Biology subjects

Chamanzar, A.

Publications and source records attributed to Chamanzar, A..

3 recordsLinked to original sources

bioRxiv2022

Noninvasive, automated and reliable detection of spreading depolarizations in severe traumatic brain injury using scalp EEG

BackgroundNoninvasive detection of spreading depolarizations (SD), as a potentially treatable mechanism of worsening brain injuries after traumatic brain injuries (TBI), has remained elusive. Current methods to detect SDs are based on intracranial recording, an invasive method with limited spatial coverage. Less invasive methods to diagnose SD are needed to improve generalizability and application of this emerging science and to guide worsening brain injury treatments. Here, we demonstrate, for the first time, a signal processing paradigm that can enable automated detection of SDs using noninvasive electroencephalography (EEG). MethodsBuilding on our previously developed WAVEFRONT algorithm, we have designed a novel automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions, as well as near-DC shifts using low-density EEG. This modified WAVEFRONT is robust to the amplitude outliers and non-propagating depressions on the scalp. We show the feasibility of detecting SD events (700 total SDs) in continuous, low-density scalp EEG recording (95{+/-}42.2 hours with 19 electrodes) acquired from 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC) and intracranial EEG that could be used as a ground truth for event detection. We quantify the performance of WAVEFRONT in terms of SD detection accuracy, including true positive rate (TPR) and false positive rate (FPR), as well as the accuracy of estimating the frequency of SDs. ResultsWAVEFRONT achieves the best average validation accuracy of 74% TPR (with 95% confidence interval of 70.8%-76.7%), with less than 1.5% FPR using Delta band EEG. Preliminary evidence suggests that WAVEFRONT can achieve a very good performance (regression with R2 [~=]0.71) in the estimation of SD frequencies. ConclusionsWe demonstrate feasibility and quantify the performance of noninvasive SD detection after severe TBI using an automated algorithm. WAVEFRONT can potentially be used for diagnosis and monitoring of worsening brain injuries to guide treatments by providing a measure of SD frequency. Extension of these results to patients with intact skulls requires further study.

neuroscience↗

bioRxiv2020

Neural silences can be localized rapidly using noninvasive scalp EEG

A rapid and cost-effective noninvasive tool to detect and characterize suppressed neural activity can be of significant benefit for the diagnosis and treatment of many disorders. We propose a novel algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural "silences", using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a novel hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric patients with lobectomy, using less than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53mm), as well for 70 different simulated regions of silence based on a real human head model (11{+/-}0.5mm vs. 54{+/-}2.2mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

neuroscience↗

bioRxiv2018

An algorithm for automated, noninvasive detection of cortical spreading depolarizations based on EEG simulations

We present a novel signal processing algorithm for automated, noninvasive detection of Cortical Spreading Depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress neuronal activity as they propagate across the brains cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address key challenges in detecting CSDs from EEG signals: (i) decay of high spatial frequencies as they travel from the cortical surface to the scalp surface; and (ii) presence of sulci and gyri, which makes it difficult to track the CSD waves as they travel across the cortex. Our algorithm detects and tracks \"wavefronts\" of the CSD wave, and stitches together data across space and time to decide on the presence of a CSD wave. To test our algorithm, we provide different models and complex patterns of CSD waves, including different widths of CSD suppressions, and use these models to simulate scalp EEG signals using head models of 4 subjects from the OASIS dataset. Our results suggest that the average width of suppression that a low-density EEG grid of 40 electrodes can detect is 1.1 cm, which includes a vast majority of CSD suppressions, but not all. A higher density EEG grid having 340 electrodes can detect complex CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus propagation is the hardest to detect because of its small suppression area.

bioengineering↗