New approach in features extraction for EEG signal detection

This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.

Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit

Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely-used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.

Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.

Analysis of dynamic brain oscillations: methodological advances

In recent years, new recording technologies have advanced such that, at high temporal and spatial resolutions, oscillations of neuronal networks can be identified from simultaneous, multisite recordings. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods that can extract meaningful information relating to time, frequency and space. Here, we aim to bridge this gap by focusing on up-to-date recording techniques for measurement of network oscillations and new analysis tools for their quantitative assessment. In particular, we emphasize how these methods can be applied, what property might be inferred from neuronal signals and potentially productive future directions. This review is part of the INMED and TINS special issue, Physiogenic and pathogenic oscillations: the beauty and the beast, derived from presentations at the annual INMED and TINS symposium (http://inmednet.com).

Usefulness of an Intraoperative Electrophysiological Navigator System for Subthalamic Nucleus Surgery in Parkinson’s Disease

OBJECTS

The characteristics and benefits are presented of an intraoperative neurophysiological navigator computerized system developed in our center (Columbus-Track 1.0) that helps the surgical team in neuronal identification and in strategy processes in subthalamic nucleus surgery for Parkinson's disease (PD).

METHODS

The navigator consists of three assembled parts: (1) neuronal identification, based on wavelet processing, filtering and gaussian characteristics of the signal; (2) track identification, based on anatomical coincidence, somatomotor response and microstimulation quotient, and (3) strategy, coordinating correction for the next track. A retrospective comparative study was performed with 15 consecutive PD patients (30 targets) operated without the system and the next 15 consecutive patients operated with the aid of the system. With the aid of the computerized navigation system, a significant reduction in the number of tracks was observed (t = -2.503, p = 0.0015), with a mean difference of 1.2 tracks per hemisphere. A non-significant reduction in the total intervention time was also observed, with a mean difference of 20 min per hemisphere (t = -1.418, p = 0.161).

CONCLUSIONS

The intraoperative computerized navigation system can aid the surgical team in better identifying the neuronal signal and in defining the optimal track to achieve the target.

Decomposing ERP time–frequency energy using PCA

OBJECTIVE

Time-frequency transforms (TFTs) offer rich representations of event-related potential (ERP) activity, and thus add complexity. Data reduction techniques for TFTs have been slow to develop beyond time analysis of detail functions from wavelet transforms. Cohen's class of TFTs based on the reduced interference distribution (RID) offer some benefits over wavelet TFTs, but do not offer the simplicity of detail functions from wavelet decomposition. The objective of the current approach is a data reduction method to extract succinct and meaningful events from both RID and wavelet TFTs.

METHODS

A general energy-based principal components analysis (PCA) approach to reducing TFTs is detailed. TFT surfaces are first restructured into vectors, recasting the data as a two-dimensional matrix amenable to PCA. PCA decomposition is performed on the two-dimensional matrix, and surfaces are then reconstructed. The PCA decomposition method is conducted with RID and Morlet wavelet TFTs, as well as with PCA for time and frequency domains separately.

RESULTS

Three simulated datasets were decomposed. These included Gabor logons and chirped signals. All simulated events were appropriately extracted from the TFTs using both wavelet and RID TFTs. Varying levels of noise were then added to the simulated data, as well as a simulated condition difference. The PCA-TFT method, particularly when used with RID TFTs, appropriately extracted the components and detected condition differences for signals where time or frequency domain analysis alone failed. Response-locked ERP data from a reaction time experiment was also decomposed. Meaningful components representing distinct neurophysiological activity were extracted from the ERP TFT data, including the error-related negativity (ERN).

CONCLUSIONS

Effective TFT data reduction was achieved. Activity that overlapped in time, frequency, and topography were effectively separated and extracted. Methodological issues involved in the application of PCA to TFTs are detailed, and directions for further development are discussed.

SIGNIFICANCE

The reported decomposition method represents a natural but significant extension of PCA into the TFT domain from the time and frequency domains alone. Evaluation of many aspects of this extension could now be conducted, using the PCA-TFT decomposition as a basis.

