Malek Adjouadi

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.

A similarity measure for stereo feature matching

An approach to stereo feature matching is presented with the introduction of a similarity measure for evaluating and confirming a stereo match. The contributions of this study are reflected in (1) the development of a similarity measure which evaluates a stereo match based on feature locality and gray-level gradient associated with the feature; and (2) the use of a matching procedure that integrates local and global matching strategies based on matching first those features with the highest similarity measure among the set of all highest similarities found locally under confined search spaces, ensuring that each feature is matched with a high degree of certainty. A left-to-right and right-to-left consistency check is used for each feature to comply with the uniqueness constraint and to confirm if a potential match can be declared a correct match.

A NEW PARAMETRIC FEATURE DESCRIPTOR FOR THE CLASSIFICATION OF EPILEPTIC AND CONTROL EEG RECORDS IN PEDIATRIC POPULATION

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

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.

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.

Remote eye gaze tracking system as a computer interface for persons with severe motor disability

State of the Art human computer interfaces (HCI) for assisting individuals with severe motor disabilities employ remote eye-gaze tracking (EGT) systems which obtain eye coordinates and convert them into mouse-pointer coordinates. The performance of those systems is traditionally affected by mouse-pointer jitter and miscalibration due to head movement. This study addresses this problem and proposes an interface to minimize those errors. The interface allows inspecting and quantifying those errors and collecting the necessary information which is used in real time for training an artificial neural network which improves the coordinate conversion mechanism. The novelty of this study resides in the integration of several procedures, such as: (a) error inspection at system startup, (b) real time improvement of the eye-to-mouse-pointer coordinate conversion mechanism, (c) determination of a practical solution to the mouse click operations, and (d) development of effective means to monitor and evaluate the system performance.

Seizure Detection: An Assessment of Time- and Frequency-based Features in a Unified Two-dimensional Decisional Space Using Nonlinear Decision Functions

Objective: This study proposes a new approach for offline seizure detection in intracranial (subdural) electroencephalogram recordings using nonlinear decision functions. It implements well-established features that are designed to deal with complex signals, such as brain recordings, and proposes a two-dimensional (2D) domain of analysis that overcomes the dilemma faced with the selection of empirical thresholds often used to delineate epileptic events. This unifying approach makes it possible for researchers in epilepsy to establish other performance evaluation criteria on the basis of the proposed nonlinear decision functions as well as introduce additional dimensions toward multidimensional analysis because the mathematics of these decision functions allows for any number of dimensions and any degree of complexity. Furthermore, because the features considered assume both time and frequency domains, the analysis is performed both temporally and as a function of different frequency ranges to ascertain those measures that are most suitable for seizure detection. In retrospect, by using nonlinear decision functions and by establishing a unified 2D domain of analysis, this study establishes a generalized approach to seizure detection that works across several features and across patients. Methods: Clinical experiments involved 14 patients with intractable seizures that were evaluated for potential surgical interventions. Of the total 157 files considered, 35 (21 interictal and 14 ictal) intracranial electroencephalogram data files or 22% were used initially in a training phase to ascertain the reliability of the formulated features that were implemented in the seizure detection process. The remaining 122 intracranial electroencephalogram data files or 78% were then used in the testing phase to assess the merits of each feature considered as means to detect a seizure. Results: The testing phase using the remaining 122 intracranial electroencephalogram data files revealed that the gamma power in the frequency domain is the feature that performed best across all patients with a sensitivity of 96.296%, an accuracy of 96.721%, and a specificity of 96.842%. The second best feature in the time domain was the mobility with a sensitivity of 81.481% an accuracy of 90.169%, and a specificity of 92.632%. In the frequency domain, all of the five other spectral bands lesser than 36 Hz revealed mixed results in terms of low sensitivity in some frequency bands and low accuracy in other frequency bands, which is expected given that the dominant frequencies during an ictal state are those higher than 30 Hz. In the time domain, other features, including complexity and correlation sum, revealed mixed success. Conclusions: All the features that are based on the time domain performed well, with mobility being the optimal feature for seizure detection. In the frequency domain, the gamma power outperformed the other frequency bands. Within this 2D plane, the best results were also observed when the degree of complexity is 3 or 4 in the implementation of the proposed nonlinear decision functions. Significance: A singular contribution of this study is in creating a common 2D space for analysis through the use of nonlinear decision functions for delineating data clusters of ictal files from data clusters of interictal files. This is critically important in establishing unifying measures that work across different features as expressed by the weight vector of the decision functions for a standardized assessment. The mathematical foundation is consequently established in support of a generalized seizure detection algorithm that works across patients, and in which all type of features that have been amply tested in the literature could be assessed within the realm of nonlinear decision functions.

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.