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At least 307 records · Page 17Linked to original sources
PubMedDate not supplied

Monitoring anesthesia using neural networks: a survey.

New methods of data processing combined with advances in computer technology have revolutionized monitoring of patients under anesthesia. The development of systems based on analysis of brain electrical activity (EEG or evoked potentials) by neural networks has provided impetus to many investigators. Though not claiming to be the end-all in patient monitoring, the potential and efficiency of the combination does indeed stand out. Various strategies are presented and discussed, as well as suggestions for further investigation.

Anesthesia↗
PubMedDate not supplied

Scenario recognition for temporal reasoning in medical domains.

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Déjà Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.

Airway Obstruction↗
PubMed2003

[Fuzzy control of the physical training intensity based on neural-network].

Using computer technique, artificial neural network and fuzzy control theory, we have explored a real-time control method for the athlete's physical workload intensity in order that the goal of physical training can be reached effectively in accordance to the exercise plan. The technique could be useful for improving the efficiency of scientific physical training.

Algorithms↗
PubMedDate not supplied

Electrical impedance cardiography using artificial neural networks.

This study evaluates the use of artificial neural networks to estimate stroke volume from pre-processed, thoracic impedance plethysmograph signals from 20 healthy subjects. Standard back-propagation was used to train the networks, with Doppler stroke volume estimates as the desired output. The trained networks were then compared to two classical biophysical approaches. The coefficient of determination (R2 x 100%) between the biophysical approaches and the Doppler was 8.20% and 9.90%, while it was 77.38% between the best neural network and the Doppler. Among these methods, only the neural network residuals had a significant zero mean Gaussian distribution (alpha=0.05). Our results indicate that an invertible relationship may exist between thoracic bioimpedance and stroke volume, and that artificial neural networks may offer a potentially advantageous approach for estimating stroke volume from thoracic electrical impedance, both because of their ease of use and their lack of confounding assumptions.

Adult↗
PubMed2002

Simplifying metabolic complexity.

A complete description of the regulation of metabolism in even a single cell will be very hard to achieve, enormous and indigestible. However, there are two powerful ways to simplify the complexity. Firstly, related processes and intermediates can be grouped into a small number of modules, and the regulation of the simplified system can be studied. Secondly, control analysis can be used. With these simplifications to illuminate the important regulatory features, even a full description could be made intellectually and experimentally accessible without distorting the essential regulatory features. Modular control analysis is powerful because it can quantify the relative importance of different flows of regulatory information through any metabolic, physiological, signalling or transcriptional network. It can answer global questions about the importance of different pathways mediating any change to a system. It has been used to analyse how cadmium, a poison with multiple effects, changes oxidative phosphorylation in isolated mitochondria, and to quantify the regulation of energy metabolism in hepatocytes. It has been used to measure how energy metabolism is regulated during mitogen stimulation of thymocytes, quantifying the relative importance of different signalling pathways and how each pathway contributes to the activation of energy metabolism. Recently, we have applied modular control analysis to modern DNA microarray expression profiling to measure the importance of different groups of mRNA transcripts in mediating physiological responses.

Adenosine Triphosphate↗
PubMed1998

Neural network technique for detecting emergency states in neurosurgical patients.

The problem of reliable detection of life-threatening situations in the neurosurgical patient undergoing treatment in the ICU is still far from reaching a satisfactory solution, although several methods of clinical and instrumental evaluation have recently been developed for the early detection of oncoming signs of danger. Continuous monitoring of intracranial pressure (ICP) provides neurosurgeons with valuable information about the current condition of the patient. However, it is increasingly felt that traditional methods of extracting information from the ICP signal have reached their natural limits, mostly because of difficulties in fitting the appropriate mathematical model to this non-linear and non-stationary process. Successful implementations of artificial neural networks in many medical tasks have encouraged the application of this method of ICP processing. Two problems are considered: the prediction of trends in ICP, and recognition of the configuration of unfavourable symptoms likely to signal danger for the neurosurgical patient. The construction of neural network predictors of ICP trends is based on wavelet pre-processing of the original signal. The approach to the second task involves pre-processing of the ICP with spectral and statistical methods and classification of the extracted features of the current signal on an arbitrarily selected scale of danger.

Brain Diseases↗
PubMed2003

Traveling-wave pattern generator controls movement and organization of sensory feedback in a spinal cord model.

A traveling wave in a two-dimensional spinal cord model constitutes a stable pattern generator for quadruped gaits. In the context of the somatotopic organization of the spinal cord, this pattern generator is sufficient to generate stable locomotive limb trajectories. The elastic properties of muscles alone, providing linear negative feedback, are sufficient to stabilize stance and locomotion in the presence of perturbative forces. We further show that such a pattern generator is capable of organizing sensory processing in the spinal cord. A single-layer perceptron was trained to associate the sensory feedback from the limb (coding force, length, and change of length for each muscle) with the two-dimensional activity profile of the traveling wave. This resulted in a well-defined spatial organization of the connections within the spinal network along a rostrocaudal axis. The spinal network driven by peripheral afferents alone supported autonomous locomotion in the positive feedback mode, whereas in the negative feedback mode stance was stabilized in response to perturbations. Systematic variation of a parameter representing the effect of gamma-motor neurons on muscle spindle activity in our model led to a corresponding shift of limb position during stance and locomotion, resulting in a systematic displacement alteration of foot positions.