Spectral analysis of slow modulation of EEG amplitude and cardiovascular variables in subjects with postural tachycardia syndrome

OBJECTIVE

Previous studies have reported slow (<0.5 Hz) modulation of electroencephalographic (EEG) background amplitude and suggested that this reflects periodic neuronal activity in the brainstem, such as may be recorded from cardiovascular and respiratory centers in animals. We searched for a relationship between EEG amplitude modulation and modulation of simultaneously recorded cardiovascular variables and attempted to determine whether this relationship was altered in subjects with postural tachycardia syndrome (POTS).

METHODS

We recorded EEG, blood flow velocity in the middle cerebral artery (MCA), heart rate, respirations, and blood pressure from subjects with POTS and controls during head-up tilt. Time-frequency analysis of 0.512-s epochs of EEG was performed to determine peak alpha amplitude. Spectra were divided into 3 bands: ultraslow, middle, and respiratory.

RESULTS

EEG alpha amplitude modulation in all frequency bands was reduced in POTS subjects while supine. EEG modulation decreased in controls with head-up tilt but not in POTS subjects. Heart rate modulation in the respiratory frequency band decreased with head-up tilt and was significantly less (P<0.02) in ultraslow and respiratory frequency bands in POTS subjects after head-up tilt. Blood pressure and MCA flow velocity modulation in middle and respiratory bands increased with head-up tilt to a greater degree in POTS subjects. Blood pressure and MCA flow velocity modulation frequencies were moderately correlated, but correlations between EEG and cardiovascular variable modulation frequencies were generally low, being highest in the respiratory band but not statistically significant.

CONCLUSION

There are subtle differences in EEG amplitude modulation in subjects with POTS. Altered EEG amplitude modulation in POTS may reflect altered brainstem physiology in this disorder.

"Convulsive" nonepileptic seizures have a characteristic pattern of rhythmic artifact distinguishing them from convulsive epileptic seizures

PURPOSE

Approximately 30% of patients admitted for video-EEG monitoring have psychogenic nonepileptic seizures (PNES). Differentiation of "convulsive" PNES from convulsive seizures can be difficult. The EEG often displays rhythmic movement artifact that may resemble seizure activity and confound the interpretation. We sought to determine whether time-frequency mapping of the rhythmic EEG artifact during "convulsive" PNES reveals a pattern that differs from that of epileptic seizures.

METHODS

EEGs from 15 consecutive patients with "convulsive" PNESs were studied with time-frequency mapping by using NEUROSCAN and compared with 15 patients with convulsive epileptic seizures. Fast Fourier transforms (FFTs) were performed to determine the dominant frequency for 1- to 2-s windows every 2 s through the seizures.

RESULTS

The dominant frequency remained stable within a narrow range for the duration of the PNES, whereas in the epileptic seizures, it evolved through a wide range. The coefficient of variation of the frequency during the seizures was considerably less for patients without epilepsy (median, 15.0%; range, 7.2-23.7% vs. median, 58.0%; range, 34.8-92.1%; p < 0.001). The median frequency did not differ significantly between groups (4.2 vs. 4.6 Hz; p = 0.290).

CONCLUSIONS

"Convulsive" PNES display a characteristic pattern on time-frequency mapping of the EEG artifact, with a stable, nonevolving frequency that is different from the evolving pattern seen during an epileptic seizure.

Advances in quantitative electroencephalogram analysis methods

Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.

Characterization of Event Related Potentials Using Information Theoretic Distance Measures

Analysis of event-related potentials (ERPs) using signal processing tools has become extremely widespread in recent years. Nonstationary signal processing tools such as wavelets and time-frequency distributions have proven to be especially effective in characterizing the transient phenomena encountered in event-related potentials. In this paper, we focus on the analysis of event-related potentials collected during a psychological experiment where two groups of subjects, spider phobics and snake phobics, are shown the same set of stimulus: A blank stimulus, a neutral stimulus and a spider stimulus. We introduce a new approach, based on time-frequency distributions, for analyzing the ERPs. The difference in brain activity before and after a stimulus is presented is quantified using distance measures as adapted to the time-frequency plane. Three different distance measures, including a new information theoretic distance measure, are applied on the time-frequency plane to discriminate between the responses of the two groups of subjects. The results illustrate the effectiveness of using distance measures combined with time-frequency distributions in differentiating between the two classes of subjects and the different regions of the brain.

