Spectro-Temporal EEG Modulation Energy based Diagnosis of Mild Alzheimer’s Disease
Alzheimer disease is the most common and fastest growing cognitive diseases in the United States. Developments of key biomarkers are challenging issues for diagnosis of Alzheimer disease and its progression. Previous research studies have highlighted the key use of EEG signals in diagnosis of several neurodegenerative disorders such as Alzheimer’s, Epilepsy and many more. In present paper, EEG spectro-temporal modulation energy approach is presented for diagnosis of Alzheimer’s disease. Firstly, the multi-channel EEG signal is separated into five bands: delta, theta, alpha, beta and gamma waves. Hilbert transformation is used for computing the temporal amplitude envelope of each band. The rate of modulation of each side band of each EEG signal is measured using modulation energy. These features are computed for 11 referential EEG signals as well as seven bipolar signals. For distinguishing the signal between two groups i.e. healthy control and Alzheimer subjected; Support vector machine classifier is used giving 95.71% accuracy on current database used.
Mattson M., “Pathways towards and away from Alzheimer’s disease”, Nature, Vol. 430, pp. 631–639, Aug.2004.
Justin Dauwels, Francois Vialatte, and Andrzej Cichocki, “Diagnosis of Alzheimer’s disease from EEG Signals: Where Are We Standing?” Current Alzheimer Research, Vol. 7 Issue 6, pp. 487-505, September 2010
McKhann,G., Drachman,D., Folstein, M., Katzman,R., Price,D.,and Stadlan, E.M, “Clinical diagnosis of Alzheimer’s disease”, Neurology vol. 34, pp. 939–939, 1984, doi: 10.1212/WNL.34.7.939.
Nilesh Kulkarni, V.K.Bairagi, “Extracting Salient features for EEG based Diagnosis of Alzheimer’s disease Using Support Vector Machine classifier”, IETE Journal of Research, Taylor and Francis, Vol. 63, Issue. 1, March 2017.
J. Dauwels, K. Srinivasan, M. Ramasubba Reddy, T. Musha, F.-B. Vialatte, C. Latchoumane, J. Jeong, and A. Cichocki, “Slowing and loss of complexity in Alzheimer’s EEG: Two sides of the same coin?” Int. J. Alzheimer’s Dis., pp. 1–12, Apr. 2011. doi:10.4061/2011/539621.
R. Cassani, T. H. Falk, F. J. Fraga, P. A. M. Kanda, and R. Anghinah, “The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer’s disease diagnosis”, Front. Aging Neurosci., Vol. 6, Article 55, pp. 1–13, Mar. 2014.
Dhiya Al-Jumeily, Shamaila Iram, Francois-Benois Vialatte, Paul Fergus, and Abir Hussain, “A Novel Method of Early Diagnosis of Alzheimer’s Disease Based on EEG Signals,” The Scientific World Journal, vol. 2015, Article ID 931387, 11 pages, 2015. doi:10.1155/2015/931387.
Tyler Staudinger, Robi Polikar, “Analysis of Complexity Based EEG Features for Diagnosis of Alzheimer Disease”, in Proc Intl Conf IEEE-EMBC, Boston, USA, pp. 2033 - 2036, 2011.
S. Du, C. Liu, and L. Xi, “A selective multiclass support vector machine ensemble classifier for engineering surface classification using high definition metrology,” ASME Trans. J. Manuf. Sci. Eng., Vol. 137, Feb. 2015. doi:10.1115/1.4028165.
Andrea Rueda, Fabio A. Gonzalez, “Extracting Salient Brain Patterns for imaging based Classification of Neurodegenerative Diseases”, IEEE Trans. Med. Imaging, vol. 33, No. 6, pp. 1262-1274, 2014.
I. Daly, N. Nicolaou, S.J. Nasuto, and Warwick, “Automated artifact removal from the electroencephalogram: A comparative study,” Clin. EEG Neurosci., Vol. 44, pp. 291–306, Oct. 2013.
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