Input features' impact on fuzzy decision processes.

Many real-world applications have very high dimensionality and require very complex decision borders. In this case, the number of fuzzy rules can proliferate, and the easy interpretability of fuzzy models can progressively disappear. An important part of the model interpretation lies on the evaluation of the effectiveness of the input features on the decision process.

Knowledge-based and data-driven models in arrhythmia fuzzy classification.

Objectives: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem.

Methods: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures.

A characterization of HRV's nonlinear hidden dynamics by means of Markov models.

A study of the 24-h heart rate variability's (HRV) hidden dynamic is performed hour by hour, in order to investigate the evolution of the nonlinear structure of the underlying nervous system. A hierarchy of null hypotheses of nonlinear Markov models with increasing order n is tested against the hidden dynamic of the HRV time series. The minimum accepted Markov order supplies information about the nonlinearity of the HRV's hidden dynamic and consequently of the underlying nervous system.

Dynamics extraction in multivariate biomedical time series.

A nonlinear analysis of the underlying dynamics of a biomedical time series is proposed by means of a multi-dimensional testing of nonlinear Markovian hypotheses in the observed time series. The observed dynamics of the original N-dimensional biomedical time series is tested against a hierarchy of null hypotheses corresponding to N-dimensional nonlinear Markov processes of increasing order, whose conditional probability densities are estimated using neural networks. For each of the N time series, a measure based on higher order cumulants quantifies the independence between the past of the N-dimensional time series, and its value r steps ahead.

Human heart rate variability and sleep stages.

With the aim of better understanding the dynamic changes in sympatho-vagal tone occurring during the night, human heart rate variability (HRV) during the various sleep stages was evaluated by means of autoregressive spectral analysis. Each recording consisted of an electroencephalogram, an electrooculogram, and electromyogram, and electrocardiogram, and a spirometry trace. All of the data were sampled and stored in digital form.

Classification of arrhythmic events in ambulatory electrocardiogram, using artificial neural networks.

We propose artificial neural networks (ANN) for ambulatory ECG arrhythmic event classification, and we compare them with some traditional classifiers (TC). Among them, the one based on the median method (heuristic algorithm) was chosen and taken as a quality reference in this study, while a back propagation based classifier, designed as an autoassociator for its peculiar capability of rejecting unknown patterns, was examined. Two tests were performed: the first to discriminate normal vs ventricular beats and the second to distinguish among three classes of arrhythmic events.