Vicente Moret-Bonillo

Usability: A Critical Analysis and a Taxonomy

A major obstacle to the implantation of User-Centered Design in the real world is the fact that no precise definition of the concept of usability exists that is widely accepted and applied in practice. Generally speaking, the literature tends to define usability in overly brief and ambiguous terms and to describe its application in informal terms. This is one of the main reasons why ad hoc techniques predominate in usability study methodologies. The aims of this article are to investigate the concept of usability and to describe it by means of a detailed taxonomy that is organized hierarchically and that contains exhaustive descriptions of usability attributes. This taxonomy can be used to support different stages in the development of usable systems.

A new method for sleep apnea classification using wavelets and feedforward neural networks

OBJECTIVES This paper presents a novel approach for sleep apnea classification. The goal is to classify each apnea in one of three basic types: obstructive, central and mixed. MATERIALS AND METHODS Three different supervised learning methods using a neural network were tested. The inputs of the neural network are the first level-5-detail coefficients obtained from a discrete wavelet transformation of the samples (previously detected as apnea) in the thoracic effort signal. In order to train and test the systems, 120 events from six different patients were used. The true error rate was estimated using a 10-fold cross validation. The results presented in this work were averaged over 100 different simulations and a multiple comparison procedure was used for model selection. RESULTS The method finally selected is based on a feedforward neural network trained using the Bayesian framework and a cross-entropy error function. The mean classification accuracy, obtained over the test set was 83.78+/-1.90%. CONCLUSION The proposed classifier surpasses, up to the authors knowledge, other previous results. Finally, a scheme to maintain and improve this system during its clinical use is also proposed.

An intelligent system for the detection and interpretation of sleep apneas

Abstract The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patients sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a nights sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patients polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patients hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.

Validation of intelligent systems: a critical study and a tool

Abstract One of the most important phases in the methodology for the development of intelligent systems is that corresponding to the evaluation of the performance of the implemented product. This process is popularly known as verification and validation (V&V). The majority of tools designed to support the V&V process are preferentially directed at verification in detriment to validation, and limited to an analysis of the internal structures of the system. The authors of this article propose a methodology for the development of a results-oriented validation, and a tool (SHIVA) is presented which facilitates the fulfilment of the tasks included in the methodology, whilst covering quantitative as well as heuristic aspects. The result is an intelligent tool for the validation of intelligent systems.

ESTER: an expert system for management of respiratory weaning therapy

ESTER is an expert system in which the most widely accepted criteria are systematically utilized to supply advice to the clinician who has to prescribe a respiratory therapy regime suited to the needs of the patient. After effecting a preliminary prognosis of the patients condition, ESTER asks for certain physiological parameters to be keyed in. Analysis of these parameters reveals the patients condition and allows a recommended respiratory therapy to be designed.<<ETX>>

Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome

The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire nights sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.

Intelligent diagnosis of sleep apnea syndrome

An effective diagnosis of the sleep apnea syndrome (SAS) is based on a contextual analysis of the patients polysomnograph, consisting of simultaneously recording electrophysiological and pneumological signals during a nights sleep. Currently, the prevalence of this disorder has caused an increase in the demand for specialist clinical assistance and sleep units. As in other areas of medicine, the volume of clinical data that has to be processed is enormous, which justifies the construction of computerized decisionmaking tools that partially automate these routine tasks. Our system, SAMOA, belongs to this category of help tools, being an automatic SAS diagnostic system that incorporates both conventional programming and artificial intelligence techniques. This article describes the most important aspects of the temporal data management in the different analysis processes and the final correlation of all the symbolic information generated by the different cooperative modules.

A method for the automatic analysis of the sleep macrostructure in continuum

Sleep staging is one of the most important tasks within the context of sleep studies. For more than 40 years the gold standard to the characterization of patients sleep macrostructure has been based on set of rules proposed by Rechtschaffen and Kales and recently modified by the American Academy of Sleep Medicine. Nevertheless the resulting map of sleep, the so-called hypnogram, has several limitations such as its low temporal resolution and the unnatural characterization of sleep through the assignment of discrete sleep states. This study reports an automatic method for the characterization of the structure of the sleep. The main intention is to overcome limitations of epoch-based sleep staging by obtaining a more continuous evolution of the sleep of the patient. The method is based on the use of fuzzy inference in order to avoid binary decisions, provide soft transitions and enable concurrent characterization of the different states. It is proven, in addition, how the new proposed continuous representation can still be used to generate the classical epoch-based hypnogram.

The PATRICIA project: a semantic-based methodology for intelligent monitoring in the ICU

The authors describe PATRICIA, an intelligent monitoring system designed to advise clinicians in the management of patients dependent on mechanical ventilation. PATRICIA incorporates a patient-oriented symbolic approach for representing knowledge and a symbolic-oriented temporal approach for the intelligent control of the monitoring process. The results and methodology used in the validation of PATRICIA are presented. Preliminary results show that PATRICIA could be a useful tool for the management of patients receiving mechanical ventilatory support.<<ETX>>

Information analysis and validation of intelligent monitoring systems in intensive care units

The validation of intelligent systems is an important task to perform. Typically, the results of the validation analysis are used to verify whether or not the system satisfies the initial design requirements, and to acquire new knowledge and/or refine the knowledge already acquired. In practice, the validation of intelligent systems usually requires the application of several different techniques (e.g. retrospective, prospective and quantitative). In this paper, the authors present the methodology devised to validate PATRICIA: an intelligent monitoring system designed to advise clinicians on the management of patients who are dependent on mechanical ventilation. The application of this methodology requires that appropriate validation paradigms are selected, depending on both the application domain and the characteristics of the intelligent system. The article also presents and discusses validation results.