Biomedical subjects

Elena Urrestarazu

Publications and source records attributed to Elena Urrestarazu.

9 recordsLinked to original sources

PubMed2006

Independent component analysis separates spikes of different origin in the EEG.

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Adult↗

PubMed2006

Independent component analysis in the study of focal seizures.

Independent component analysis (ICA) is a novel technique that can separate statistically independent elements from complex signals. It has demonstrated its utility in separating artifacts and analyzing interictal discharges in EEG. ICA has been used recently in ictal recordings, showing the possibility of isolating the ictal activity. The goal of our study was to analyze focal seizures with ICA, decomposing the elements of the seizures to understand their genesis and propagation, and to differentiate between various types of focal seizures. We studied 26 focal seizures of temporal, frontal, or parietal origin. Only seizures with suspected focal onset were included in the study. The EEG recordings were acquired by using standard video-EEG equipment, with scalp electrodes. All the off-line analysis was carried out on a PC by means of specific software developed in the Matlab environment. ICA components were calculated with the use of the JADE (Joint Approximate Diagonalization of Eigen-matrices) algorithm. The decomposition of the seizures varied according to the EEG seizure pattern. In the seizures with focal rhythmic theta slow or sharp waves, the rhythmic activity was separated into one to five components, having an initial component with a clear concordance with the focus, whereas the others had an onset a few milliseconds later and corresponded to neighboring areas. In the 6 frontal seizures with regional rhythmic low voltage fast activity, 4 to 10 components were found, practically with a simultaneous timing, having a frontal distribution. In the three frontal seizures with a diffuse attenuation of the EEG signal, it was not possible to differentiate components of cerebral origin from the components of muscle artifact. ICA is an interesting tool to study the nature of focal seizures. The results depend on the EEG pattern. In the seizures with a clear EEG focal pattern, ICA may be useful to separate components of the ictal onset from the propagated activity.

Brain Mapping↗

PubMed2006

High-frequency intracerebral EEG activity (100-500 Hz) following interictal spikes.

PURPOSE: High-frequency activity has been recorded with intracerebral microelectrodes in epileptic patients and related to seizure genesis. Our goal was to analyze high-frequency activity recorded with electroencephalograph (EEG) macroelectrodes during the slow wave immediately following interictal spikes, given the potential importance of this presumed hyperpolarization in transforming spikes into seizures. METHODS: Depth electrode EEG recordings from 10 patients with intractable focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Spikes were categorized according to localization and morphology. Segments of 256 ms were selected immediately following (postspike), and 2 s before each spike (baseline). Power was estimated in subgamma (0-40 Hz), gamma (40-100 Hz), high frequency (100-200 Hz), and very high frequency (250-500 Hz) bands. RESULTS: Changes in power above 100 Hz were seen in 22 of 29 spike categories, consisting primarily of a widespread decrease in frequencies above 100 Hz. This decrease became spatially more restricted as frequencies increased, and coincided with the localization of largest spikes for the highest frequencies. High-frequency power decreases were prominent in the hippocampus but less common in amygdala and neocortex. High-frequency power increases were observed in the amygdala. CONCLUSIONS: Thus high-frequency EEG activity can be recorded with macroelectrodes in humans and may provide insights on neuronal mechanisms related to human epilepsy. This activity undergoes consistent modifications after EEG spikes. We propose that the reduction in high frequencies reflects a postspike depression in neuronal activity that is more pronounced in the region of spike generation. This depression is almost always seen in hippocampus but less in amygdala.

Adult↗

PubMed2006

Sleep-related laryngospasm: a video-polysomnographic recording.

We present a case of laryngospasm in a 12-year-old male who experienced sudden, nocturnal episodes of breathing difficulties and agitation. Apart from laryngospasm, the main differential diagnoses included frontal seizures, sleep-related choking syndrome, sleep asthma, sleep apnoea and REM sleep behaviour disorder. The video and the EEG recordings supported the diagnosis of laryngospasm. The pH-metry confirmed the existence of reflux. Its treatment successfully controlled the episodes. This case illustrates, with a typical video recording, this infrequent type of paroxysmal event with an important differential diagnosis.[Published with video sequences].

Child↗

PubMed2005

Independent component analysis in the distinction between focal and multifocal spikes.

