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

Trajectory Data Mining in Mouse Models of Stroke

Contains fulltext : 273912.pdf (Publisher’s version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p

Passive transfer of sera from als patients with identified mutations evokes an increased synaptic vesicle number and elevation of calcium levels in motor axon terminals, similar to sera from sporadic patients

Previously, we demonstrated increased calcium levels and synaptic vesicle densities in the motor axon terminals (MATs) of sporadic amyotrophic lateral sclerosis (ALS) patients. Such alterations could be conferred to mice with an intraperitoneal injection of sera from these patients or with purified immunoglobulin G. Later, we confirmed the presence of similar alterations in the superoxide dismutase 1 G93A transgenic mouse strain model of familial ALS. These consistent observations suggested that calcium plays a central role in the pathomechanism of ALS. This may be further reinforced by completing a similar analytical study of the MATs of ALS patients with identified mutations. However, due to the low yield of muscle biopsy samples containing MATs, and the low incidence of ALS patients with the identified mutations, these examinations are not technically feasible. Alternatively, a passive transfer of sera from ALS patients with known mutations was used, and the MATs of the inoculated mice were tested for alterations in their calcium homeostasis and synaptic activity. Patients with 11 different ALS-related mutations participated in the study. Intraperitoneal injection of sera from these patients on two consecutive days resulted in elevated intracellular calcium levels and increased vesicle densities in the MATs of mice, which is comparable to the effect of the passive transfer from sporadic patients. Our results support the idea that the pathomechanism underlying the identical manifestation of the disease with or without identified mutations is based on a common final pathway, in which increasing calcium levels play a central role

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets using Multiple Classifiers

The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple “accuracy rate” can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that “misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets”, a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples

Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

Preclinical research into amyotrophic lateral sclerosis : a comparison of established and novel techniques and models

Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressing degenerative disease which leads to muscular atrophy via the degeneration of both upper and lower motor neurons with the majority of patient death resulting from respiratory failure. The majority (~90-95%) of ALS cases are non-inherited or sporadic ALS (sALS), with the remaining 5-10% inherited or familial ALS (fALS). ALS presents with a broad heterogeneity, as age of onset, symptoms and rate of disease progression are highly variable among patients, and this is further compounded by recent findings that ALS sits on a spectrum of diseases together with frontotemporal dementia (FTD) and being referred to as ALS-frontotemporal spectrum disorder. Only one drug is currently widely approved for the treatment of ALS (Riluzole) with many other drug candidates failing in clinical trials after seeing success in preclinical animal models for the disease. One of the factors potentially contributing to the discrepancy between outcomes of preclinical testing and clinical trials for a new treatment candidate is the impact of the laboratory environment on ALS mouse models. The work presented in this thesis shows that mouse handling and mouse cage systems used for ALS transgenic mouse models can impact on anxiety and behaviours such as fear-associated learning and sensorimotor gating. These findings can have implications for the validity of these mouse models for research into human diseases as well as the particular experimental strategy chosen. Additionally, my work provides new insights into ALS-relevant gene-gene interactions that are capable of influencing behavioural phenotypes in established mouse models for the disease. Finally, I provide evidence that administration of 50mg/kg CBD as a novel treatment is not effective in female SOD1G93A mice with the chosen parameters. Ultimately, laboratory environments / procedures should always be considered as potential test confounders and therefore be specifically selected for any preclinical research project. It is important to realise that these factors can impose on the validity of mouse models for ALS - even small alterations to the onset or progression of disease-relevant phenotypes can impact on experimental outcomes and thereby influence the translational potential of e.g. novel treatments in diseases with narrow treatment windows such as ALS

Reconocimientos y clasificación de patrones de marcha neurodegenerativa mediante variables temporoespaciales y machine learning : Esclerosis Múltiple, Parkinson, Huntington

Tesis (Kinesiólogo)La literatura ha descrito que la marcha humana puede ser clasificada con diversas técnicas y modelos, desde un simple péndulo hasta clasificadores complejos de aprendizaje automatizado, estos últimos son conocidos como clasificadores o clustering . Las técnicas de clasificación son métodos de análisis de multivarianza mostrando su mayor potencial en el reconocimiento de patrones. Las redes neuronales es un tipo de clasificador supervisado, caracterizadas por hacer su análisis y solución de problemas a través de los ajustes de los pesos conocidos, entregando como salida una matriz de confusión y una Receiver Operating Characteristic o curva ROC, que demuestran la calidad de la clasificación. Se ha relatado ampliamente la marcha patológica en enfermedades neurodegenerativas a través de distintos algoritmos computacionales , inclusivamente en la misma población, estableciendo un interés mundial, dando una relevancia al método de clasificación y descripción de la marcha humana. Sin embargo en la actualidad se desconocen los diversos motivos de análisis, poca comparación entre bases de datos en los distintos modelos y clasificaciones, adicionalmente de la pobre descripción de la marcha en base de datos, y la refuta de los procesos metodológicos. Es por esto, que es de suma importancia describir la marcha neurodegenerativa y la clasificación de patrones a través de métodos rigurosos en distintos tipos de bases de datos alternando los sujetos de estudio y sus variables, con el propósito de valorar el real aporte de estos parámetros temporo espaciales como atributos o características que permitan la correcta clasificación. ¿Es posible describir y clasificar la marcha neurodegenerativa en las patologías de ELA, EP, EH. mediante machine learning y las variables temporoespaciales