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Biomedical subjects

B H Jansen

Publications and source records attributed to B H Jansen.

At least 19 recordsLinked to original sources
PubMed2001

Evoked potential enhancement using a neurophysiologically-based model.

OBJECTIVE: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. METHOD: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur. RESULTS: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual's response varies considerably from trial to trial. CONCLUSION: The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages.

Adult↗
PubMed2000

Development and evaluation of the piecewise Prony method for evoked potential analysis.

A new method is presented to decompose nonstationary signals into a summation of oscillatory components with time varying frequency, amplitude, and phase characteristics. This method, referred to as piecewise Prony method (PPM), is an improvement over the classical Prony method, which can only deal with signals containing components with fixed frequency, amplitude and phase, and monotonically increasing or decreasing rate of change. PPM allows the study of the temporal profile of post-stimulus signal changes in single-trial evoked potentials (EPs), which can lead to new insights in EP generation. We have evaluated this method on simulated data to test its limitations and capabilities, and also on single-trial EPs. The simulation experiments showed that the PPM can detect amplitude changes as small as 10%, rate changes as small as 10%, and 0.15 Hz of frequency changes. The capabilities of the PPM were demonstrated using single electroencephalogram/EP trials of flash visual EPs recorded from one normal subject. The trial-by-trial results confirmed that the stimulation drastically attenuates the alpha activity shortly after stimulus presentation, with the alpha activity returning about 0.5 s later. The PPM results also provided evidence that delta activity undergoes phase alignment following stimulus presentation.

Algorithms↗
PubMed1997

A fuzzy clustering approach to study the auditory P50 component in schizophrenia.

We have recently provided evidence that selective evoked response averaging based on a fuzzy clustering approach is a useful way to increase the signal-to-noise ratio, particularly when recording low-amplitude components, such as the auditory P50. We have also reported that, when stimuli are delivered in pairs (S1 followed by S2) with a short interstimulus interval, the first stimulus (S1) results in synchronization of the EEG producing a large-amplitude evoked response, whereas the second stimulus (S2) causes phase opposition resulting in a lower amplitude average evoked response. In the current study we reanalyzed data previously obtained from 13 normal volunteers and 17 chronic schizophrenia patients. Our results show that the partial EPs corresponding to the S1 stimulus are highly synchronized in normal subjects but not in schizophrenia patients. However, such a synchronization is not present after delivery of the S2 stimulus, neither in normal controls nor in patients. These findings are in agreement with previous reports of decreased amplitude of the S1 response without a significant further decrease in the amplitude of the S2 response in schizophrenia patients.

Adult↗
PubMed1997

A fuzzy clustering approach to EP estimation.

The problem of extracting a useful signal (a response) buried in relatively high amplitude noise has been investigated, under the conditions of low signal-to-noise ratio. In particular, we present a method for detecting the "true" response of the brain resulting from repeated auditory stimulation, based on selective averaging of single-trial evoked potentials. Selective averaging is accomplished in two steps. First, an unsupervised fuzzy-clustering algorithm is employed to identify groups of trials with similar characteristics, using a performance index as an optimization criterion. Then, typical responses are obtained by ensemble averaging of all trials in the same group. Similarity among the resulting estimates is quantified through a synchronization measure, which accounts for the percentage of time that the estimates are in phase. The performance of the classifier is evaluated with synthetic signals of known characteristics, and its usefulness is demonstrated with real electrophysiological data obtained from normal volunteers.

Algorithms↗
PubMed1996

Nonlinear dynamics and quantitative EEG analysis.

Quantitative, computerized electroencephalogram (EEG) analysis appears to be based on a phenomenological approach to EEG interpretation, and is primarily rooted in linear systems theory. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of nonlinear dynamics and chaos theory, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing nonlinearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. A review of the fundamentals of chaos theory is provided. Evidence supporting the nonlinear dynamics paradigm to EEG interpretation is presented, and the kind of new information that can be extracted from the EEG is discussed. A case is made that a nonlinear dynamic systems viewpoint to EEG generation will profoundly affect the way EEG interpretation is currently done.

