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Biomedical subjects

G Carrault

Publications and source records attributed to G Carrault.

18 recordsLinked to original sources
PubMed2003

Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms.

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.

Algorithms↗
PubMed2002

[Evaluation of pendulum testing of spasticity].

OBJECTIVES: To identify valid measurements of spasticity derived from the pendulum test of the leg in a representative population of spastic patients. MATERIAL AND METHODS: Pendulum testing was performed in 15 spastic and 10 matched healthy subjects. The reflex-mediated torque evoked in quadriceps femoris, as well as muscle mechanical parameters (viscosity and elasticity), were calculated using mathematical modelling. Correlation with the two main measures derived from the pendulum test reported in the literature (the Relaxation Index and the area under the curve) was calculated in order to select the most valid. RESULTS, DISCUSSION: Among mechanical parameters, only viscosity was found to be significantly higher in the spastic group. As expected, the computed integral of the reflex-mediated torque was found to be larger in spastics than in healthy subjects. A significant non-linear (logarithmic) correlation was found between the clinically-assessed muscle spasticity (Ashworth grading) and the computed reflex-mediated torque, emphasising the non-linear behaviour of this scale. Among measurements derived from the pendulum test which are proposed in the literature for routine estimation of spasticity, the Relaxation Index exhibited an unsuitable U-shaped pattern of variation with increasing reflex-mediated torque. On the opposite, the area under the curve revealed a linear regression, which is more convenient for routine estimation of spasticity. CONCLUSION: The pendulum test of the leg is a simple technique for the assessment of spastic hypertonia. However, the measurement generally used in the literature (the Relaxation Index) exhibits serious limitations, and would benefit to be replaced by more valid measures, such as the area under the goniometric curve, especially for the assessment of therapeutics.

Adult↗
PubMed2001

A comprehensive model of spastic hypertonia derived from the pendulum test of the leg.

We propose a comprehensive model of spastic hypertonia based on clinical neurophysiology and validated using experimental data obtained from the pendulum test of the leg in 8 healthy volunteers and 15 spastic patients. This nonlinear computational model includes mechanical parameters and a stretch reflex representation involving three neural parameters: a threshold coefficient, the gain of the stretch reflex, and a time lag accounting for the reflex loop latency and the electromechanical coupling delay. Variation of the threshold coefficient alone allowed an overall reproduction of experimental data obtained from spastic and healthy subjects. We propose that this parameter could represent the supraspinal drive, supposed to be preserved in control subjects and decreased in spastic patients. No subsequent variation of the reflex gain was required to simulate spastic traces. Adjustment of the time lag influenced the duration of the swinging phase and oscillatory phenomena possibly occurring during the pendulum test. It could be related to the involvement of either short- or long-latency stretch reflex loops. With respect to current neurophysiological concepts of motor control, this modeling approach may help in understanding mechanisms underlying spastic hypertonia, and in predicting the clinical effect of antispasticity agents.

Adult↗
PubMed2001

Numerically solving physiological models based on a polynomial approach.

Much research effort has been directed in different physiological contexts towards describing realistic behaviors with differential equations. One observes obviously that more state-variables give the model more accuracy. Unfortunately, the computational cost involved is higher. A new algorithm is presented for simulating a model described by a system of differential equations in which efficiency may not be altered by its size. In order to do this, the method is based on a polynomial description of the state-variables' evolution and on a computation distributed control. Evaluations and results performed with classical models like Fitzhugh Nagumo or Hodgkin Huxley, allow validation of the method and exhibits its potential to decrease the computational costs.

Algorithms↗
PubMed2001

Atrial activity enhancement by Wiener filtering using an artificial neural network.

This paper describes a novel technique for the cancellation of the ventricular activity for applications such as P-wave or atrial fibrillation detection. The procedure was thoroughly tested and compared with a previously published method, using quantitative measures of performance. The novel approach estimates, by means of a dynamic time delay neural network (TDNN), a time-varying, nonlinear transfer function between two ECG leads. Best results were obtained using an Elman TDNN with nine input samples and 20 neurons, employing a sigmoidal tangencial activation in the hidden layer and one linear neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT-BIH arrhythmia database and compared with an adaptive cancellation scheme proposed in the literature. Results show the advantages of the proposed approach, and its robustness during noisy episodes and QRS morphology variations.

Algorithms↗
PubMed2001

Real-time ECG transmission via Internet for nonclinical applications.

Telemedicine is producing a great impact in the monitoring of patients located in remote nonclinical environments such as homes, elder communities, gymnasiums, schools, remote military bases, ships, and the like. A number of applications, ranging from data collection, to chronic patient surveillance, and even to the control of therapeutic procedures, are being implemented in many parts of the world. As part of this growing trend, this paper discusses the problems in electrocardiogram (ECG) real-time data acquisition, transmission, and visualization over the Internet. ECG signals are transmitted in real time from a patient in a remote nonclinical environment to the specialist in a hospital or clinic using the current capabilities and availability of the Internet. A prototype system is composed of a portable data acquisition and preprocessing module connected to the computer in the remote site via its RS-232 port, a Java-based client-server platform, and software modules to handle communication protocols between data acquisition module and the patient's personal computer, and to handle client-server communication. The purpose of the system is the provision of extended monitoring for patients under drug therapy after infarction, data collection in some particular cases, remote consultation, and low-cost ECG monitoring for the elderly.

Aged↗
PubMed2000

Pediatric anesthesia monitoring with the help of EEG and ECG.