Independent component analysis (ICA) is a novel method that extracts independent sources in recorded signals. One of its capabilities is to separate epileptiform activity of different origins. The goal of this study was to demonstrate that ICA is useful for differentiating focal and multifocal epilepsies. Using ICA, the authors analyzed 160 samples of patients with unifocal (temporal: 50 samples from 5 patients; frontal: 50 samples from 5 patients) or multifocal epilepsy (bitemporal: 30 samples from 3 patients; multifocal extratemporal: 30 samples from 5 patients). Each sample included at least two spikes. ICA was applied using the JADE algorithm implemented in a Matlab platform. The components were identified visually. The EEG and the isopotential map of the suspected components were reconstructed to demonstrate the nature and location of each spike. In multifocal epilepsies, the spikes were separated in distinct components in all cases. In unifocal temporal epilepsies, ICA extracted all the spikes from the same location into a single component. In the patients with unifocal frontal epilepsy, one component included all the spikes in 80% of the samples; in some of these cases, other components were responsible for small parts of the spikes, but with a close topography to the main component. The waves were separated in different components both in the unifocal and in the multifocal samples. Therefore, in epilepsies with independent foci, ICA separates the spikes of different origins into distinct components. In unifocal epilepsies, ICA tends to locate the spikes from the same area into the same component, especially in temporal epilepsies. The authors conclude that ICA might be a useful tool to distinguish between unifocal and multifocal epilepsies.

Electroencephalography↗

PubMed2004

Oscillatory cortical changes during periodic limb movements.

STUDY OBJECTIVES: Periodic limb movements are a distinctive and frequent finding on polysomnography. The pathophysiology and clinical significance of these common sleep-related movements are not yet well understood. The brainstem, spinal cord, and peripheral nervous system may be implicated in the pathogenesis of periodic limb movements. The goal of our study was to probe if there is any participation of cortical mechanisms in the periodic limb movements. We focused the study on the analysis of electroencephalogram oscillations around the periodic limb movement. PARTICIPANTS: We selected polysomnographic recordings of 6 patients diagnosed with periodic limb movements of sleep. METHODS/DESIGN: We analyzed the cortical activity offline after averaging 10-second segments, having the middle point with the beginning of each limb movement in non-rapid eye movement sleep, selecting exclusively samples without obvious arousals. In each patient, polysomnographic segments containing limb movements were selected from both slow-wave (stage 3-4) and lighter (stage 1-2) non-rapid eye movement sleep. RESULTS: An increase in all the frequencies immediately after the limb movements, having the maximum 2.5 to 3 seconds after the beginning of the movement, was found. The slope started 1.5 to 2 seconds before the limb movements. The topography of this activity was different from the activity accompanying voluntary movements in the awake state. The meaning of this activity is uncertain, but one possibility is its relationship with microarousals or an increase of the sensory inputs. The beginning of the increase before the limb movements suggests that the cortex may be engaged during the neural changes associated with this kind of movement, though not playing a causal role.

Adult↗

PubMed2004

Independent component analysis removing artifacts in ictal recordings.

PURPOSE: Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset. METHODS: We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample. RESULTS: All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant. CONCLUSIONS: ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.

Adult↗

PubMed2003

Controversies in the diagnosis and management of psychogenic pseudoseizures.

Since the advent of video-EEG telemetry studies (V-EEG) neurologists have become increasingly aware of psychogenic pseudoseizures (PPS) given the relatively high prevalence of these events among patients seen in epilepsy centers. The use of V-EEG has been accepted as the gold standard study in establishing this diagnosis; some clinicians, however, have suggested that the clinical phenomena of PPS are so obvious in many patients that V-EEG may not be necessary. This is one of many controversial points that clinicians face when evaluating patients suspected of having PPS. In this article, we review some of these controversies, specifically the need for a video-EEG monitoring study in all patients suspected of having PPS and the role of induction protocols in the evaluation of PPS, the question of whether patients have any control over their events, and finally some of the therapeutic strategies for PPS including the need to limit these patients' driving privileges.

Anticonvulsants↗

PubMedDate not supplied

Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study.

Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of recordings with spikes and evident artifacts of electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode artifact were studied. ICA components were calculated using the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed excluding those components related to the artifacts. A normalized correlation coefficient was used as a measure of the changes caused by the suppression of these components. ICA produced an evident clearing-up of signals in all the samples. The morphology and the topography of the spike were very similar before and after the removal of the artifacts. The correlation coefficient showed that the rest of the signal did not change significantly. Two examiners independently looked at the samples to identify the changes in the morphology and location of the discharge and the artifacts. In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters. The distortion of the interictal activity measured by correlation analysis was minimal.

Algorithms↗