John Gade

A simple format for exchange of digitized polygraphic recordings

A simple digital format supporting the technical aspects of exchange and storage of polygraphic signals has been specified. Implementation of the format is simple and independent of hard- or software environments. It allows for any local montages, transducers, prefiltering, sampling frequencies, etc. At present, 7 laboratories in various countries have used the format for exchanging sleep-wake recordings. These exchanges have made it possible to create a common database of sleep records, to compare the analysis algorithms local to the various laboratories to each other by applying these algorithms to identical signals, and to set up a computer-aided interlaboratory evaluation of manual and automatic analysis methods.

Signal processing in prolonged EEG recordings during intensive care

Methods for analyzing and displaying EEG signals are discussed. The increasing availability and affordability of powerful computer equipment makes possible the use of ever more sophisticated signal processing techniques, which extract relevant (but not readily discernible) information from long-term EEG recordings and can easily identify important features in the EEG. Whether these techniques are actually taken up in clinical practice is heavily dependent on how well they match clinical requirements. This article concentrates on requirements set in the context of long-term recordings in the ICU that demand the ability to process short-term discrete events as well as long-term trend information. A huge range of potentially useful signal processing techniques exists. This article illustrates the value of some of these techniques for ICU signals using the EEG recordings collected during the IMPROVE project.

Cognitive function in Type 1 (insulin-dependent) diabetic patients after nocturnal hypoglycaemia

SummaryEight Type 1 (insulin-dependent) diabetic patients with no diabetic complications were studied on two consecutive and one subsequent overnight occasions. The aim was to evaluate the influence of nocturnal hypoglycaemia on neuropsychological and reaction time tests the following morning. Hypoglycaemia was induced by i. v. insulin infusion, blood glucose nadir was 1.5±0.3 mmol/l. Duration of hypoglycaemia (blood glucose < 3 mmol/l) was 101±38 min. Whole night sleep statistics for all patients showed no statistical differences between the normoglycaemic and hypoglycaemic nights, however, there was a tendency of prolongation of the second sleep cycle in the nights with hypoglycaemia. Each patient was used as his own control and periods with blood glucose concentration less than 3 mmol/l were compared to exactly the same periods in nights with blood glucose level over 5 mmol/l. During hypoglycaemia the amount of deep sleep was reduced and replaced by superficial sleep and arousals of short duration. Further, the reduction in deep sleep was replaced later at night. Neuropsychological test scores and reaction time measurements in the morning showed no differences between the normoglycaemic and hypoglycaemic nights. In conclusion: despite sleep disturbances, nocturnal hypoglycaemia did not impair cognitive function the following morning in Type 1 (insulin-dependent) diabetic patients.

Collecting EEG signals in the IMPROVE data library

One of the key issues for the IMPROVE (IMPROVing control of patient status in critical carE) project was to define and build a data library (DL) of annotated data acquired in the intensive-care unit (ICU), with particular reference to problems of mismatch between oxygen utilisation and supply. An additional aim of the IMPROVE study was to test the feasibility and clinical value of including limited monitoring of high-quality long-term EEG signals with the main DL in a restricted number of patients. Such an EEG DL would form a useful basis for testing the applicability and validity of different signal processing and interpretation methods in ICU monitoring, and also demonstrate the degree to which useful information could be obtained by a degree of fusion between systemic and cerebral variables. In this article, we describe the setup for collection of the EEG DL, the tools developed to facilitate visual analysis of the EEG together with simultaneous data from other non-EEG variables, data concerning quality control, and some preliminary observations from detailed visual assessment of EEG patterns in relation to other ICU events.

Multi-centre comparison of five eye movement detection algorithms.

Although various investigators have suggested algorithms for the automatic detection of eye movements during sleep, objective comparisons of the proposed methods have previously been difficult due to different recording arrangements of different investigators. In this study the results of five eye movement detection algorithms applied to the same data were compared to visually scored data. The percentages of true and false detections are given for various threshold levels in rapid and slow eye movement detections. The methods gave best results when they were used with the same electrode montage they were designed for but the performance decreased when other montages were used. Subtracting the cross‐talk of EEG delta activity improved the correctness of eye movement detections.

Technical description of the IBIS Data Library

The IBIS Data Library (DL) is an annotated data library that contains practically all the monitored data and other clinical information from critically ill patients during surgery and in intensive care. The data have been collected at three sites: the intensive care unit of the Kuopio University Hospital, Finland; Royal Brompton Hospital, London, UK; and St. Bartholomews Hospital, London, UK. The purpose of the DL is to form the basis for development of biosignal interpretation methods in the Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery project in the European Union (EU) BIOMED2 programme (BMH4-97-2570). The DL contains continuous electroencephalography signals, multimodal evoked potential recordings and diagnostic electrocardiography recorded during intensive care and surgery. In addition, signal types similar to those recorded during an earlier project, the EU-BIOMED1 project IMPROVE, are stored in the DL. In addition, trend data from patient monitors, laboratory data, annotations, nursing actions, and medications recorded and stored by a Patient Data Management System (PDMS) during routine care are included. The data obtained routinely are complemented by special annotations made by a physician who observes the patient during the data collection session. Annotations include, for example, assessment of the awareness of the patient and specific events during surgery not recorded routinely by the PDMS. Inclusion of information about the care plan and the aims of the care make the contents of the DL complete. The present paper describes the technical set-up used for recording of the DL and the contents of the DL. The paper also includes an appendix defining a new data format, the extended evoked potentials format, used for storage of sweep data in the DL.

Analysis Of Brain Synchronization, Based On Noise-driven Feedback Models

The activity of many brain cells can be synchronized by synaptic interconnections. A noise driven feedback model of this mechanism simulates both rhythmic activity and phasic events in the EEG. While classical algorithms such as Fourier analysis quantify power, the model-based algorithms quantify the synchronized, that is predictable, part of this power. The model-based method shows better time resolution and is biased less by artifacts and randomness.

The challenges in creating critical-care databases

As critical care is critical, the demands for patient monitoring are high. The methods need to function properly over a large variety of possible physiological conditions in an environment full of sources for technical artifacts, and they need to support clinical reasoning. Several iterations are usually required in the method development to meet these demands. First, the proposed method is developed and tested with simulated or optimal test data on a proof-of-principle basis. When this level is passed, the method needs to be tested offline with more realistic and nonoptimal data. Only after passing these levels may clinical trials be conducted. Usually, these developments require a significant period of time. Availability of extensive, well-characterized, and well-documented real physiological data during critical care would potentially reduce the amount of time required for the first phases of the method development. Such databases would also enable bench validation of any new methods and objective comparison between different methods. Recently, there have been some attempts to collect large signal databases during critical care. In this article, we aim to discuss the special requirements for collecting signal databases in critical care and to summarize the main features of the existing databases.

Steps on the Road to Clinical Application of Decision Support --- Example TREAT

The decision support system TREAT advices on antibiotic treatment of severe infections. A multicenter randomized clinical trial has demonstrated that Treat reduces inappropriate treatment by 50%. This paper will show that TREAT satisfies several features closely correlated with decision support systemss ability to improve clinical practice. Examples of such criteria are: providing recommendations, not just assessments; transparent line of reasoning; convenience in use. Additional design features, such as transferability and addressing an important clinical problem, will also be discussed.