FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.

Abstract

Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.