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In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models. A Particle Swarm Optimization algorithm is used to optimize the model parameters. Features describing the latencies and amplitudes of SEPs are automatically derived. A rat model is then used to evaluate the automatic parameterization of SEPs in two experimental cases, i.e., anesthesia level and spinal cord injury (SCI). Experimental results show that chirp-based model parameters and the derived SEP features are significant in describing both anesthesia level and SCI changes. The proposed automatic optimization based approach for extracting chirp parameters offers potential for detailed SEP analysis in future studies. The method implementation in Matlab technical computing language is provided online.

Ultrasound provides a promising non-invasive, safe, objective means of monitoring and quantifying bone healing. In this paper, the relationship between the ultrasound image intensity and the acoustic impedance was exploited to develop a quantitative measure towards assessment and monitoring of the bone healing process. Information theoretic criterion (KLD) was used to quantify the degree of bone healing using the intensity histogram of the callus region obtained from B-Mode ultrasound. Results from a pilot experimental study, show that the proposed method is capable of accurately quantifying the degree of bone healing.

Graded spinal cord injury was induced in rats using the NY-Impactor. Somatosensory evoked potentials (SEPs), in response to stimuli to the nerves in the limbs, were recorded from the cranium. These signals were analyzed using spectral coherence analysis. Results obtained from 15 rats show information that was missed when other techniques are used.

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.

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.

In 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.

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.