Brain↗
PubMed1995

Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns.

This study deals with neurophysiologically based models simulating electrical brain activity (i.e., the electroencephalogram or EEG, and evoked potentials or EPs). A previously developed lumped-parameter model of a single cortical column was implemented using a more accurate computational procedure. Anatomically acceptable values for the various model parameters were determined, and a multi-dimensional exploration of the model parameter-space was conducted. It was found that the model could produce a large variety of EEG-like waveforms and rhythms. Coupling two models, with delays in the interconnections to simulate the synaptic connections within and between cortical areas, made it possible to replicate the spatial distribution of alpha and beta activity. EPs were simulated by presenting pulses to the input of the coupled models. In general, the responses were more realistic than those produced using a single model. Our simulations also suggest that the scalp-recorded EP is at least partially due to a phase reordering of the ongoing activity.

Animals↗
PubMed1994

Selective averaging of evoked potentials using trajectory-based clustering.

A clustering method has been developed to group evoked potentials that display similar prestimulus dynamic behavior. The procedure involves using the method of time delay embedding to construct a trajectory in state space from a time series. Certain features that characterize the geometry of the trajectory have been defined. The trajectory-based clustering algorithm has been applied to visual evoked potentials to determine relationships between prestimulus EEG and evoked potential shape.

Cluster Analysis↗
PubMed1993

A neurophysiologically-based mathematical model of flash visual evoked potentials.

Evidence is presented that a neurophysiologically-inspired mathematical model, originally developed for the generation of spontaneous EEG (electroencephalogram) activity, can produce VEP (visual evoked potential)-like waveforms when pulse-like signals serve as input. It was found that the simulated VEP activity was mainly due to intracortical excitatory connections rather than direct thalamic input. Also, the model-generated VEPs exhibited similar relationships between prestimulus EEG characteristics and subsequent VEP morphology, as seen in human data. Specifically, the large correlation between the N1 amplitude and the prestimulus alpha phase angle, and the insensitivity of P2 to the latter feature, as observed in actual VEPs to low intensity flashes, was also found in the model-generated data. These findings provide support for the hypothesis that the spontaneous EEG and the VEP are generated by some of the same neural structures and that the VEP is due to distributed activity, rather than dipolar sources.

Cerebral Cortex↗
PubMed1993

Sleep staging with movement-related signals.

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Adult↗
PubMed1991

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.

Algorithms↗
PubMed1991

Monitoring of the ballistocardiogram with the static charge sensitive bed.

The static charge sensitive bed (SCSB) consists of a sensitive movement detector embedded in a mattress. When a subject rests on the bed, a single electrical signal, containing components reflective of cardiac, respiratory, and body movement related motion, is produced. This paper describes a digital signal processing technique to separate the BCG from the SCSB signal. An evaluation of this algorithm was conducted using recordings from normal volunteers. Comparisons with simultaneously recorded reference signals indicated that the algorithm performed satisfactorily in a laboratory environment.

Algorithms↗
PubMed1991

The relationship between prestimulus-alpha amplitude and visual evoked potential amplitude.

Root-mean-square (RMS) amplitude derived from power spectral measures in the alpha band of the 1 s prestimulus EEG were related to the peak-to-peak amplitude of the N1 and P2 components (N1P2PP) of the visual evoked potential (VEP) in 7 male subjects. Stimuli were low intensity flashes delivered randomly between 2 and 6 whole seconds. Trials were rank ordered according to the levels of prestimulus alpha amplitude and were partitioned into groups of 40 trials each (25 groups per data set). Averaged VEPs were computed from these groups and scattergrams of N1P2PP and enhancement factor (following the approach by Başar, 1980) vs. prestimulus alpha amplitude were produced. There was a correlation of 0.74 (p less than .0001) between prestimulus alpha amplitude and N1P2PP, and all seven subjects displayed a general inverse relationship between VEP enhancement and prestimulus alpha amplitude, replicating the results of Başar. However, we observed an exponential relationship, rather than the linear relationship reported by Başar.

