Mariano Cabrero-Canosa

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.

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.

Application of RFID Technology in Patient Tracking and Medication Traceability in Emergency Care

One of the most important factors that directly affects the quality of health care is patient safety. Minimize the occurrence of adverse events is one of the main challenges for health professionals. This requires continuous tracking of the patient by different areas and services, a process known as traceability and proper patient identification and medication prescribed. This article presents an information system for patient tracking and drugs developed for the Emergency Department of Hospital A Coruña. The systems use RFID technology to perform various tasks: (1) locate patients in different areas; (2) measure patient care times and waiting times; (3) identify unitary doses of medication; and (4) ensure the correct matching between the patient and the medication prescribed by the doctor. The hardware infrastructure as well as the optimal configuration of devices interconnected via a wireless network was determined by conducting a detailed coverage study. To support all the functionality needed, specific tools were designed and integrated with proprietary software applications. The RFID system was evaluated positively by staff from different professional profiles involved in its development or subsequent implementation.One of the most important factors that directly affects the quality of health care is patient safety. Minimize the occurrence of adverse events is one of the main challenges for health professionals. This requires continuous tracking of the patient by different areas and services, a process known as traceability and proper patient identification and medication prescribed. This article presents an information system for patient tracking and drugs developed for the Emergency Department of Hospital A Coruña. The systems use RFID technology to perform various tasks: (1) locate patients in different areas; (2) measure patient care times and waiting times; (3) identify unitary doses of medication; and (4) ensure the correct matching between the patient and the medication prescribed by the doctor. The hardware infrastructure as well as the optimal configuration of devices interconnected via a wireless network was determined by conducting a detailed coverage study. To support all the functionality needed, specific tools were designed and integrated with proprietary software applications. The RFID system was evaluated positively by staff from different professional profiles involved in its development or subsequent implementation.

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>>

Integration of data, information and knowledge in intelligent patient monitoring

Abstract Efficient patient monitoring requires the integration of bedside monitors, database information and the application of artificial intelligence (AI) techniques, in order to obtain correct interpretations and to prescribe appropriate therapies. In this article, the authors present the new architecture of PATRICIA, an intelligent monitoring system designed to advise clinicians in the management of patients in the intensive care unit (ICU). The systems new architecture is based on current trends in the design of hospital health care systems, and allows integration of bedside monitors to front-end computers, and through the data network to a central monitor that controls and manages all the network operations. We have applied the client–server philosophy that takes advantage from information integration, shared resources and equipment networking. This approach results in an efficient and flexible system, and offers several benefits from the clinical point of view, as it serves as a helping tool for clinical decision-making in an ICU environment.

A knowledge model for the development of a framework for hypnogram construction

Abstract We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for different clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been developed but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not affected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.

RFID and NFC in hospital environments: reaching a sustainable approach

The increase of safety and the improvement of care received by the patient during their healthcare process are one of the main challenges facing health professionals. Obtaining patient traceability and minimising the occurrence of adverse events during the perscription-validation-dispensing-administration process of medication to patients, encourages making measures of improvement to ensure the quality of the processes that take place in the clinical practice of a hospital. It is therefore essential to study current leading technologies such as RFID and NFC in a sustainable way to determine the feasibility of its application in the healthcare environment.

Automated analog-to-digital conversion of graphical cardiotocographic records.

The system described, VISC, has as its aim the acquisition and subsequent digitization of monodimensional cardiotocographic signals. These signals can originate in any type of hospital cardiotocographic register, as well as in a book of uterine activity (UA) or fetal heart rate (FHR) signals. VISC consists of a set of algorithms to identify the fetal heart rate signal, although it can be used with minor corrections to work on any continuous curve. Some of the problems that may arise, such as signal loss and presence of noise, are discussed to illustrate the proposed method. The program is integrated into a wider environment, whose main nucleus is the NST-EXPERT expert system, for the diagnosis of fetal well-being, the recommendation of therapeutical plans and the prognosis of immediate neonatal states.

VISC: cardiotocographic signals visualization system

VISC is a program for the visualization of monodimensional cardiotocographic signals. The aim of such a system is the acquisition of cardiotocographic tracings to digitalize the fetal heart rate (FHR) and uterine activity (UA) signals. These signals are then fedinto theantenatalexpertsystem NST-EXPERT, which perfoms the diagnosis. prognosis and therapy of the fetal state based on thenons b a s test. VISC supplies information to two different NST-EXPERT subsystems: a) the diagnostic module of the expert system and b) an artificial neural network (ANN) that works as a reassuring method of the former. Both subsystems constitute the diagnostic unit of the hybrid antenatal expert system. VISC will be integrated with this hybrid system. being its output the digitalized m A signals.

Towards the Standardization of Hypnograms Construction for Sleep Analysis

Sleep can be considered as a mechanism of self-regulation and resting that occurs in the majority of mammals in 24-hour cycles approximately, alternating with states of wakefulness. As a whole, sleep is a heterogeneous state presenting different stages. These stages can be identified through the recording and analysis of certain physiological parameters. From a medical point of view the analysis of the sleep is useful in the diagnosis of health problems that receive the generic name of “sleep disorders”. Sleep disorders can be grouped into four main categories: (a) problems to fall asleep and stay asleep, (b) problems to stay awake, (c) problems to maintain a regular schedule of sleep, and (d) unusual behaviors during sleep. This article presents an overview of the evolution of sleep research, with special attention to the most relevant milestones that have led to the systematic and automatic analysis of the sleep, and the establishment of standards for the construction of the so-called “Hypnograms”.