Hasan Al-Nashash

A novel technique for the extraction of fetal ECG using polynomial networks

In this paper, we propose a novel technique for extracting fetal electrocardiogram (FECG) from a thoracic ECG recording and an abdominal ECG recording of a pregnant woman. The polynomial networks technique is used to nonlinearly map the thoracic ECG signal to the abdominal ECG signal. The FECG is then extracted by subtracting the mapped thoracic ECG from the abdominal ECG signal. Visual test results obtained from real ECG signals show that the proposed algorithm is capable of reliably extracting the FECG from two leads only. The visual quality of the FECG extracted by the proposed technique is found to meet or exceed that of published results using other techniques such as the independent component analysis.

Wavelet entropy for subband segmentation of EEG during injury and recovery

AbstractIn this paper, subband wavelet entropy (SWE) is used for the segmentation of electroencephalographic signals (EEG) recorded during injury and recovery following global cerebral ischemia. Wavelet analysis is used to decompose the EEG into standard clinical subbands followed by computation of the Shannon entropy. The EEG was measured from rodent brains in a controlled experimental brain injury model by hypoxic-ischemic cardiac arrest. Results show that while the relative EEG power failed to reveal the order of bursting activity associated with recovery, SWE was used to segment the EEG and delineate the initial bursting periods in each subband. Based on entropy variations obtained from a cohort of animals with graded levels of hypoxic-ischemic cardiac arrest, an intermittent pattern of bursting was observed in the high frequency bands.

Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials

Our proposed algorithm for seizure prediction is based on the principle that seizure build-up is always preceded by constantly changing bursting levels. We use a novel measure of residual subband wavelet entropy (RSWE) to directly estimate the entropy of bursts, which is otherwise obscured by the ongoing background activity. Our results are obtained using a slow infusion anesthetized pentylenetetrazol (PTZ) rat model in which we record field potentials (FPs) from frontal cortex and two thalamic areas (anterior and posterior nuclei). In each frequency band, except for the theta-delta frequency bands, we observed a significant build-up of RSWE from the preictal period to the first ictal event (p/spl les/0.05) in cortex. Significant differences were observed between cortical and thalamic RSWE (p/spl les/0.05) subsequent to seizure development. A key observation is the twofold increase in mean cortical RSWE from the preictal to interictal period. Exploiting this increase, we develop a slope change detector to discern early acceleration of entropy and predict the approaching seizure. We use multiple observations through sequential detection of slope changes to enhance the sensitivity of our prediction. Using the proposed method applied to a cohort of four rats subjected to PTZ infusion, we were able to predict the first seizure episode 28 min prior to its occurrence.

Monitoring of global cerebral ischemia using wavelet entropy rate of change

In this paper, the subband wavelet entropy (SWE) and its time difference are proposed as two quantitative measures for analyzing and segmenting the electroencephalographic (EEG) signals. SWE for EEG subbands, namely Delta, Theta, Alpha, Beta, and Gamma, is calculated and segmented using wavelet analysis. In addition, a time difference entropy measure was calculated because it does not require a baseline and equals to zero in all clinical bands as the initial condition. Visual and quantitative results were obtained from 11 rodents that were subjected to 3, 5, and 7 min of global ischemic brain injury by asphyxic cardiac arrest. We found that the time difference of SWE is capable of amplifying the variations between clinical bands during the various stages of the recovery process and may serve as a novel analytical approach to grade and classify brain rhythms during global ischemic brain injury and recovery.

EEG signal modeling using adaptive Markov process amplitude

In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.

Spinal Cord Injury Detection and Monitoring Using Spectral Coherence

In this paper, spectral coherence (SC) is used to study the somatosensory evoked potential (SEP) signals in rodent model before and after spinal cord injury (SCI). The SC technique is complemented with the Basso, Beattie, and Bresnahan (BBB) behavior analysis method to help us assess the status of the motor recovery. SC can be used to follow the effects of SCI without any preinjury baseline information. In this study, adult female Fischer rats received contusion injury at T8 level with varying impact heights using the standard New York University impactor. The results show that the average SC between forelimb and hindlimb SEP signals before injury was relatively high ( ges0.7). Following injury, the SC between the forelimb and hindlimb SEP signals dropped to various levels (les0.7) corresponding to the severity of SCI. The SC analysis gave normalized quantifiable results for the evaluation of SCI and recovery thereafter using the forelimb signals as an effective control, without the need of any baseline data. This technique solves the problems associated with the commonly used time-domain analysis like the need of a trained neurophysiologist to interpret the data and the need for baseline data. We believe that both SC and BBB may provide a comprehensive and complementary picture of the health status of the spinal cord after injury. The presented method is applicable to SCIs not affecting the forelimb SEP signals.

Surface myoelectric signal classification for prostheses control

This paper represents an ongoing investigation for surface myoelectric signal segmentation and classification. The classical moving average technique augmented with principal components analysis and time-frency analysis were used for segmentation. Multiresolution wavelet analysis was adopted as an effective feature extraction technique while artificial neural networks were used for classification. Results of classifying four elbow and wrist movement signals recorded from biceps and triceps gave 5.1% classification error when two channels were used.

Cognitive workload estimation due to vague visual stimuli using saccadic eye movements.

Visual perception is affected by the quality of stimulus. In this paper, we investigate the rise in cognitive workload of an individual performing visual task due to vague visual stimuli. We make use of normalized average peak saccadic velocity to estimate the cognitive workload. Results obtained from 16 human subjects show that the mean of peak saccadic velocity increases with workload indicating that faster saccades are required to obtain information as the workload increases. This technique should find application in assessment of vigilance and cognitive performance in many demanding professional, industrial and transportation situation.

Cognitive workload modulation through degraded visual stimuli: a single-trial EEG study.

OBJECTIVE Our experiments explored the effect of visual stimuli degradation on cognitive workload. APPROACH We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. MAIN RESULTS These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. SIGNIFICANCE Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.

Assessment of retinopathy severity using digital fundus images

This paper proposes a framework for detecting the presence and assessing the severity of exudate formations in patients suffering from diabetic retinopathy. The proposed framework also localizes and detects the fovea, the knowledge of which aids in determining the severity of the impairment to visual function posed by the exu-dates. Results are presented using digital fundus images collected from both normal and diabetic retinopathy patients.