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B H Jansen

Publications and source records attributed to B H Jansen.

15 recordsLinked to original sources
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↗
PubMed1978

Quantification of EEG variability.

This paper presents a method of segmenting the EEG based on the well-known power spectrum analysis. This procedure is applied to the EEG recordings of two normal subjects in order to determine the temporal EEG variability. These results are compared with a more classical approach.

Adult↗
PubMed1978

Demonstration of segmentation techniques for EEG records.

In this paper three different techniques for segmenting EEG's are presented. The principles of these techniques (the Kalman filter approach, the power spectrum analysis and the texture matrix approach) are explained and the results obtained summarised. The segmentation is used in an interactive EEG interpretation system.

Adult↗
PubMedDate not supplied

Pre-stimulus spectral EEG patterns and the visual evoked response.

The relationship between the latencies and amplitudes of the N1 and P2 components of the visual evoked potential (VEP) and the psychophysiological state of the brain immediately preceding the time of the stimulus has been investigated in 7 male subjects. Power spectral measures in the delta, theta, alpha and beta bands of the 1 sec pre-stimulus EEG were used to assess the brain state, and low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. Trials were ranked separately according to the relative amounts of pre-stimulus power in each EEG band and were partitioned into groups by an equal pre-stimulus spectral power criterion. Averaged EPs were computed from these groups and multiple regression analysis was used to relate pre-stimulus spectral power values to EP features. Five of the 7 subjects displayed consistent increases in N1-P2 amplitude as a function of increasing pre-stimulus relative alpha power. The between-subjects effect of pre-stimulus EEG on N1 latency was small, but was moderate for P2 latency (both significant). Both N1 and P2 latency were found to decrease with increasing amounts of pre-stimulus relative delta and theta power.

Adult↗
PubMedDate not supplied

The effect of the phase of prestimulus alpha activity on the averaged visual evoked response.

The relationship between the latencies and amplitudes of the N1 and P2 components of the averaged visual evoked potential (EP) and the phase of the alpha activity immediately preceding the time of the stimulus, has been investigated in 7 male subjects. Low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. The phase angle of the EEG at the moment of stimulation was computed for all trials containing more than 100 microV2 of prestimulus alpha power. The single trials were grouped into 8 classes on the basis of the phase angle value, and averaged EPs for each individual were computed from these groups. In addition, averaged EPs were computed in 3 ways: (1) a grand average consisting of all artifact-free trials, (2) an 'alpha average' consisting of all trials containing more than 100 microV2 of prestimulus alpha power, and (3) a 'non-alpha average' consisting of all trials with less than 100 microV2 of prestimulus alpha power. Each of these 3 averages were cross-correlated with the phase-selective averages. It was found that the particular N1 component assessed in this experiment may possibly be entrained alpha activity, and that the measured P2 component is not an alpha process, yet it is influenced by the amount of prestimulus alpha activity.

Adult↗