EEG complexity modifications and altered compressibility in mild cognitive impairment and Alzheimer's Disease

Smart Innovation, Systems and Technologies

Volumn 26, Issue , 2014, Pages 163-173

  Labate, Domenico ^a,b   La Foresta, Fabio ^a   Palamara, Isabella ^a   Morabito, Giuseppe ^c   Bramanti, Alessia ^d   Zhang, Zhilin ^e   Morabito, Francesco C ^a  

^a UNIVERSITY MEDITERRANEA OF REGGIO CALABRIA   (Italy)

^b UNIVERSITY OF CALABRIA   (Italy)

^c UNIVERSITY OF PAVIA   (Italy)

^d IRCCS CENTRO NEUROLESI BONINO PULEJO   (Italy)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Alzheimer's Disease; Compressive sensing; EEG; Permutation Entropy

Indexed keywords

ALZHEIMER'S DISEASE; BACKGROUND DIFFERENCES; COMPRESSIVE SENSING; EXPERIMENTAL DATABASE; HEALTHY CONTROLS; MILD COGNITIVE IMPAIRMENTS; NONLINEAR INDEX; PERMUTATION ENTROPY;

COMPRESSIBILITY; ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; ENTROPY; NEURAL NETWORKS; SIGNALING;

NEURODEGENERATIVE DISEASES;

EID: 84897831474     PISSN: 21903018     EISSN: 21903026     Source Type: Book Series    
DOI: 10.1007/978-3-319-04129-2_17     Document Type: Conference Paper

References (16)

1. Weiner, M.W., et al.: The Alzheimer's Disease Neuroimaging Iniziative: A review. Alzheimer's & Dementia 8, S1-S68 (2012)
2. Jeong, J.: EEG Dynamics in patients with Alzheimer's disease. Clinical Neurophysiology 115, 1490-1505 (2004)
3. Bandt, C., Pompe, B.: Permutation entropy - a natural complexity measure for time series. Physical Review Lett. 88, 174102 (2002)
4. Dauwels, J., Srinivasan, K., et al.: Slowing and loss of complexity in Alzheimer's EEG: two sides of the same coin? Intl. J. of Alzheimer's Disease (2011)
5. Labate, D., La Foresta, F., Morabito, G., Palamara, I., Morabito, F.C.: Entropic Measures of EEG Complexity in Alzheimer's Disease through a Multivariate Multiscale Approach. IEEE Sensors Journal 13(9), 3284-3292 (2013)
6. Morabito, F.C., Labate, D., Bramanti, A., La Foresta, F., Morabito, G., Palamara, I., Szu, H.H.: Enhanced Compressibility of EEG Signal in Alzheimer's Disease Patients. IEEE Sensors Journal 13(9), 3255-3262 (2013)
7. Zhang, Z., Jung, T.P., Makeig, S., Rao, B.D.: Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware. IEEE Trans. Biomed. Eng. 60(1), 221-224 (2013)
8. Cands, E.J., Wakin, M.B.: An introduction to compressive sensing. IEEE Signal Processing Magazine 25(2), 14-20 (2008)
9. Donoho, D.: Compressed sensing. IEEE Trans. Inform Theory 52(4), 1289-1306 (2006)
10. Zhang, Z., Rao, B.D.: Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Trans. on Signal Processing 61(8), 2009-2015 (2013)
11. Tipping, M.: Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211-244 (2001)
12. Morabito, F.C., Labate, D., La Foresta, F., Bramanti, A., Morabito, G., Palamara, I.: Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG. Entropy 14(7), 1186-1202 (2012)
13. Keller, K., Sinn, K.: Ordinal analysis of time series. Physica A 356, 114-120 (2005)
14. Szu, H., et al.: Smartphone household wireless electroencephalogram hat. Applied Computational Intelligence and Soft Computing, ID 241489 (2013)
15. Mammone, N., La Foresta, F., Morabito, F.C.: Automatic artifact rejection from multichannel scalp by EEG by wavelet ICA. IEEE Sensors Journal 12(3), 533-542 (2012)
16. Wang, Z., Bovik, A.: Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE Signal Processing Magazine 26(1), 98-117 (2009)