Comparación con simulación

The importance and the novel contributions of this research work can be assessed from two different perspectives—medical and IT fields. It has not only provided a very narrow research work on the causes and symptoms of the NDDs but has also highlighted those crucial moments where IT could play its significant role to diagnose the progression of these diseases from their onset to acute stages. Furthermore, it has explored more directions and innovations in the field of machine learning, signal processing and signal classification to get maximum benefits from this filed. Adding to the solutions, it has also discovered those hidden significant features that can be used as biomarkers from the early diagnosis of these life threatening diseases.

Following are the main contributions of the thesis:

 Solutions for Skewed Datasets: In Chapter 4, we have highlighted the limitations of imbalanced datasets in term of getting biased results for majority class patterns. Possible solutions in term of under sampling and over-sampling are also provided. Later, over-sampling method has been demonstrated using gait dynamics of different neurodegenerative diseases and control subjects.

 Extended set of gait features: Previous findings are based on a very limited set of features that are used to classify the neurodegenerative diseases. In our research work, we have selected all significant features that have direct or indirect impact on the progression of NDDs. For instance, age and gender have significant relation with NDDs. The likelihood of developing Alzheimer’s increases in advance age. Also, we have calculated BMI of each person to notice if weight of the person has any link with the progression of neurological diseases. We have observed and demonstrated that all these factors are equally important in the early diagnosis of NDDs.

 Different sets of classifiers: As we already have mentioned that the classifier space that is considered for pattern recognition does not always contain the optimal classifier. For instance, a set of linear classifiers is chosen for a dataset that can best

recognize by nonlinear classifier can never help us to find an optimal classifier. Instead of focusing on a single classifier we have selected a number of different classifiers that belong to different categories—linear, nonlinear and Bayes classifiers.

 Performance Evaluation Matrices: It has already been demonstrated through literature survey that one particular performance measure may evaluate a classifier on a single perspective while fails to measure on another [112]. Although researchers have been evaluating classification algorithms by various techniques, yet there is no single authorized criterion that outperforms others. To overcome this complication, in our research work (Chapter 4), we have presented the results both by statistical and visualizing techniques. This has helped us to compare the results from different perspectives and eventually to select one of the classifiers that best suits to our dataset.

 Classifiers Fusion Strategy: In Chapter 5, we have proposed a novel idea to combine all those base-level classifiers that has given us comparatively better results. By combining different classifiers together, an opportunity is provided to the designer to have an access of different classifiers that belong to different context and are developed for entirely different representation. Moreover, an ensemble classifier can also handle the multivariate training sets that are collected at different times and also in different environment. The training set may also have different features. Results revealed that combining classifiers is good idea especially in a case where data belongs to multidimensional (different NDDs such as AD, PD, HD and ALS) as well as multivariate (a large set of features) datasets.

 Dividing EEG Signals into Narrow frequency bands: In Chapter 6, for the early diagnosis of Alzheimer’s disease, we have filtered EEG signals into five (5) different frequency bands. These frequency ranges are delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ). These narrow frequency bands helped us to find all those hidden patterns that can be used as biomarkers for the early diagnosis of Alzheimer’s.

 Different sets of EEG data: A high inter-subject variability has been seen in the EEG signals of AD patients, especially with different levels of severity and comorbidities. In this situation the findings are not more reliable on a single set of data. Most of the existing studies focus on a single synchrony measure with a single set of data. In this case it’s hard to compare the results to conclude a single

hypothesis. To extract a general set of feature we have analysed three different databases, each from one hospital at a time. This has not only increased the validity of our research but has also provided more reliable findings that can later be used in clinical applications.

 PCA technique: Literature survey does not provide us any obvious solution for removing the redundant information or noise from EEG dataset before applying synchrony measurement techniques. Analysing results without removing the redundant information or without eliminating the noise leads to non-reliable results. To remove the ambiguity of biased results due to “features redundancy” we have applied PCA (Principal Component Analysis) technique before applying synchrony measurement techniques (Chapter 6). This helped us finding more reliable results that can be used for clinical practices.

 Average Method vs. PCA Method: In Chapter 6, we have compared our proposed method PCA based synchrony measure with another proposed method called Average

synchrony measure. Results revealed that although Average method provides us some useful information for the early diagnosis of Alzheimer’s yet PCA method provides us more significant and authentic information that can be used as a biomarker for the early diagnosis of Alzheimer’s.

 Gait Signals Vs. EEG Signals: Two different kinds of signals are computed in this research work—Gait and EEG signals. Gait signals are computed to discriminate different NDDs for an accurate and exact diagnosis of a disease and also to provide in time treatment of the patient. On the other hand, EEG signals are computed to detect any perturbation in the brain. Any change in the synchronization of EEG signals is an indication of onset of an abnormality/pathology in the brain.