Juan P Amezquita-Sanchez1, Nadia Mammone2, Francesco C Morabito3, Silvia Marino2, Hojjat Adeli4. 1. Autonomous University of Queretaro (UAQ), Faculty of Engineering, Departments Biomedical and Electromechanical, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P. 76807, San Juan del Río, Qro., Mexico. 2. IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, 98124, Messina, Italy. 3. Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy. 4. Departments of Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43220, USA. Electronic address: adeli.1@osu.edu.
Abstract
BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.
BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 ADpatients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and ADpatients with an accuracy of 90.3%.
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Authors: Olivia Vargas-Lopez; Juan P Amezquita-Sanchez; J Jesus De-Santiago-Perez; Jesus R Rivera-Guillen; Martin Valtierra-Rodriguez; Manuel Toledano-Ayala; Carlos A Perez-Ramirez Journal: Sensors (Basel) Date: 2019-12-18 Impact factor: 3.576