Biomedical subjects

Melvin Ayala

Publications and source records attributed to Melvin Ayala.

9 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

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↗

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.

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↗

PubMed2003

Bone morphogenetic protein-1/Tolloid-like proteinases process dentin matrix protein-1.

Bone morphogenetic protein-1 (BMP-1)/Tolloid-like metalloproteinases play key roles in formation of mammalian extracellular matrix (ECM), through the biosynthetic conversion of precursor proteins into their mature functional forms. These proteinases probably play a further role in formation of bone through activation of transforming growth factor beta-like BMPs. Dentin matrix protein-1 (DMP1), deposited into the ECM during assembly and involved in initiating mineralization of bones and teeth, is thought to undergo proteolysis in vivo to generate functional cleavage fragments found in extracts of mineralized tissues. Here, we have generated recombinant DMP1 and demonstrate that it is cleaved, to varying extents, by all four mammalian BMP-1/Tolloid-like proteinases, to generate fragments similar in size to those previously isolated from bone. Consistent with possible roles for the BMP-1/Tolloid-like proteinases in the physiological processing of DMP1, NH2-terminal sequences of products generated by BMP-1 cleavage of DMP1 match those predicted from processing at the predicted DMP1 site that shows greatest cross-species conservation of sequences. Moreover, fibroblasts derived from mouse embryos homozygous null for genes encoding three of the four mammalian BMP-1/Tolloid-like proteinases appear to be deficient in processing of DMP1. Thus, a further role for BMP-1-Tolloid-like proteinases in formation of mineralized tissues is indicated, via proteolytic processing of DMP1.

Amino Acid Sequence↗

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↗