Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System

Stabilometry is a branch of medicine that studies balance-related human functions. The analysis of stabilometric-generated time series can be very useful to the diagnosis and treatment balance-related dysfunctions such as dizziness. In stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods known as events. In this study, a feature extraction scheme has been developed to identify and characterise the events. The proposed scheme utilises a statistical method that goes through the whole time series from the start to the end, looking for the conditions that define events, according to the experts¿ criteria. Based on these extracted features, an Adaptive Fuzzy Inference Neural Network (AFINN) has been applied for the classification of stabilometric signals. The experimental results validated the proposed methodology

Generating reference models for structurally complex data: application to the stabilometry medical domain

We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of singlevalued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions

Computerised accelerometric machine learning techniques and statistical developments for human balance analysis

Balance maintenance is crucial to participating in the activities of daily life. Balance is often considered as the ability to maintain the centre of mass (COM) position within the base of support. Primarily, to maintain balance, reliance is placed on the balance related sensory systems i.e., the visual, proprioceptive and vestibular. Several factors can affect a person’s balance such as neurological diseases, ageing, medication and obesity etc. To gain insight into the balance operations, studies rely on statistical and machine learning techniques. Statistical techniques are used for inferencing while machine learning techniques proved effective for interpretation. The focus of this study was on the issues encountered in human balance analysis such as the quantification of balance by relevant features, the relationships between COM and ground projected body sway, the performance of various sensory systems in balance analysis, and their relationships between the directions of body sway (i.e., mediolateral (ML) and anteriorposterior (AP)). A portable wireless accelerometry device was developed, balance analysis methods based on the inverted pendulum were devised and evaluated for their accuracy and reliability against a setup designed to allow manual balance measurements. Balance data were collected from 23 healthy adult subjects with the mean (standard deviation) of the age, height and weight: 24.5 (4.0) years, 173.6 (6.8) cm, and 72.7 (9.9) kg respectively. The accelerometry device was attached to the subjects at the approximate position of the illac crest, while they performed 30 seconds trials of the four conditions associated with a standard balance test called the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). These required standing on a hard (ground) surface with the eyes open, standing on hard surface with the eyes closed, standing on a compliant surface (sponge, 10 cm thick) with the eyes open and standing on a compliant surface with the eyes closed. Statistical and machine learning techniques such as t-test, Wilcoxon signed-rank test, the Mann-Whitney U test, Analysis of variance (ANOVA), Kruskal-Wallis test, Friedman test, correlation analysis, linear regression, Bland and Altman analysis, principal component analysis (PCA), K-means clustering, and Kohonen neural network (KNN) were employed for interpreting the measurements. The findings showed close agreement between the developed balance analysis methods and the related measurements from the manual setup for balance analysis. The COM was observed to be responsible for differing amount of sway across the subjects and could affect both the angle and ground projected sway. The AP direction was more sensitive to sway than the ML direction. The subjects were observed to depend more on their proprioceptive system to control balance. The proprioceptive system was observed to have a greater impact in controlling the AP velocity of the subjects as compared to their visual system. The proprioceptive system had no impact on the ML velocity. The visual system was responsible for the control of the ML velocity and for reducing the acceleration in both directions. It was concluded that for comparison of postural sway information, subjects with closely related COM positions should be compared, comparison should be carried out in respect to the base of their support. The sway normalisation by dividing with COM position should be performed to reduce the obscuring effect of the COM. Enhancement of the proprioceptive system should be carried out to reduce the AP velocity while enhancement of the visual system should be used to reduce the ML sway and acceleration in ML and AP directions. The velocity in the AP direction should be used to examine the performance of the proprioceptive system while the ML velocity and acceleration should be used for the visual system. The vestibular system characterised sway more in the AP direction, and hence, the AP direction should be used to examine its performance in balance

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial