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Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) relieves disabling symptoms of neurologic and psychiatric diseases when medical treatments fail, yet its therapeutic mechanism is unknown. We hypothesized that ventral intermediate (VIM) nucleus stimulation for essential tremor activates the cortex at short latencies, and that this potential is related to the suppression of tremor in the contralateral arm. We measured cortical activity with electroencephalography in 5 subjects (seven brain hemispheres) across a range of stimulator settings, and reversal of the anode and cathode electrode contacts minimized the stimulus artifact, allowing visualization of brain activity. Regression quantified the relationship between stimulation parameters and both the peak of the short latency potential and tremor suppression. Stimulation generated a polyphasic event-related potential in the ipsilateral sensorimotor cortex, with peaks at discrete latencies beginning less than 1 ms after stimulus onset (mean latencies 0.9 ± 0.2, 5.6 ± 0.7, and 13.9 ± 1.4 ms, denoted R1, R2, and R3, respectively). R1 showed more fixed timing than the subsequent peaks in the response (P < 0.0001, Levene's test), and R1 amplitude and frequency were both closely associated with tremor suppression (P < 0.0001, respectively). These findings demonstrate that effective VIM thalamic stimulation for essential tremor activates the cerebral cortex at approximately 1 ms after the stimulus pulse. The association between this short latency potential and tremor suppression suggests that DBS may improve tremor by synchronizing the precise timing of discharges in nearby axons and, by extension, the distributed motor network to the stimulation frequency or one of its subharmonics.

A fundamental technical hurdle in systems neurophysiology has been to record the activity of individual neurons in situ while using microstimulation to activate inputs or outputs. Stimulation artifact at the recording electrode has largely limited the usefulness of combined stimulating and recording to using single stimulation pulses (e.g., orthodromic and antidromic activation) or to presenting brief trains of pulses to look for transient responses (e.g., paired-pulse stimulation). Using an adaptive filter, we have developed an on-line method that allows continuous extracellular isolation of individual neuron spikes during sustained experimental microstimulation. We show that the technique accurately and robustly recovers neural spikes from stimulation-corrupted records. Moreover, we demonstrate that the method should generalize to any recording situation where a stereotyped, triggered transient might obscure a neural event. Index Terms--Action potential, adaptive filter, microstimulation, spike detection, stimulation artifact.

The aim of the present study was to determine whether the treatment of obstructed rat bladders with αlipoic acid (ALA) and silymarin reverses the biochemical and physiological responses to bladder outlet obstruction (BOO). A total of 32 adult Sprague Dawley rats were divided into four groups (n=8 per group): sham (placebo surgery) animals with no treatment (group 1); control animals with surgically induced BOO (group 2); obstructed rats treated with ALA (group 3); and obstructed rats treated with silymarin (group 4). Histological evaluation, bladder weights, collagen structure, TdT-mediated biotin nick end-labeling (TUNEL), inducible nitric oxide sentase (iNOS) mRNA levels, malondialdehyde (MDA) levels and tumor necrosis factor (TNF) levels were investigated. The ALA-treated group had similar bladder weights, collagen levels and TUNEL positivity and decreased iNOS levels compared with the control group, while the silymarin group exhibited further differences. Serum MDA and TNF-α levels were both decreased in the ALA and silymarin groups. ALA treatment reduced the increased oxidative stress and bladder inflammation caused by BOO and may contribute to the protection of bladder function.