Deep brain stimulation relieves disabling symptoms of neurologic and psychiatric diseases when medical treatments fail, yet its therapeutic mechanism is unknown. We hypothesized that ventral intermediate nucleus stimulation for essential tremor activates cortex at short latencies and that this potential is related to suppression of tremor in the contralateral arm. We measured cortical activity with electroencephalography in 5 subjects (7 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 ipsilateral sensorimotor cortex with peaks at discrete latencies beginning less than one millisecond after stimulus onset (mean latencies 0.9±0.2, 5.6±0.7, and 13.9±1.4 milliseconds, 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 ventral intermediate nucleus thalamic stimulation for essential tremor activates cerebral cortex at approximately one millisecond after the stimulus pulse. The association between this short latency potential and tremor suppression suggests that deep brain stimulation 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.
This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham’s line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG).
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