Feedback, Physiological↗
PubMed1997

Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals.

This technical note describes a robust version of moving averages, that enables reliable monitoring of the evoked potential (EP) signals. A cluster analysis (CA) procedure is introduced to robustify the signal averaging (SA). It is implemented via a Hopfield neural network (HNN), which performs selection of the trials forming a cluster around the current state of the EP signal. The core of this cluster serves as an estimate of the instantaneous EP. The effectiveness of the method, indicated by application to real data, and its computation efficiency, due to the use of simple matrix operations, makes it very promising for clinical observations.

Electroencephalography↗
PubMed1992

[Improved learning capacity and discrimination performance of neural networks in pattern recognition of biosignals].

Pattern recognition was an important goal in the early work on artificial neural networks. Without arousing dramatic speculation, the paper describes, how a "natural" method of dealing with the configuration of the input layer can considerably improve learning behaviour and classification rate of a modified multi-layered perception with backpropagation of the error learning rule. Using this method, recognition of complex patterns in electrophysiological signals can be performed more accurately, without rules or complicated heuristic procedures. The proposed technique is demonstrated using recognition of the J-point in the ECG as an example.

Electrocardiography↗
PubMed2005

Cell therapy for neurological disorders: a comprehensive review.

Neurodegenerative diseases are characterized by the irreversible loss of neurons involved in networks, important for specific physiological functions. At present, several renewable cell sources stand in line to replace fetal brain cells as potential cell source for transplantation in the damaged brain. Recent developments raise the hope that selective populations of different neuronal phenotypes could be made "on demand". However, for every potential cell source there are still a lot of questions and drawbacks, which need to be resolved before a cell source could become the standard for clinical neuronal transplantation. The recent finding that the brain responds to damage by increased endogenous neurogenesis could prelude new "neurothrophic therapies", based on stimulating this endogenous repair. From preclinical studies it is evident that different disease mechanisms require different cell therapy approaches, depending on the underlying factor of the disease, the identity of neuronal systems that are involved and the complexity of networks that are affected. In this review the potential of different cell sources, including the endogenous progenitor cells, are discussed. Also results of preclinical and clinical transplantation studies in three different disease models are critically evaluated.

Animals↗
PubMed1995

Variability of Doppler microembolic signal counts in patients with prosthetic cardiac valves.

BACKGROUND AND PURPOSE: The purpose of this study was the evaluation of intraobserver, interobserver, and intrasubject variability in the Doppler detection of microembolic signals in patients with mechanical prosthetic valves. Simultaneously, the feasibility of automated embolus detection by means of a neuronal network was investigated. METHODS: From 25 patients with mechanical prosthetic heart valves, single transcranial Doppler monitoring sessions of 30 minutes' duration were recorded on videotape, randomized, and subsequently analyzed by eight independent trained observers from three centers. Three observers evaluated these tapes on three separate occasions, blinded to their previous results. An additional 48 patients with prosthetic heart valves were repetitively monitored with transcranial Doppler ultrasonography for 30 minutes three times within 1 year to examine the long-term variability in the occurrence of microembolic signals. Finally, in an effort to assess the short-term intrasubject variability, 20 patients were examined for 90 minutes, and the results of the three 30-minute periods were compared. The interobserver, intraobserver, and intrasubject (both short- and long-term) variability was evaluated. RESULTS: No significant differences in microembolic signal counts were found among the different observers, between the human observers and the neuronal network, or among the three separate evaluations of stored data by the same observer. The same was true for repeat examinations of the same patient (P > .05, Student's two-paired t test and Friedman's test). CONCLUSIONS: The detection of microembolic signals in patients with prosthetic cardiac valves is a reproducible technique. The reliable performance of the neuronal network argues for a broader use of this device. The intrasubject stability of the microembolic rate over 1 year supports the concept that the underlying emboligenic process is associated with intrinsic mechanical properties of the valve implant and not due to a thromboembolic process in the heart.

Cerebral Arteries↗
PubMed1994

Event detection on patient monitoring data using Causal Probabilistic Networks.

Applications are described of the knowledge-representation technique Causal Probabilistic Networks for the detection of events related to the patient and the monitoring equipment, in the context of intensive care patient monitoring. The purpose of the event detection is to reduce the number of false alarms generated by the patient monitor, and to generate alarms related to patient or equipment events rather than to threshold violation. A PC-based system has been developed and tested on patient data. A system for real-time use in patient monitoring is under development.

