A novel method of early diagnosis of Alzheimer's disease based on EEG signals.

Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer's disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test) to compare the results

Early Detection of Neurodegenerative Diseases from Bio-Signals: A Machine Learning Approach

Given the fact that people, especially in advanced countries, are living longer due to the advancements in medical sciences which resulted in the prevalence of age-related diseases like Alzheimer’s and dementia. The occurrence of such diseases continues to increase and ultimately the cost of caring for these groups will become unsustainable. Addressing this issue has reached a critical point and failing to provide a strategic way forward will negatively affect patients, national health services and society as a whole.Three distinctive development stages of neurodegenerative diseases (Retrogenesis, Cognitive Impairment and Gait Impairment) motivated me to divide this research work into two main parts. To fully achieve the purpose of early detection/diagnosis, I aimed at analysing the gait signals as well as EEG signals, separately, as both of these signals severely get affected by any neurological disease.The first part of this research work focuses on the discrimination analysis of gait signals of different neurodegenerative diseases (Parkinson’s, Huntington, and Amyotrophic Lateral Sclerosis) and also of control subjects. This involves relevant feature extraction, solving the issues of imbalanced datasets and missing entries and lastly classification of multiclass datasets. For the classification and discrimination of gait signals, eleven (11) classifiers are selected representing linear, non-linear and Bayes normal classification techniques. Results revealed that three classifiers have provided us with higher accuracy rate which are UDC, LDC and PARZEN with 65%, 62.5% and 60% accuracy, respectively. Further, I proposed and developed a new classifier fusion strategy that combined classification algorithms with combining rules (voting, product, mean, median, maximum and minimum). It generates better results and classifies subjects more accurately than base-level classifiers.The last part of this research work is based on the rectification and computation of EEG signals of mild Alzheimer’s disease patients and control subjects. To detect the perturbation in EEG signals of Alzheimer’s patients, three neural synchrony measurement techniques; phase synchrony, magnitude squared coherence and cross correlation are applied on three different databases of mild Alzheimer’s disease (MiAD) patients and healthy subjects. I have compared right and left temporal parts of brain with rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s. Two novel methods are proposed to compute the neural synchronization of the brain; Average synchrony measure and PCA based synchrony measure. These techniques are evaluated for three different datasets of MiAD patients and control subjects using the Wilcoxon ranksum test (Mann-Whitney U test). Results demonstrated that PCA based method helped us to find more significant features that can be used as biomarkers for the early diagnosis of Alzheimer’s

Investigation of Multi-dimensional Tensor Multi-task Learning for Modeling Alzheimer's Disease Progression

Machine learning (ML) techniques for predicting Alzheimer's disease (AD) progression can significantly assist clinicians and researchers in constructing effective AD prevention and treatment strategies. The main constraints on the performance of current ML approaches are prediction accuracy and stability problems in medical small dataset scenarios, monotonic data formats (loss of multi-dimensional knowledge of the data and loss of correlation knowledge between biomarkers) and biomarker interpretability limitations. This thesis investigates how multi-dimensional information and knowledge from biomarker data integrated with multi-task learning approaches to predict AD progression. Firstly, a novel similarity-based quantification approach is proposed with two components: multi-dimensional knowledge vector construction and amalgamated magnitude-direction quantification of brain structural variation, which considers both the magnitude and directional correlations of structural variation between brain biomarkers and encodes the quantified data as a third-order tensor to address the problem of monotonic data form. Secondly, multi-task learning regression algorithms with the ability to integrate multi-dimensional tensor data and mine MRI data for spatio-temporal structural variation information and knowledge were designed and constructed to improve the accuracy, stability and interpretability of AD progression prediction in medical small dataset scenarios. The algorithm consists of three components: supervised symmetric tensor decomposition for extracting biomarker latent factors, tensor multi-task learning regression and algorithmic regularisation terms. The proposed algorithm aims to extract a set of first-order latent factors from the raw data, each represented by its first biomarker, second biomarker and patient sample dimensions, to elucidate potential factors affecting the variability of the data in an interpretable manner and can be utilised as predictor variables for training the prediction model that regards the prediction of each patient as a task, with each task sharing a set of biomarker latent factors obtained from tensor decomposition. Knowledge sharing between tasks improves the generalisation ability of the model and addresses the problem of sparse medical data. The experimental results demonstrate that the proposed approach achieves superior accuracy and stability in predicting various cognitive scores of AD progression compared to single-task learning, benchmarks and state-of-the-art multi-task regression methods. The proposed approach identifies brain structural variations in patients and the important brain biomarker correlations revealed by the experiments can be utilised as potential indicators for AD early identification