Abstract
Alzheimer’s Disease (AD) is considered one of the most common form of dementia; it involves a progressive decline in cognitive function because of pathological modifications or damage of the brain. One of the major challenges is to develop tools for early diagnosis and disease progression. Electroencephalogram represents potentially a noninvasive and relatively non-expensive approach for screening of dementia and AD. It provides a method to objectively quantify the cortical activation patterns but it is usually considered insensitive in the early AD. This study introduces a novel method where electroencephalographic recordings (EEG) are subjected to Empirical Mode Decomposition (EMD), which decomposes a signal into components known as Intrinsic Mode Functions (IMFs). The results, suggest that, the IMFs may be used to determine the particular frequency bandwidths in which specific phenomena occur.
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References
Jeong, J.: EEG Dynamics in patients with Alzheimer’s disease. Clinical Neurophysiology 115, 1490–1505 (2004)
Delbeuck, X., van Der Linden, M., Collette, F.: Alzheimer’s Disease as a disconnection syndrome, 121, 1438–1446 (2003)
Fouquet, et al.: Cerebral imaging and physiopathology of Alzheimer’s disease. Psychol Neuropsychiatr Vieil 5, 269–279 (2007)
Goldeberger, A.L., Amaral, L.A.N., Hausdorff, J.M., Ivanov, P.C., Peng, C.K., Stanley, H.E.: Fractal dynamics in physiology: Alterations with disease and aging. Nat. Acad. Sci. 99, 2466–2472 (2002)
Dauwels, J., Vialatte, F., Latchoumane, C., Jeong, J., Cichocki, A.: EEG synchrony analysis for early diagnosis of Alzheimer’s disease: a study with several synchrony measures and EEG data sets. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 2224–2227 (2009)
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), doi:10.3390/e14071186
Inuso, G., La Foresta, F., Mammone, N., Morabito, F.C.: Brain activity investigation by EEG processing: Wavelet analysis, kurtosis and Renyi’s entropy for artifact detection. In: Proceedings of the 2007 International Conference on Information Acquisition, ICIA 2007, Jeju City, South Korea, pp. 195–200 (2007), doi:10.1109/ICIA.2007.4295725
Azzerboni, B., Finocchio, G., Ipsale, M., La Foresta, F., McKeown, M.J., Morabito, F.C.: Spatio-temporal analysis of surface electromyography signals by independent component and time-scale analysis. In: Proceedings of The Annual International Conference of the IEEE Engineering in Medicine and Biology, vol. 1, pp. 112–113 (2002)
Calcagno, S., La Foresta, F., Versaci, M.: Independent component analysis and discrete wavelet transform for artifact removal in biomedical signal processing. American Journal of Applied Sciences 11(1), 57–68 (2014)
Mammone, N., Inuso, G., La Foresta, F., Versaci, M., Morabito, F.C.: Clustering of entropy topography in epileptic electroencephalography. Neural Computing and Applications 20(6), 825–833 (2011)
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)
Vialatte, F.B., Cichocki, A., Dreyfus, G., Musha, T., Shishkin, S.L., Gervais, R.: Early Detection of Alzheimer’s Disease by Blind Source Separation, Time Frequency Representation, and Bump Modeling of EEG Signals. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 683–692. Springer, Heidelberg (2005)
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, Article number 6552994 (2013)
Labate, D., La Foresta, F., Palamara, I., Morabito, G., Bramanti, A., Zhang, Z., Morabito, F.C.: EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease. In: Bassis, S., Esposito, A., Morabito, F.C. (eds.) Recent Advances of Neural Networks Models and Applications. SIST, vol. 26, pp. 163–173. Springer, Heidelberg (2014)
Labate, D., La Foresta, F., Occhiuto, G., Morabito, F.C., Lay-Ekuakille, A., Vergallo, P.: Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: A comparison. IEEE Sensors Journal 13(7), 2666–2674 (2013), doi:10.1109/JSEN.2013.2257742
Mandic, D., Souretis, G., Leong, W.Y., Looney, D., Van Hulle, M.M., Tanaka, T.: Complex Empirical Mode Decomposition for Multichannel Information Fusion. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion, pp. 243–260 (2008)
Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc. Royal Soc. London A 454, 903–995 (1998)
Campolo, M., Labate, D., La Foresta, F., Morabito, F.C., Lay-Ekuakille, A., Vergallo, P.: ECG-derived respiratory signal using Empirical Mode Decomposition. In: Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA 2011), article number 5966727 (2011)
Rutkowski, T.M., Cichocki, A., Tanaka, T., Ralescu, A.L., Mandic, D.P.: Clustering of Spectral Patterns Based on EMD Components of EEG Channels with Applications to Neurophysiological Signals Separation. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 453–460. Springer, Heidelberg (2009)
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Labate, D., La Foresta, F., Morabito, G., Palamara, I., Morabito, F.C. (2015). On the Use of Empirical Mode Decomposition (EMD) for Alzheimer’s Disease Diagnosis. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_12
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DOI: https://doi.org/10.1007/978-3-319-18164-6_12
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