Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abásolo D, Hornero R, Espino P et al (2005) Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clin Neurophysiol 116:1826–1834. doi: 10.1016/j.clinph.2005.04.001
Abásolo D, Hornero R, Espino P et al (2006) Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol Meas 27:241–253. doi: 10.1088/0967-3334/27/3/003
Abásolo D, Hornero R, Gómez C et al (2006) Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med Eng Phys 28:315–322. doi: 10.1016/j.medengphy.2005.07.004
Alonso JF, Mañanas MA, Hoyer D et al (2007) Evaluation of respiratory muscles activity by means of mutual information function at different levels of ventilatory effort. IEEE Trans Biomed Eng 54:1573–1582. doi: 10.1109/TBME.2007.893494
Andrzejak RG, Lehnertz K, Moormann F et al (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E Stat Nonlin Soft Matter Phys 64:061907. doi: 10.1103/PhysRevE.64.061907
Babloyantz A, Destexhe A (1988) The Creutzfeldt-Jakob disease in the hierarchy of chaotic attractors. In: Markus M, Müller S, Nicolis G (eds) From chemical to biological organization. Springer, Berlin, pp 307–316
Bird TD (2001) Alzheimer’s disease and other primary dementias in Harrison’s principles of internal medicine. In: Braunwald E, Fauci AS, Kasper DL et al (eds) The McGraw-Hill Companies Inc., New York, pp 2391–2399
Bruhn J, Röpcke H, Rehberg B et al (2000) Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect. Anesthesiology 93:981–985. doi: 10.1097/00000542-200010000-00018
Costa M, Goldberger AL, Peng CK (2005) Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 71:021906. doi: 10.1103/PhysRevE.71.021906
David O, Cosmelli D, Friston KJ (2004) Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21:659–673. doi: 10.1016/j.neuroimage.2003.10.006
De Lucia M, Fritschy J, Dayan P et al (2008) A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. Med Biol Eng Comput 46:263–272. doi: 10.1007/s11517-007-0289-4
Eckmann JP, Ruelle D (1992) Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems. Physica D 56:185–187. doi: 10.1016/0167-2789(92)90023-G
Escudero J, Abásolo D, Hornero R et al (2006) Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiol Meas 27:1091–1106. doi: 10.1088/0967-3334/27/11/004
Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198. doi: 10.1016/0022-3956(75)90026-6
Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140. doi: 10.1103/PhysRevA.33.1134
Goldberger AL, Peng CK, Lipsitz LA (2002) What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 23:23–26. doi: 10.1016/S0197-4580(01)00266-4
Gómez C, Hornero R, Abásolo D et al (2007) Analysis of the magnetoencephalogram background activity in Alzheimer’s disease patients with auto mutual information. Comput Methods Program Biomed 87:239–247. doi: 10.1016/j.cmpb.2007.07.001
Hesse CW, James CJ (2007) Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure source scalp topographies. Med Biol Eng Comput 45:909–916. doi: 10.1007/s11517-006-0103-8
Hornero R, Aboy M, Abásolo D et al (2005) Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng 52:1671–1680. doi: 10.1109/TBME.2005.855722
Hoyer D, Pompe B, Chon KH et al (2005) Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales. IEEE Trans Biomed Eng 52:584–592. doi: 10.1109/TBME.2005.844023
Hoyer D, Friedrich H, Frank B et al (2006) Autonomic information flow improves prognostic impact of task force HRV monitoring. Comput Methods Program Biomed 81:246–255. doi: 10.1016/j.cmpb.2006.01.002
Huang L, Yu P, Ju F et al (2003) Prediction of response to incision using the mutual information of electroencephalogram during anaesthesia. Med Eng Phys 25:321–327. doi: 10.1016/S1350-4533(02)00249-7
Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 115:1490–1505. doi: 10.1016/j.clinph.2004.01.001
