Armando Barreto

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.

Integrated electromyogram and eye-gaze tracking cursor control system for computer users with motor disabilities

This research pursued the conceptualization, implementation, and testing of a system that allows for computer cursor control without requiring hand movement. The target user group for this system are individuals who are unable to use their hands because of spinal dysfunction or other afflictions. The system inputs consisted of electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an eye-gaze tracking (EGT) system. Each input was processed by an algorithm that produced its own cursor update information. These algorithm outputs were fused to produce an effective and efficient cursor control. Experiments were conducted to compare the performance of EMG/EGT, EGT-only, and mouse cursor controls. The experiments revealed that, although EMG/EGT control was slower than EGT-only and mouse control, it effectively controlled the cursor without a spatial accuracy limitation and also facilitated a reliable click operation.

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.

Adaptive cancelation of motion artifact in photoplethysmyographic blood volume pulse measurements for exercise evaluation

An adaptive transversal filter was designed to minimize the impact of motion artifact in the measurement of photoplethysmographic Blood Volume pulse (BVP) in an exercising subject. The rationale of the design is introduced and results from off-line testing with signals recorded from an experimental setup are presented.

Adaptive pre-processing of photoplethysmographic blood volume pulse measurements

Two adaptive transversal filters were designed as an alternative way to filter both the impact of motion artifact in the measurement of photoplethysmographic blood volume pulse (BVP) in an exercising subject and the respiratory trend appearing at the low frequency end of the BVP spectrum. The rationale for the design is presented and results obtained on signals recorded from an experimental setup are shown.

Sub-patterns of language network reorganization in pediatric localization related epilepsy: A multisite study

To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub‐groups using a distance method in the principal component analysis (PCA)‐based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Brocas) and along left superior temporal gyrus (Wernickes) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Brocas area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data‐driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub‐pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task. Hum Brain Mapp, 2011.

Low vision: the role of visual acuity in the efficiency of cursor movement

Graphical user interfaces are one of the more prevalent interface types which exist today. The popularity of this interface type has caused problems for users with poor vision. Because usage strategies of low vision users differ from blind users, existing research focusing on blind users is not sufficient in describing the techniques employed by low vision users. The research presented here characterizes the interaction strategies of a particular set of low vision users, those with Age-related Macular Degeneration, using an analysis of cursor movement. The low vision users have been grouped according to the severity of their vision loss and then compared to fully sighted individuals, with respect to cursor movement efficiency. Results revealed that as the size of the icons on the computer screen increased, so did the performance of the fully sighted participants as well as the participants with AMD.

Multilinear principal component analysis for face recognition with fewer features

In this study, a method is proposed based on multilinear principal component analysis (MPCA) for face recognition. This method utilized less features than traditional MPCA algorithm without downgrading the performance in recognition accuracy. The experiment results show that the proposed method is more suitable for large dataset, obtaining better computational efficiency. Moreover, when support vector machine is employed as the classification method, the superiority of the proposed algorithm reflects significantly.

Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors

In this paper a sensor fusion algorithm is developed and implemented for detecting orientation in three dimensions. Tri-axis MEMS inertial sensors and tri-axis magnetometer outputs are used as input to the fusion system. A Kalman filter is designed to compensate the inertial sensors errors by combining accelerometer and gyroscope data. A tilt compensation unit is designed to calculate the heading of the system.

A 3-D Liver Segmentation Method with Parallel Computing for Selective Internal Radiation Therapy

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.