Biomedical subjects

Miguel Valencia

Publications and source records attributed to Miguel Valencia.

8 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

High-frequency oscillations in the somatosensory evoked potentials of patients with cortical myoclonus: pathophysiologic implications.

SUMMARY: A small series of high-frequency wavelets overlapping the earliest part of the N20 wave (high-frequency oscillations, HFOs) can be observed in the somatosensory evoked potentials (SSEPs) of normal subjects after filtering then with a high-pass filter (>500 Hz). These HFOs have been related to interneuronal activity in the primary somatosensory cortex. In patients with cortical myoclonus there is a sensorimotor cortical hyperexcitability, expressed neurophysiologically as high-amplitude waves in the SSEPs (giant SSEPs). There have been contradicting reports in the literature on the changes in the HFOs in these patients. The authors studied HFOs in a group of 20 patients with cortical myoclonus of different origins and in a control group by means of time-frequency transforms, comparing the results obtained with the amplitude and latency of the classical SSEP waves. All controls had normal HFOs, with two components. Nine patients had no HFOs, nine patients had low-amplitude and/or delayed HFOs, and the remaining two patients, the only without ataxia, had high-amplitude HFOs with a long latency. These results suggest heterogeneity in the pathophysiology of cortical myoclonus, which might be related to the different systems affected.

Adolescent↗

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↗

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

Human cerebral activation during steady-state visual-evoked responses.

Flicker stimuli of variable frequency (2-90 Hz) elicit a steady-state visual-evoked response (SSVER) in the electroencephalogram (EEG) with the same frequency as the stimulus. In humans, the amplitude of this response peaks at approximately 15 Hz, decreasing at higher stimulation frequencies. It was not known whether this peak response corresponds to increased synaptic activity in the visual cortex or to other mechanisms [for instance, the temporal coherence (phase summation) of evoked responses]. We studied the SSVER in 16 normal volunteers by means of visual stimulation at 14 different frequencies (from 5 to 60 Hz) while recording the EEG. In nine subjects of the group, we measured regional cerebral blood flow (rCBF) with positron emission tomography (PET)-H2(15)O at rest and during visual stimulation at five different frequencies: 5, 10, 15, 25, and 40 Hz. We confirmed that the amplitude of the SSVER in occipital regions peaks at 15 Hz stimulation. Applying to the PET rCBF data a contrast weighted by the amplitude of the SSVER, we determined that the primary visual cortex rCBF follows an activation pattern similar to the SSVER. This finding suggests that the amplitude of the SSVER corresponds to increased synaptic activity, specifically in Brodmann's area 17. Additionally, this study showed that visual stimulation at 40 Hz causes selective activation of the macular region of the visual cortex, and that a region in the dorsal aspect of the Crus I lobule of the left cerebellar hemisphere is activated during repetitive visual stimulation.

Adult↗

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↗