Mercedes Cabrerizo

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.

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 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.

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.

Adaptive eye-gaze tracking using neural-network-based user profiles to assist people with motor disability.

This study developed an adaptive real-time human-computer interface (HCI) that serves as an assistive technology tool for people with severe motor disability. The proposed HCI design uses eye gaze as the primary computer input device. Controlling the mouse cursor with raw eye coordinates results in sporadic motion of the pointer because of the saccadic nature of the eye. Even though eye movements are subtle and completely imperceptible under normal circumstances, they considerably affect the accuracy of an eye-gaze-based HCI. The proposed HCI system is novel because it adapts to each specific users different and potentially changing jitter characteristics through the configuration and training of an artificial neural network (ANN) that is structured to minimize the mouse jitter. This task is based on feeding the ANN a users initially recorded eye-gaze behavior through a short training session. The ANN finds the relationship between the gaze coordinates and the mouse cursor position based on the multilayer perceptron model. An embedded graphical interface is used during the training session to generate user profiles that make up these unique ANN configurations. The results with 12 subjects in test 1, which involved following a moving target, showed an average jitter reduction of 35%; the results with 9 subjects in test 2, which involved following the contour of a square object, showed an average jitter reduction of 53%. For both results, the outcomes led to trajectories that were significantly smoother and apt at reaching fixed or moving targets with relative ease and within a 5% error margin or deviation from desired trajectories. The positive effects of such jitter reduction are presented graphically for visual appreciation.

An optimal decisional space for the classification of alzheimer's disease and mild cognitive impairment

This paper proposes to combine MRI data with a neuropsychological test, mini-mental state examination (MMSE), as input to a multi-dimensional space for the classification of Alzheimers disease (AD) and its prodromal stages-mild cognitive impairment (MCI) including amnestic MCI (aMCI) and nonamnestic MCI (naMCI). The decisional space is constructed using those features deemed statistically significant through an elaborate feature selection and ranking mechanism. FreeSurfer was used to calculate 55 volumetric variables, which were then adjusted for intracranial volume, age and education. The classification results obtained using support vector machines are based on twofold cross validation of 50 independent and randomized runs. The study included 59 AD, 67 aMCI, 56 naMCI, and 127 cognitively normal (CN) subjects. The study shows that MMSE scores contain the most discriminative power of AD, aMCI, and naMCI. For AD versus CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores are found to improve all classifications with accuracy increments of 8.2% and 12% for aMCI versus CN and naMCI versus CN, respectively. Results also show that brain atrophy is almost evenly seen on both sides of the brain for AD subjects, which is different from right-side dominance for aMCI and left-side dominance for naMCI. Furthermore, hippocampal atrophy is seen to be the most significant for aMCI, while Accumbens area and ventricle are most significant for naMCI.

Scalp EEG brain functional connectivity networks in pediatric epilepsy

This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The raters opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant (p<0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical raters opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis.

Classification of Leukemia Blood Samples Using Neural Networks

Pattern recognition applied to blood samples for diagnosing leukemia remains an extremely difficult task which frequently leads to misclassification errors due in large part to the inherent problem of data overlap. A novel artificial neural network (ANN) algorithm is proposed for optimizing the classification of multidimensional data, focusing on acute leukemia samples. The programming tool established around the ANN architecture focuses on the classification of normal vs. abnormal blood samples, namely acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). There were 220 blood samples considered with 60 abnormal samples and 160 normal samples. The algorithm produced very high sensitivity results that improved up to 96.67% in ALL classification with increased data set size. With this type of accuracy, this programming tool provides information to medical doctors in the form of diagnostic references for the specific disease states that are considered for this study. The results obtained prove that a neural network classifier can perform remarkably well for this type of flow-cytometry data. Even more significant is the fact that experimental evaluations in the testing phase reveal that as the ALL data considered is gradually increased from small to large data sets, the more accurate are the classification results.