qiu, Z. C. Dynamic process of information transmission complexity in human brains. Biol. Cybern. 83:355-366, 2000.
10. S.M. Pincus. Heart rate control in normal and aborted SIDS infants. Am. J. Physiol. (Regulatory Integrative Comp. Physiol.), PP: R638-R646, 1991.
11. S.M. Pincus and D.L. Keefe. quantification of hormone pulsatility via an approximate entropy algorithm. Am. J. Physiol. 262: E741-E754, 1991.
12. Radhakrishnan N. and Gangadhar, BN. Estimating regularity in epileptic seizure time-series data. IEEE Engineering in Medicine and Biology. 89-94,1998.
13. S.M. Pincus. Assessing Serial Irregularity and Its Implications for Health. Ann. NY Acad. Sci. 954:245-267, 2001.
14. S.M. Pincus. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88:2297-2301, 1991.
15. Olkkonen H, Pesola P, Olkkonen JT, Valjakka A, Tuomisto L. EEG noise cancellation by a subspace method based on wavelet decomposition, Med. Sci. Monit. 8(11):199-204, 2002.
16. Abásolo, D., Hornero, R., Espino, P., Alonso, A., de la Rosa, R. Electroencephalogram Analysis with Approximate Entropy to Help in the Diagnosis of Alzheimer's Disease. Proc. of the 4th Annual IEEE Conf. on Information Technology Application in Biomedicine, UK, 2003.