Biomedical subjects

Hasan Al-Nashash

Publications and source records attributed to Hasan Al-Nashash.

3 recordsLinked to original sources

PubMed2005

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.

Algorithms↗

PubMed2004

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.

Algorithms↗

PubMed2003

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 < or = 0.05) in cortex. Significant differences were observed between cortical and thalamic RSWE (p < or = 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.

Algorithms↗