Biomedical subjects

Malek Adjouadi

Publications and source records attributed to Malek Adjouadi.

13 recordsLinked to original sources

PubMed2006

An interactive interface for seizure focus localization using SPECT image analysis.

Accurate epileptic focus localization using single photon emission computed tomography (SPECT) images has proven to be a challenging endeavor. First, commonly used radiopharmaceuticals such as hexamethylpropylene amine oxime (HMPAO) quantitatively underestimate large blood flows, leading to subtracted SPECT images that do not reflect the true cerebral physiological conditions, and often display non-distinct epileptic foci. The proposed relative change subtraction method of SPECT image analysis helps alleviate this quantitative burden. Second, the image analysis process traditionally performed by physicians is time consuming and prone to error. Toward this end, an automated algorithm was designed to analyze SPECT images and provide feedback to users through a visual interface.

Algorithms↗

PubMed2006

Pattern extraction in interictal EEG recordings towards detection of electrodes leading to seizures.

This study introduces an algorithm for a new application dedicated at discriminating between electrodes leading to a seizure onset and those that do not lead to seizure using interictal subdural EEG data. The significance of this study is in determining among all of these channels, all containing interictal spikes that are asynchronously, independent of region and time, which are selected randomly (these EEG portions may or may not contain spikes), and yet through the developed algorithm, we are able to classify those channels that lead to seizure and those that do not. The main zones of ictal activity are supposed to evolve from the tissue located at the channels that present interictal activity, but sometimes this is no the case. The purpose is to gain a better understanding of the dynamics of the human brain through a study of subdural EEG, with an emphasis on attempting to characterize the common behaviors of interictal EEG channels prior to an ictal activity. The study will try to correlate the clinical features with the EEG findings and to determine whether the patient has a consistent source of ictal activity, which is coming from the location of the group of channels that present interictal activity. If a method was found to detect the electrodes that present interictal activity, with the most potential to lead to an pileptic seizure, then the epilepsy focus could be located with a higher degree of certainty. This analysis allows for the detection of neurological disorders due to epileptic activity in the brain, and rings out how different patients react prior to a seizure.

Algorithms↗

PubMed2006

Enhanced real-time cursor control algorithm, based on the spectral analysis of electromyograms.

This paper presents a new version of an EMG-based, hands-free, cursor control system, and compares its performance to that of a previous version. Both systems use classification algorithms that rely on the periodogram estimation of the power spectral density (PSD) of electromyogram (EMG) signals from muscles in the face. The older system requires three electrodes for EMG input, and utilizes an algorithm that calculates partial power accumulations over the frequency ranges of 0Hz - 145Hz and 145Hz - 600Hz in the PSDs of the EMG signals. The new system requires four electrodes for EMG input, and utilizes an algorithm that calculates mean power frequency (MPF) values to assist in distinguishing the cranial muscle that contracted. An experiment was devised to gauge the point-and-click capabilities of both systems. The experimental results were evaluated using Fitts' Law analysis. The results show that the new algorithm provides improved point-and-click performance over the old algorithm.

Algorithms↗

PubMed2006

Optimizing the classification of acute lymphoblastic leukemia and acute myeloid leukemia samples using artificial neural networks.

Accurate classification of human blood cells plays a decisive role in the diagnosis and treatment of diseases. Artificial Neural Networks (ANN) have been consistently used as a trusted classification tool for this type of analysis. In this study, a new Artificial Neural Network approach is proposed for the multidimensional classification of two of the most common forms of leukemia: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML), also sometimes called Acute Myelogenous Leukemia. Beckman-Coulter Corporation supplied flow cytometry data of 120 patients that were used in the training and testing phases. The ANN algorithm was thus developed to exploit the different features of the different blood cells provided in an optimized fashion. The goal was to establish a programming tool, supported through this new ANN development, for the identification of normal and abnormal blood samples and provide information to medical doctors in the form of diagnostic references for the specific disease state that is considered for this study. The application of the ANN algorithm produced remarkable classification accuracy results that show a 95% classification accuracy for the normal blood samples and 90% classification accuracy for the abnormal samples even under the ubiquitous problem of overlap.