Wavelet based automatic seizure detection in intracerebral electroencephalogram

BACKGROUND

Automatic seizure detection is often used during long-term monitoring, and is particularly important during intracerebral investigations. Existing methods make many false detections, particularly in intracerebral electroencephalogram (EEG) because of frequent large amplitude rhythmic activity bursts that are non-epileptiform.

OBJECTIVE

To develop a seizure detection method for intracerebral monitoring that is as sensitive as existing methods but has fewer false detections.

METHODS

To capture the rhythmic nature of seizure discharges, we developed a wavelet-based method, examining how different frequency ranges fluctuate compared to the background. In particular, the system remembers rhythmic bursts occurring commonly in the background to avoid detecting them as seizures.

RESULTS

The method was evaluated on test data from 11 patients, including 229 h and 66 seizures, and its performance compared to the method of Gotman (Electroencephalogr clin Neurophysiol 76 (1990) 317). Detection sensitivity was unchanged at close to 90%, but false detections were reduced from 2.4 to 0.3/h.

CONCLUSIONS

Perfect sensitivity is unlikely because the morphology of seizure discharges is so variable. Nevertheless, the 87% sensitivity obtained in the combined training and testing data is quite high. We reduced the average false alarm rate to one per 3 h of recording, or 6 per 24-h period. Given how rapidly one can decide visually that a detection is erroneous, false detections should not cause any burden to the reviewer.

SIGNIFICANCE

In intracerebral EEG it is possible to detect seizures automatically with high sensitivity and high specificity.

Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (>>30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Analysis and localization of epileptic events using wavelet packets

This article compares results obtained in previous studies using time-frequency representations (Wigner-Ville, Choi-Williams and Parametric) and the wavelet transform with those obtained with wavelet packet functions to show new findings about their quality in the analysis of ECoG recordings in human intractable epilepsy: data from 21 patients have been analyzed and processed with four types of wavelet functions, including Orthogonal, Biorthogonal and Non-Orthogonal basis. These functions were compared in order to test their quality to represent spikes in the ECoG. The energy based on the wavelet coefficients to different scales was also calculated. The best results were found with the biorthogonal-6.8 wavelet on 5-7 scales, which gave 0.92 sensitivity, but with a high percentage of false positives; this representation was highly correlated with spike events on time and duration. To improve these results we have studied the wavelet packet coefficients energy. We found that reconstruction wavelet packet coefficients at 4 and 9 nodes contain significant information to characterize the spike event. These nodes' reconstruction coefficients were multiplied and this product was highly correlated with spikes events on time and duration. With this procedure we improved the sensitivity up to 0.96 with the same biorthogonal-6.8 wavelet at four levels. With this technique we do not sacrifice computation time: 896 samples are processed at only 0.16 s, so that it is possible to show the spike scattering path on line, because 896 samples (7 s)/16 channels are processed at 3.13 s.

Compensating for window effects in the calculation of spectrographic instantaneous bandwidth

Exact results derived by Cohen and Lee are used to study the distortion induced by the window in the computation of instantaneous bandwidth via the spectrogram. These concepts have been recently used in an interesting study regarding lesion-induced blood flow disturbances, where an approximation was made to compensate for the window effects. We show that this compensation is accurate for stationary signals, but becomes increasingly poorer as the signal becomes less stationary (e.g., large frequency modulations). We propose an alternative technique to reduce the window distortions, and point out the use of other time-frequency distributions that do not suffer such distortions.

Time-frequency analysis of the heart rate variability during the Valsalva manoeuvre

We present an analysis of the heart rate variability during the Valsalva manoeuvre. The more frequently used time-frequency distributions were applied in order to analyse the dynamical behaviour of several spectral indexes during the manoeuvre. The influence of the branches of the autonomous system can be predicted following the evolution of the studied indexes. The exponential time-frequency distribution showed the best results in the graphical representation, as well as in the indexes calculation. The total power, the low-to-high frequency ratio and the fractal dimension were analysed throughout the different phases of the manoeuvre and a representative model of these parameters' evolution was proposed.

