Thesis Summary

This thesis has presented a framework for the early detection of neurodegenerative diseases using signal processing and signal classification techniques. As we have previously discussed that a NDD starts with the deterioration of short and long term memory of the subjects due to abnormal brain functionality. This kind of perturbation in the brain is considered an initial symptom for the progression of a neurodegenerative disease. Moreover, a healthy gait pattern requires a direct input from neurological system of the brain. Any disturbance in the brain has direct impact on the gait patterns of a person. So an abnormal gait pattern in these patients is considered a final symptom of any neurological disease.

To keep things in a normal sequence and to make them easily understandable, we have divided our project into two main parts—classification of gait signals to discriminate between different NDDs (Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis) and analysis of EEG signals to compute neural synchronization of MiAD patients. The first part of the project is completed using an online database repository called “Physionet” while the processing and analysis of EEG of MiAD and healthy subjects has been done in France at “SIGMA Laboratory”.

Machine learning approach has been selected to complete this project. A set of eleven classification algorithms is implemented and evaluated using PRTools in Matlab for the gait pattern recognition. A comparison of various evaluation techniques is provided, based on visualization and statistical analysis, which helps us to understand the difference and importance of different performance evaluation techniques. In the second half of the thesis we have presented a novel idea of combining base-level classifiers to increase the percentage of classification accuracy. Lastly, we have presented the computation of neural synchronization of the EEG signals for MiAD and control subjects to determine the significant features that can help the clinicians to diagnose Alzheimer’s at its earlier stage.

The chapter wise summaries of the whole thesis with their derived conclusions are provided below:

Chapter 1 outlined the potential issues of NDDs along with the challenges related to machine learning. We argued here, the potential of machine learning approaches to provide significant improvements in the early diagnosis of NDDs. It provided a brief introduction of the methods we have proposed in this research work. Finally, it outlined research aims and novel contributions of the thesis.

Chapter 2 presented detail information about neurodegenerative diseases and a description about their development stages with their probable symptoms. It provided background and preliminary information about signal processing and signal classification. Matters like feature extraction, feature selection and feature classification are discussed in this chapter. In addition to NDDs and signal processing, we have discussed supervised machine learning approach in detail. We have discussed logic based classifiers, artificial neural networks, and statistical learning algorithms.

Chapter 3 presented our proposed strategic framework for the early detection of neurodegenerative diseases and discussed each of its components, i.e. the data collection, data preprocessing and data classification and decision making. It provided information about the tools (PRTools, Statistical, Communication and Signal Processing tool) that are selected for this particular project and the reasons behind their selection.

In Chapter 4, we demonstrated the assessment of gait signals. In this chapter we have discussed the problems with imbalanced datasets, missing data entries, multiclass pattern recognition, and discrimination among similar diseases along with their possible solutions. A set of eleven statistical learning algorithms has been selected to process the gait signals of 16 CO, 15 PD, 20 HD, and 13 ALS subjects. They belonged to normal density based classifiers, linear and nonlinear classifiers. At first, the classification accuracy results are presented using

confusion matrix. Later, the results are also presented using other visualization and statistical analysis techniques. Two Bayes classifiers (LDC and UDC) and one linear classifier (Parzen) have outperformed other.

Chapter 5 highlighted our novel idea for combining the base-level classifiers to check if we can obtain higher classification accuracy. Three base-level classifiers (LDC, UDC, Parzen) are combined together by six fixed combining rules. We observed that total mean error rate in case of combined classifier is less than base-level classifiers. Moreover, it has also been noted that voting combing rule has provided the highest accuracy rate as compare to other combining rules.

Chapter 6 presented the second half of our project that we have completed in France in SIGMA laboratory. Here we have analysed three different sets of MiAD and healthy subjects to compute the neural synchronization of EEG signals. Two novel methods are proposed to apply three neural synchrony measure techniques (phase synchrony, cross correlation and MS coherence) on three datasets. One of the methods is named PCA based synchrony while the second method is called Average synchrony. Results revealed that PCA based synchrony has given us more significant results that can help us to diagnose Alzheimer’s at its earlier stage than Average synchrony. Moreover, cross correlation measure has proved to be the best one among others to provide better results. Results have been compared using Wilcoxon ranksum (Mann-Whitney) test. Later, Gram Schmidt orthogonalization algorithm is applied with “n-

probe” function to get the most important features that can help clinicians for the classification and early diagnosis of AD.