Diagnosis, Computer-Assisted↗
PubMed1998

Anesthesia control using midlatency auditory evoked potentials.

This paper shows the development of a system to control inhalation anesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEP's). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anesthetic levels to obtain a data training set. The MLAEP's were compacted by means of discrete time wavelet transform (DTWT), and together with anesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only three features were necessary for this recognition. These features were then used to train a four-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of ten animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.

Anesthesia↗
PubMed2006

A new synaptic plasticity rule for networks of spiking neurons.

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.

Algorithms↗
PubMed1995

Computer-assisted decision support systems for patient management in an intensive care unit.

The application of the intelligent monitoring techniques of case-based reasoning and neural network analysis to physician decision making concerning patient care in an Intensive Car Unit (ICU) is described. Case-based reasoning offers a model for quickly matching--using a predetermined hierarchical structure--a single patient's parameters (text or numeric) to similar parameters contained in a clinical database. The output produces a group of patients which may be set to match exactly on certain characteristics and may also be set to match "as closely as possible" on a gradient of patient properties. Clinicians may thus use the system to find the group of the closest matching cases to their current patient. Aspects of the ICU history of the selected group may then be displayed graphically (e.g., mortality, length of stay, hours of ventilation, procedures utilized, and complications encountered). Neural network analysis is a pattern recognition technique which uses a training set of patient data (text or numeric) to seek mathematical relationships between various subsets of patient parameters. The discovered relationships from the training set are then applied to estimate the outcomes (e.g., mortality, length of stay, hours of ventilation) of new patients. The effects of these intelligent monitoring techniques are scheduled to be tested in a field trial held in a regional referral center ICU.

Algorithms↗
PubMed2003

Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis.

Determination of the adequacy of dialysis is a routine but crucial procedure in patient evaluation. The total dialysis dose, expressed as Kt/V, has been widely recognised to be a major determinant of morbidity and mortality in haemodialysed patients. Many different factors influence the correct determination of Kt/V, such as urea sequestration in different body compartments, access and cardiopulmonary recirculation. These factors are responsible for urea rebound after the end of the haemodialysis session, causing poor Kt/V estimation. There are many techniques that try to overcome this problem. Some of them use analysis of blood-side urea samples, and, in recent years, on-line urea monitors have become available to calculate haemodialysis dose from dialysate-side urea kinetics. All these methods require waiting until the end of the session to calculate the Kt/V dose. In this work, a neural network (NN) method is presented for early prediction of the Kt/V dose. Two different portions of the dialysate urea concentration-time profile (provided by an on-line urea monitor) were analysed: the entire curve A and the first half B, using an NN to predict the Kt/V and compare this with that provided by the monitor. The NN was able to predict Kt/V is the middle of the 4h session (B data) without a significant increase in the percentage error (B data: 6.69% +/- 2.46%; A data: 5.58% +/- 8.77%, mean +/- SD) compared with the monitor Kt/V.

Adult↗
PubMed2001

[Neural networks and pain processing. New insights from imaging techniques].

Imaging techniques with high spatial and temporal resolution (PET,fMRI, MEG) provide detailed information about the brains' processing of pain. Structures detected by these techniques are not understood as pain centers but as nodal points of a dynamic network which is influenced by physiological and psychological input. Imaging techniques can be used for the investigation of different pain components. The neuronal network that encodes sensory-discriminative information consists of the primary and secondary somatosensory cortex which receive input from lateral thalamic nuclei. Information for the affective pain component reach the anterior cingulate cortex, insula and prefrontal cortex via medial thalamic nuclei. Until now only little is known about cortical structures mediating the cognitive pain component. In chronic pain the cortical and subcortical processing of nociceptive input is presumably modified. Reorganization in the primary somatosensory cortex is presented as an example of neuronal plasticity induced by chronic pain.

Animals↗
PubMed1996

Use of adaptive Hilbert transformation for EEG segmentation and calculation of instantaneous respiration rate in neonates.

Broad, as well as narrow, band Hilbert transform filters (HTFs) were used as preprocessing units in the analysis of electroencephalogram (EEG) and respiratory movements in neonates. For these applications, new algorithms for the adaptation of the resonance frequency of a narrow-band-pass filter to the actual signal properties on the basis of an analytic filter design were developed. For the segmentation of the discontinuous EEG, the location of the resonance frequency was imbedded into the learning algorithm of a neural network (NN). In such automatic EEG pattern recognition, the detection of spike activity was taken into consideration. The spike detection scheme introduced uses broad-band HTFs as basis units. Additionally, the algorithm for the continuous control of the resonance frequency was applied to achieve the adaptation of the processing unit that performed the calculation of the instantaneous respiration rate, in this framework, a new on-line method for adaptive frequency estimation that is less sensitive to low signal-to-noise ratios (SNRs) was obtained. The new approaches introduced were tested in comparison with processing methods that have been established for the analysis of experimental and clinical data.

Algorithms↗