Visualization of epileptogenic phenomena using cross-correlation analysis: localization of epileptic foci and propagation of epileptiform discharges

The main objectives of the preoperative evaluation of a patient with medically intractable epileptic seizures are localization of the foci and propagation of the epileptiform discharges. Electrocorticographic (ECoG) data of intractable focal epilepsy were analyzed using an AR model, wavelet analysis, and cross-correlation analysis. In order to derive the time-shifts, the cross correlations of the epileptiform discharges were calculated between electrodes for every unit of time. Further analyses were made by means of a set of contour maps of the time-shifts and sequential two- and three-dimensional visualizations of the time-shift maps in order to localize the epileptic foci and study their propagation process. Two types of foci and propagation were revealed in the results. In the first type, epileptiform discharges were generated at localized focal sites and spread quickly to other sites. In the second type, the foci of epileptiform discharges, which appeared soon after the former bursts, were localized at more than one site, and the discharges tended to spread more slowly. The findings suggest that epileptic phenomena can be caused by at least two kinds of mechanisms in one patient: in the former, the propagation might be mediated through synaptic projections, while in the latter, the extracellular diffusion of an excitatory factor might play an important role. In addition, our newly developed visualization technique for the localization of epileptic foci and the propagation of epileptiform discharges should prove useful in the study of epileptogenesis etiology.

Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering

Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were imputed to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of cluster overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. University may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering

Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event prediction. Examples can be found in tachycardia detection from ECG signals, epileptic seizure or psychotic attack prediction from an EEG signal, and prediction of vehicle drivers falling asleep from both signals. The problem generally treats a set of ordered measurements and asks for the recognition of some patterns of observed elements that will forecast an event or a transition between two different states of the biological system. It is proposed to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally treated by means of fuzzy clustering. In such cases, an adaptive selection of the number of clusters (the number of underlying semi-stationary processes) can overcome the general non-stationary nature of biomedical signals and enable the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical fuzzy partitioning. Each pattern can have a non-zero membership in more than one data subset in the hierarchy. A 'natural' and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to state recognition during recovery from exercise using the heart rate signal and to the forecasting of generalised epileptic seizures from the EEG signal.

Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe

OBJECTIVES

The ability to analyze patterns of recorded seizure activity is important in the localization and classification of seizures. Ictal evolution is typically a dynamic process with signals composed of multiple frequencies; this can limit or complicate methods of analysis. The recently-developed matching pursuit algorithm permits continuous time-frequency analyses, making it particularly appealing for application to these signals. The studies here represent the initial applications of this method to intracranial ictal recordings.

METHODS

Mesial temporal onset partial seizures were recorded from 9 patients. The data were analyzed by the matching pursuit algorithm were continuous digitized single channel recordings from the depth electrode contact nearest the region of seizure onset. Tine frequency energy distributions were plotted for each seizure and correlated with the intracranial EEG recordings.

RESULTS

Periods of seizure initiation, transitional rhythmic bursting activity, organized rhythmic bursting activity and intermittent bursting activity were identified. During periods of organized rhythmic bursting activity, all mesial temporal onset seizures analyzed had a maximum predominant frequency of 5.3-8.4 Hz with a monotonic decline in frequency over a period of less than 60 s. The matching pursuit method allowed for time-frequency decomposition of entire seizures.

CONCLUSIONS

The matching pursuit method is a valuable tool for time-frequency analyses of dynamic seizure activity. It is well suited for application to the non-stationary activity that typically characterizes seizure evolution. Time-frequency patterns of seizures originating from different brain regions can be compared using the matching pursuit method.

Elimination of interference component in Wigner-Ville distribution for the signal with 1/f spectral characteristic

A new technique for interference-term suppression in Wigner-Ville distribution (WVD) is proposed for the signal with 1/f spectrum shape. The spectral characteristic of the signal is altered by f alpha filtering before time-frequency analysis and compensated after analysis. With the utilization of the proposed technique in smoothed pseudo Wigner-Ville distribution, an excellent suppression of interference component can be achieved.