Jeong J, Chae JH, Kim SY et al (2001) Nonlinear dynamic analysis of the EEG in patients with Alzheimer’s disease and vascular dementia. J Clin Neurophysiol 18:58–67. doi: 10.1097/00004691-200101000-00010
Jeong J, Gore JC, Peterson BS (2001) Mutual information analysis of the EEG in patients with Alzheimer’s disease. Clin Neurophysiol 112:827–835. doi: 10.1016/S1388-2457(01)00513-2
Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge
Lehnertz K, Mormann F, Kreuz T et al (2003) Seizure prediction by nonlinear EEG analysis. IEEE Eng Med Biol 22:57–63. doi: 10.1109/MEMB.2003.1191451
Markand ON (1990) Organic brain syndromes and dementias. In: Daly DD, Pedley TA (eds) Current practice of clinical electroencephalography. Raven Press, New York, pp 401–423
Mendez MO, Bianchi AM, Montano N et al (2008) On arousal from sleep: time–frequency análisis. Med Biol Eng Comput 46:341–351. doi: 10.1007/s11517-008-0309-z
Na SH, Jin SH, Kim SY et al (2002) EEG in schizophrenic patients: mutual information analysis. Clin Neurophysiol 113:1954–1960. doi: 10.1016/S1388-2457(02)00197-9
Palacios M, Friedrich H, Götze C et al (2007) Changes of autonomic information flow due to idiopathic dilated cardiomyopathy. Physiol Meas 28:677–688. doi: 10.1088/0967-3334/28/6/006
Palus M (1996) Coarse-grained entropy rates for characterization of complex time series. Physica D 93:64–77. doi: 10.1016/0167-2789(95)00301-0
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301. doi: 10.1073/pnas.88.6.2297
Pincus SM (2001) Assessing serial irregularity and its implications for health. Ann N Y Acad Sci 954:245–267
Pincus SM, Goldberger AL (1994) Physiological time series analysis: what does regularity quantify? Am J Physiol Heart Circ Physiol 266:H1643–H1656
Pincus SM, Keefe DL (1992) Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol Endocrinol Metab 262:E741–E754
Pompe B (1993) Measuring statistical dependencies in a time series. J Stat Phys 73:587–610. doi: 10.1007/BF01054341
Pompe B, Blidh P, Hoyer D et al (1998) Using mutual information to measure coupling in the cardiorespiratory system. IEEE Eng Med Biol 17:32–39. doi: 10.1109/51.731318
Pritchard WS, Duke DW, Coburn KL et al (1994) EEG-based neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear EEG measures. Electroencephalogr Clin Neurophysiol 91:118–130. doi: 10.1016/0013-4694(94)90033-7
Radhakrishnan N, Gangadhar BN (1998) Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. IEEE Eng Med Biol 17:89–94. doi: 10.1109/51.677174
Röschke J, Fell J, Beckmann P (1995) Non-linear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent. Psychiatry Res 56:257–269. doi: 10.1016/0165-1781(95)02562-B
Rossor M (2001) Alzheimer’s disease. In: Donaghy M (ed) Brain’s diseases of the nervous system. Oxford University Press, Oxford, pp 750–754
Selkoe DJ (1994) Cell biology of the amyloid beta-protein precursor and the mechanism of Alzheimer’s disease. Annu Rev Cell Biol 10:373–403. doi: 10.1146/annurev.cb.10.110194.002105
Stam CJ, Jelles B, Achtereekte HAM et al (1995) Investigation of EEG non-linearity in dementia and Parkinson’s disease. Electroencephalogr Clin Neurophysiol 95:309–317. doi: 10.1016/0013-4694(95)00147-Q
Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116:2266–2301. doi: 10.1016/j.clinph.2005.06.011
Varma NK, Kushwaha R, Beydoun A et al (1997) Mutual information analysis and detection of interictal morphological differences in interictal epileptiform discharges of patients with partial epilepsies. Electroencephalogr Clin Neurophysiol 103:426–433. doi: 10.1016/S0013-4694(97)00039-4
Vastano JA, Swinney HL (1988) Information transport in spatiotemporal systems. Phys Rev Lett 60:1773–1776. doi: 10.1103/PhysRevLett.60.1773
Xu J, Liu ZR, Liu R et al (1997) Information transformation in human cerebral cortex. Physica D 106:363–374. doi: 10.1016/S0167-2789(97)00042-0
Zhang XS, Roy RJ (2001) Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng 48:312–323. doi: 10.1109/10.914794