This paper presents research regarding the monitoring of the brain and the adequacy of anesthesia during surgery. Particular variables are derived from EEG and ECG signals and are correlated to anesthetic gas (sevoflurane) concentration, in pediatric anesthesia. The methods used for parameter extraction are based on change detection theory and time-frequency representation. Preliminary results show that the expired anesthetic gas concentration modulates both the heart rate variability and the duration of the burst suppression. Monitors of the central nervous system and autonomic nervous system activities can be expected to be based on these variables.

Anesthesia, General↗
PubMed2000

Overview of CARMEM: a new dynamic quantitative cardiac model for ECG monitoring and its adaptation to observed signals.

Different approaches have been proposed in order to achieve knowledge integration for coronary care monitoring applications, usually in the form of expert systems. The clinical impact of these expert systems, which are based only on "shallow" knowledge, has not been remarkable due to the difficulties associated with the construction and maintenance of a complete knowledge base. Model-based systems represent an alternative to these problems because they allow efficient integration of the "deep" knowledge on the underlying physiological phenomena being monitored. In this work, a brief review of existing model-based systems for cardiac rhythm interpretation is presented, followed by the description of a new system for Cardiac Arrhythmia Recognition by Model-Based ECG Matching (CARMEM). Fundamental characteristics of CARMEM are presented; in particular, its ability to provide online parameter adaptation to simulate complex rhythms and to match observed ECG signals. The proposed model can be useful for the explanation of the origin of cardiac arrhythmias and contribute towards their robust characterization in the context of coronary care units.

Artificial Intelligence↗
PubMed1999

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring.

Information management for critical care monitoring is still a very difficult task. Medical staff is often overwhelmed by the amount of data provided by the increased number of specific monitoring devices and instrumentation, and the lack of an effective automated system. Specifically, a basic task such as arrhythmia detection still produce an important amount of undesirable alarms, due in part to the mechanistic approach of current monitoring systems. In this work, multisensor and multisource data fusion schemes to improve atrial and ventricular activity detection in critical care environments are presented. Applications of these schemes are quantitatively evaluated and compared with current methods, showing the potential advantages of data fusion techniques for event detection in noise corrupted signals.

Coronary Care Units↗
PubMed1996

Non-linear algorithms for processing biological signals.

This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.

Algorithms↗
PubMed1996

Linear multivariate models for physiological signal analysis: theory.

The general linear parametric multivariate modelling concept is presented. This model combines a variety of different kinds of multivariate linear models. The concept of partial spectral analysis is derived from the general model. Some emphasis is laid on the causality demands of the model, and it is shown that the classic strictly-causal structure must be abandoned in order to utilise the modelling in many practical situations. Two special sub-class models are described in detail: the multivariate autoregressive model and the multivariate dynamic adjustment model. Furthermore, time-varying modelling is considered. The modelling of the real system is presented on a general level as a system identification cycle. The application of the methods to real physiological data is presented in the companion paper.

Data Collection↗
PubMed1996

Linear multivariate models for physiological signal analysis: applications.

Some applications of linear multivariate modelling methods in the analysis of physiological signals are presented. These applications illustrate the methods in the analysis of cardiovascular dynamics, which has been one of the main application fields of the multivariate modelling during the last ten years. It is demonstrated that physiologically meaningful information about the causal interactions in the cardiovascular system can be drawn from the routinely available clinical signals. Both static and dynamic conditions are considered.

Humans↗
PubMed1995

An approach to intelligent ischaemia monitoring.

The paper describes an approach to intelligent ischaemia event detection based on ECG ST-T segment analysis. ST-T trends are processed by means of a Bayesian forecasting approach using the multistate Kalman filter. A complete procedure, intended for use in CCU/ICU monitoring areas, is proposed, in order to give the clinician an intelligent monitoring tool. The approach serves to describe trends and their changes in a symbolic way. A novel aspect is its ability to observe certain features of ST-T elevation/depression not detected by other means, and to reject artefacts and erroneous events. A sensitivity of 89.58% and a predictivity of 84.31% are obtained on selected records of the European ST-T database. Using a restriction on event amplitude, the predictivity is raised to 95.55%. An ischaemia sensitivity index of 1.2 was determined. The method has been shown to be a robust and practical trend analysis tool, and seems to be appropriate for numeric/symbolic transformations in next-generation intelligent monitoring systems.

Bayes Theorem↗
PubMed1994

Heart signal recognition by Hidden Markov Models: the ECG case.

Wave recognition in ECG signals by Hidden Markov Models (HMMs) relies on the stationary assumption for the set of parameters used to describe ECG waves. This approach seems unnatural and consequently generates severe errors in practice. A new class of HMMs called Modified Continuous Variable Duration HMMs is proposed to account for the specific properties of the ECG signal. An application of the latter, coupled with a multiresolution front-end analysis of the ECG is presented. Results show these methods can increase the performance of ECG recognition compared to classical HMMs.

Artificial Intelligence↗
PubMedDate not supplied

Surface electromyogram automatic analysis and Guillain-Barré syndrome follow up.

An automatic and quantitative framework for the analysis of surface EMG interference patterns at maximal non-controlled force is described. The main contents are focused on different ways to represent surface EMG signals and to analyse collected data set through principal component analysis. The methods are tested on control individuals and on patients with Guillain-Barré Syndrome examined at different moments after the beginning of the disease (first week, three months, and one year). The results exhibit that a high diagnostic yield can be provided and furthermore that quantification and differentiation can be expected. Surface EMG interference pattern may be hoped to be a useful tool in daily practice.

Adult↗
PubMedDate not supplied

Segmentation of depth-EEG seizure signals: method based on a physiological parameter and comparative study.

The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.

Algorithms↗