Blood Cell Count↗

PubMed2006

3-D brain segmentation towards the integration of DTI and MRI modalities.

This study introduces a 3-D segmentation method together with a graphical user interface (GUI) as means to effectively automate the process of segmentation with the ultimate objective of integrating and visualizing diffusion tensor imaging (DTI) with magnetic resonance imaging (MRI) in a fully automated 3-D brain imaging system. A secondary objective is to reduce significantly the segmentation time required to extract key landmarks of the brain in contrast to the manual process currently used at many hospital settings. The results provided will prove this important assertion. The inter-correlation coefficient revealed 96.1% accuracy in segmenting all of the processed data, which consequently led to effective registration of the DTI and MRI modalities since they involve the same landmarks. The average speed of segmentation was just 35 seconds, a reduction of over 20 times of what is required for manual segmentation. In order to create a highly integrated interface, the segmentation results serve as input to a registration algorithm we are currently investigating and whose preliminary results support the significance of relying on an effective segmentation process. T1-weighted 3D Gradient Echo MR and DT images from 16 patients at Miami Children's Hospital were used for evaluation purposes.

Algorithms↗

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↗

PubMed2005

Software-based compensation of visual refractive errors of computer users.

For human beings, vision is one of the most important senses in interacting with the surrounding environment, as well as with any tools that require visual communication. As such, the ability to interact effectively with computers through typical graphic user interfaces (GUIs) is greatly affected by any refractive errors present in an individual's visual system. If the refractive errors can be mathematically modeled, a system for overcoming these aberrations can be devised which can increase the effective human-computer interaction for these individuals. Several methods, such as Adaptive Optics, have been proposed that attempt to solve this problem using electro-mechanical devices. These methods are costly and impractical, preventing most visually impaired individuals from benefiting from their use. In contrast, an image-processing method, based on deconvolution techniques, has recently been proposed for the pre-compensation of images to be displayed in a computer. This method is much more practical, being completely implemented in software, and has achieved encouraging results. Previous results have yielded an average 50% increase in visual efficiency in the compensation of a known artificial aberration introduced into the field of vision of experimental subjects. This paper describes the difficulties encountered with the present software-only compensation and proposes several methods for overcoming these obstacles. The difficulties, as well as the proposed solutions, are described theoretically and followed by examples using a lens system showing the improvement over previous methods.

Algorithms↗

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.

Action Potentials↗

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.

Action Potentials↗

PubMed2004

An optimization approach to recognition of epileptogenic data using neural networks with simplified input layers.

This study introduces a simplified approach for the implementation of artificial neural networks (ANN) for the recognition of epileptic data in electroencephalograph (EEG) recordings. The training set construction is based on a trend-adaptive polygon which simplifies the search process as it reduces the size of the training set. This data reduction, at a sampling rate of 200 Hz, yielded a reduction ratio of 34% as a minimum to an 81% in the best case scenario. With a higher sampling rate of 500 Hz, a reduction ratio of 73% as a minimum to an impressive 92% in the best case scenario was achieved. The outcome is thus a computationally attractive classifier with a simpler design implementation and with higher prospects for accurate diagnosis. The algorithm was trained and tested with EEG data from four epileptic patients using the k-fold cross-validation technique.

Action Potentials↗

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↗

PubMed2003

3-d source localization of epileptic foci integrating EEG and MRI data.

This study evaluates the utility of 3-D localization of interictal spike activity on the electroencephalographs (EEG) superimposed on magnetic resonance imagery (MRI) in a pediatric population with extra-temporal lesional epileptic foci. 3-D software programming based on the CURRY platform (a multimodal neuro-imaging software) was adapted for analyzing scalp EEG data and reconstructing superimposed images in 10 children who underwent extensive pre-surgical evaluation for intractable partial seizures. The results of 3-D spike source localization were assessed in relationship to focal lesions evident on the patient's MRI scans. Calculated spike sources were closest to the lesions during intervals corresponding to the spike peaks. The information was useful in surgical planning in six children that underwent successful resections.

Action Potentials↗

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.

Action Potentials↗