Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Simple low cost causal discovery using mutual information and domain knowledge

PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them

Functional Brain Oscillations: How Oscillations Facilitate Information Representation and Code Memories

The overall aim of the modelling works within this thesis is to lend theoretical evidence to empirical findings from the brain oscillations literature. We therefore hope to solidify and expand the notion that precise spike timing through oscillatory mechanisms facilitates communication, learning, information processing and information representation within the brain. The primary hypothesis of this thesis is that it can be shown computationally that neural de-synchronisations can allow information content to emerge. We do this using two neural network models, the first of which shows how differential rates of neuronal firing can indicate when a single item is being actively represented. The second model expands this notion by creating a complimentary timing mechanism, thus enabling the emergence of qualitive temporal information when a pattern of items is being actively represented. The secondary hypothesis of this thesis is that it can be also be shown computationally that oscillations might play a functional role in learning. Both of the models presented within this thesis propose a sparsely coded and fast learning hippocampal region that engages in the binding of novel episodic information. The first model demonstrates how active cortical representations enable learning to occur in their hippocampal counterparts via a phase-dependent learning rule. The second model expands this notion, creating hierarchical temporal sequences to encode the relative temporal position of cortical representations. We demonstrate in both of these models, how cortical brain oscillations might provide a gating function to the representation of information, whilst complimentary hippocampal oscillations might provide distinct phasic reference points for learning

Traitement bio-inspiré de la parole pour système de reconnaissance vocale

Cette thèse présente un traitement inspiré du fonctionnement du système auditif pour améliorer la reconnaissance vocale. Pour y parvenir, le signal de la parole est filtré par un banc de filtres et compressé pour en produire une représentation auditive. L'innovation de l'approche proposée se situe dans l'extraction des éléments acoustiques (formants, transitions et onsets ) à partir de la représentation obtenue. En effet, une combinaison de détecteurs composés de neurones à décharges permet de révéler la présence de ces éléments et génère ainsi une séquence d'événements pour caractériser le contenu du signal. Dans le but d'évaluer la performance du traitement présenté, la séquence d'événements est adaptée à un système de reconnaissance vocale conventionnel, pour une tâche de reconnaissance de chiffres isolés prononcés en anglais. Pour ces tests, la séquence d'événements agit alors comme une sélection de trames automatique pour la génération des observations (coefficients cepstraux). En comparant les résultats de la reconnaissance du prototype et du système de reconnaissance original, on remarque que les deux systèmes reconnaissent très bien les chiffres prononcés dans des conditions optimales et que le système original est légèrement plus performant. Par contre, la différence observée au niveau des taux de reconnaissance diminue lorsqu'une réverbération vient affecter les données à reconnaître et les performances de l'approche proposée parviennent à dépasser celles du système de référence. De plus, la sélection de trames automatique offre de meilleures performances dans des conditions bruitées. Enfin, l'approche proposée se base sur des caractéristiques dans le temps en fonction de la nature du signal, permet une sélection plus intelligente des données qui se traduit en une parcimonie temporelle, présente un potentiel fort intéressant pour la reconnaissance vocale sous conditions adverses et utilise une détection des caractéristiques qui peut être utilisée comme séquence d'impulsions compatible avec les réseaux de neurones à décharges