CHARACTERIZATION OF BRAIN ECVT IMAGES AND EEG SIGNALS IN MILD ALZHEIMER’S DISEASE FOR EARLY DETECTION OF ALZHEIMER’S

• Main Author: HANDAYANI (NIM : 30213002), NITA
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/29692
• Institution: Institut Teknologi Bandung

Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by the decreased of memory function and cognitive ability. Alzheimer's disease is caused by the damage of neurons due to the accumulation of beta-amyloid plaque (Aβ) and neurofibrillary tangles (NFTs) within the neurons. This condition causes disruption of the electrical signal transmission process in the brain and changes in the complexity and irregularity of brain signals. Brain signals are biological signals that are non-stationary, have high complexity and dynamic behavior. Early detection of AD is very important so that when AD symptoms appear, it can be immediately given appropriate treatment and therapy. One of the biomarkers used to detect AD is a functional neuroimaging based on electrophysiology signal measurements. The examples of functional brain imaging modalities that use electrophysiology technique are Brain ECVT and EEG. Both modalities have similarity in terms of non-radiative, non-invasive, mobile and inexpensive, so it can be used for screening people with potential AD in large populations. Therefore, the purpose of this study was to observed the functional abnormalities of the brain in AD to obtain characteristics of brain electrical activity based on Brain ECVT images and EEG signals as the basis of early detection of AD. This research consists of two schemes, that is the observation of brain electrical activity using Brain ECVT and Quantitative EEG analysis. The research stages of Brain ECVT include data acquisition process, image reconstruction and ECVT’s image analysis. In this study, the test subjects were consisted of 10 Mild Alzheimer's Disease (Mild AD) and 12 normal elderly subjects as controls. All test subjects were screened using MMSE and MoCA tests to assess their cognitive abilities. The reconstruction method used in the research is ILBP (Iterative Linear Back Projection) and average substraction. The differences in ECVT’s images between the Mild AD groups and the control groups were analyzed based on three image criteria ie SIE (Spatial Image Error), DE (Distribution Error), and CC (Correlation Coefficient) and first-order feature extraction test. Kolmogorov-Smirnov test, a non-parametric statistical test was used to compare gray level distribution of two images statistically with a significance level of 5%. Based on the analysis of image criteria, the average value of SIE, DE and CC were 12.86%, 3.77% and 83.21% for the Mild AD group and 10.42%, 2.80% and 87.04% respectively for the control group. Statistically, there is a significant difference between the ECVT’s image of the Mild AD group and the control group, especially in the 10th slice up to the 25th slice. The second scheme is recording the electrical brain activity using EEG. The research stages in this section include data recording process, EEG signal pre-processing and EEG data analysis. Quantitative EEG (QEEG) methods used for data analysis include FFT and wavelet transform, power spectral analysis with Welch periodogram, brain mapping of power spectral, functional brain connectivity and brain signal complexity analyses. EEG’s data recording used the 14-electrodes Emotiv Epoc with a sampling frequency of 128 Hz. Based on the power spectral analysis, it was found that for the resting state in the Mild AD group there was an increase of the power spectral in the delta (1-4 Hz) and theta (4-7 Hz) frequencies and a decrease of the power spectral in the alpha (7- 13 Hz) and beta (13-30 Hz) frequencies. The study of functional connectivity of the brain in this research used two methods namely linear method (coherence) and non-linear method (synchronization phase). Coherence analysis is divided into two categories which are the intra-hemisphere and inter-hemisphere coherences. Based on the results of the study, in the Mild AD group there was a decrease of intra-hemisphere coherence especially in the temporo-parieto-occipital area and a decrease of inter-hemisphere coherence in the frontal area. This is due to the decreased of cholinergic connectivity in the Mild AD between different of brain areas. Furthermore, EEG signal synchronization analysis is done by calculating the PLV (Phase Locking Value) in the area associated with long cortico-cortical connection for high frequency band that is alpha and beta bands. Based on the calculations, PLV in the Mild AD decreases in all electrode pairs for the beta frequency. This means that the synchronization of EEG signals was weakened so that it can be said that the Mild AD has "loss of beta-band synchronization". The method of analyzing the complexity of EEG signals using two physical quantities are LLE (Largest Lapunov Exponent) and spectral entropy (SpecEn). The brain is considered a dynamic system of chaos because its amplitude changes randomly with time. Based on the analysis results, it was obtained information that the Mild AD group has a lower chaotic level than the control group, observed on electrodes AF3, F7, FC5, P8, T8, F4, F8, and AF4. While the difference in the value of spectral entropy was observed on electrodes P8, F4 and AF4. So it can be said that brain signals in the Mild AD have a low level of complexity and irregularity. Based on the result of this research, it can be concluded that Brain ECVT and EEG modalities can be used as early detection tool of AD. Both of modalities can shows the functional abnormalities of the brain in the Mild AD comprehensively and complement each other. Brain ECVT produces images of electrical activity in the cortico-cortical and intracranial areas of the brain whereas EEG provides information on changes in brain signal characteristics in Mild AD compared to control subjects.