Biomedical subjects

Mercedes Cabrerizo

Publications and source records attributed to Mercedes Cabrerizo.

5 recordsLinked to original sources

PubMed2005

Integrated study of topographical functional maps based on an auditory comprehension paradigm using an eigensystem study and spectrum analysis.

This study integrates a spectral analysis of key frequency bands (Alpha, Beta, Delta, and Theta) with an eigensystem-based study in order to validate brain functional mappings associated with the characterization effects of an Auditory/Comprehension paradigm. This numerical characterization supported by topographic functional maps brings added insight in the involvement of the Wernicke and Broca's brain areas to language comprehension. A thorough examination of EEG recordings through the eigensystem reveals that eigenvectors associated with the largest eigenvalues produce an interesting activity pattern located in the frontal area of the brain directly attributable to those characteristic behaviors found in the Alpha, Beta, Delta, and Theta frequency bands. An evaluation of spectral arrays is performed using topographic maps of the induced brain activities during both listening and answering phases. This evaluation is then augmented with quantifying measures using the eigensystem study while results are validated through integration of EEG activity and eigensystem modalities. Such a representation can provide insightful information on how different patients react during an auditory and response phases, and in the ability to detect the presence of potential neurological disorders by assessing similar/dissimilar behaviors with respects to all former patients already included in the database. The algorithm as developed in this study could be extended in its application to other brain functional mapping tasks given its simple but effective practical mathematical foundation.

Acoustic Stimulation↗

PubMed2004

Interictal spike detection using the Walsh transform.

The objective of this study was to evaluate the feasibility of using the Walsh transformation to detect interictal spikes in electroencephalogram (EEG) data. Walsh operators were designed to formulate characteristics drawn from experimental observation, as provided by medical experts. The merits of the algorithm are: 1) in decorrelating the data to form an orthogonal basis and 2) simplicity of implementation. EEG recordings were obtained at a sampling frequency of 500 Hz using standard 10-20 electrode placements. Independent sets of EEG data recorded on 18 patients with focal epilepsy were used to train and test the algorithm. Twenty to thirty minutes of recordings were obtained with each subject awake, supine, and at rest. Spikes were annotated independently by two EEG experts. On evaluation, the algorithm identified 110 out of 139 spikes identified by either expert (True Positives = 79%) and missed 29 spikes (False Negatives = 21%). Evaluation of the algorithm revealed a Precision (Positive Predictive Value) of 85% and a Sensitivity of 79%. The encouraging preliminary results support its further development for prolonged EEG recordings in ambulatory subjects. With these results, the false detection (FD) rate is estimated at 7.2 FD per hour of continuous EEG recording.

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PubMed2004

A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data.

This study focuses on the design of orthogonal operators based on unique Electroencephalograph (EEG) signal decompositions in order to detect interictal spikes that characterize epileptic seizures in EEG data. The merits of the algorithm are: (a) in elaborating a unique analysis scheme that scrutinizes EEG data through orthogonal operators designed to extract features that best characterize spikes in epileptogenic EEG data; and (b) in establishing mathematical derivations that provide quantitative measures through the designed operators, and characterize and locate the event of an interictal spike. The uniqueness of this algorithm is in its good performance and simplicity of implementation. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Spikes were annotated independently by three EEG experts. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a Precision (Positive Predictive Value) of 92% and a Sensitivity of 82%. Based on the 20 to 30-minute epochs of continuous EEG recording per subject, the false detection (FD) rate is estimated at 1.8 FD per hour of recorded EEG. These are good results that support further development of this algorithm for EEG diagnosis.

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PubMed2004

An integrated auditory-comprehension process augmented through topographical maps and a new eigensystem study.

The algorithm developed in this study integrates a frequency analysis of key frequency bands (Alpha, Beta, Delta, and Theta) with the principal component analysis (PCA) in order to validate brain functional mappings associated with the characterization effects of an Auditory/Comprehension task. This study provides added insight to earlier findings involving the Wernicke and Broca's brain areas in relation to language comprehension. A thorough examination of the electroencephalograph (EEG) recordings through the PCA reveals that eigenvectors associated with the largest eigenvalues produce an interesting activity pattern directly attributable to those characteristic behaviors found in the Alpha, Beta, Delta, and Theta frequency bands. The clinical EEG data involved 9 patients at Miami Children's Hospital using the Electrical Source Imaging system with 256 electrodes. An evaluation of spectral arrays is performed using topographic maps of the induced brain activities during both listening and answering phases. This evaluation is then augmented with quantifying measures using the PCA while results are validated through integration of EEG and PCA modalities. Such a representation allows us to bring new insight out on how different patients react under different circumstances, and be able to detect consequently the presence of potential neurological disorders by assessing similar/dissimilar behaviors with respects to all former patients already included in the database. The good results obtained are foreseen to extend the algorithm's application to other brain functional mapping tasks.

Algorithms↗

PubMedDate not supplied

Detection of interictal spikes and artifactual data through orthogonal transformations.

This study introduces an integrated algorithm based on the Walsh transform to detect interictal spikes and artifactual data in epileptic patients using recorded EEG data. The algorithm proposes a unique mathematical use of Walsh-transformed EEG signals to identify those criteria that best define the morphologic characteristics of interictal spikes. EEG recordings were accomplished using the 10-20 system interfaced with the Electrical Source Imaging System with 256 channels (ESI-256) for enhanced preprocessing and on-line monitoring and visualization. The merits of the algorithm are: (1) its computational simplicity; (2) its integrated design that identifies and localizes interictal spikes while automatically removing or discarding the presence of different artifacts such as electromyography, electrocardiography, and eye blinks; and (3) its potential implication to other types of EEG analysis, given the mathematical basis of this algorithm, which can be patterned or generalized to other brain dysfunctions. The mathematics that were applied here assumed a dual role, that of transforming EEG signals into mutually independent bases and in ascertaining quantitative measures for those morphologic characteristics deemed important in the identification process of interictal spikes. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Three EEG experts annotated spikes independently. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a precision (positive predictive value) of 92% and a sensitivity of 82%. Based on the 20- to 30-minute epochs of continuous EEG recording per subject, the false detection rate is estimated at 1.8 per hour of continuous EEG. These are positive results that support further development of this algorithm for prolonged EEG recordings on ambulatory subjects and to serve as a support mechanism to the decisions made by EEG experts.

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