Adult↗
PubMed1989

Knowledge-based approach to sleep EEG analysis--a feasibility study.

A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages ("objects") are represented in terms of frames. The latter capture the morphological and spatio-temporal information for each object. An object detection module ("frame matcher"), operating on the frames, is employed to identify what features need to be extracted from the EEG and to trigger the appropriate "specialist"--specialized signal processing modules--to obtain values for these features. This leads to an opportunistic approach to EEG interpretation with quantitative information being extracted from the signal only when needed by the reasoning processes. The system has been tested on the detection of K complexes and sleep spindles. Its performance indicates that the approach followed is feasible and can become a powerful tool for automated EEG interpretation.

Electroencephalography↗
PubMed1988

Structural EEG analysis: an explorative study.

A method is described to detect (subtle) changes in an EEG (electroencephalogram) by means of a Markovian modeling approach. This method, termed structural EEG analysis, treats the non-stationary EEG as a sequence of a finite number of short elementary patterns. Subtle changes in an EEG may be detected by studying the transition probabilities between the different patterns. By viewing the patterns as states in a Markov chain, a representation of the EEG structure based on a state transition probability matrix emerges. Various techniques to estimate the state transition probability matrices have been investigated. A number of experiments were performed with artificially generated data to determine the data length required to obtain a reliable estimate of the transition matrices. It appeared that a data length of approximately five to eight times the number of entries in the matrices is needed to accurately estimate the matrices. It was determined that the data length required to reliably estimate the transition probability matrix is dependent on the number of states and the number of non-zero entries of the matrix. Also, the data length appears independent of the values of the probabilities. The structural analysis approach was applied to actual EEG data, recorded from normal volunteers and epileptic subjects. It was demonstrated that visually confirmable changes in the EEG could be detected by the structural analysis method more accurately than by a more conventional approach.

Adult↗
PubMed1985

Automated morphological analysis by means of dynamic time-warping.

A new technique for the clustering of EEG wave forms is proposed. This method, termed dynamic time-warping (DTW) based clustering, involves the determination of a distance measure by allowing a certain degree of flexibility in the time axes of the two waves to be compared. Sharp waves and spikes, taken from actual EEG data, were subjected to the DTW-clustering approach. The results were compared with an approach based on features extracted from the wave forms and one based on computing the peak-aligned difference between wave forms. It was found that the DTW approach resulted in more homogeneous clusters than the other two approaches. These results, although preliminary, clearly indicate the feasibility of applying this new method for wave form clustering.

Electroencephalography↗
PubMed1985

EEG waveform analysis by means of dynamic time-warping.

The feasibility of using dynamic time-warping (DTW) to cluster EEG waveforms was studied. DTW compresses and extends the time axes of pairs of digitized waveforms to reduce the effects of minor differences in shape due to noise and normal, random shape fluctuations. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity index in a clustering procedure. Experiments with simulated data revealed that DTW based clustering could distinguish between waves only slightly different in frequency, amplitude, peak location, or initial phase. DTW clustering was also applied to sharp waves and spikes taken from actual EEG data and compared with an approach based on features extracted from the waveforms, and one based on computing the peak-aligned difference between waveforms. The results indicated that the DTW approach yielded more homogeneous clusters than the other two methods.

Biometry↗
PubMed1985

Analysis of biomedical signals by means of linear modeling.

The recording and subsequent analysis of electrical signals of physiological origin constitutes an important aspect of current biomedical research. A versatile method for the analysis of such signals is based on linear, i.e., autoregressive (moving average) modeling. These techniques are based on fitting a hypothetical model to the signal under observation. These models are capable of generating the original signal by a linear combination of past observations and past and present noise samples. High resolution spectral estimates can be obtained in this way. Also, the often small number of model coefficients offer a concise description of the signal and may be used for classification purposes. Other applications entail the detection of nonstationarities, data-compression, and signal enhancement. In this review, linear modeling methods for the analysis of electroencephalograms, electro- and phono-cardiograms, electromyograms, and gastrointestinal signals are surveyed.

Digestive System Physiological Phenomena↗