Mark Musngi’s research while affiliated with Diné College and other places

Epilepsy affects approximately 67 million people worldwide with up to 75% from developing countries. Diagnosing epilepsy using electroencephalogram (EEG) is complicated due to its poor signal-to-noise ratio, high sensitivity to various forms of artifacts, and low spatial resolution. Laplacian EEG signal via novel and noninvasive tripolar concentric ring electrodes (tEEG) is superior to EEG via conventional disc electrodes due to its unique capabilities, which allow automatic attenuation of common movement and muscle artifacts. In this work, we apply exponentially embedded family (EEF) to show feasibility of automatic detection of gamma band high-frequency oscillations (HFOs) in tEEG data from two human patients with epilepsy as a step toward the ultimate goal of using the automatically detected HFOs as auxiliary features for seizure onset detection to improve diagnostic yield of tEEG for epilepsy. Obtained preliminary results suggest the potential of the approach and feasibility of detecting HFOs in tEEG data using the EEF based detector with high accuracy. Further investigation on a larger dataset is needed for a conclusive proof.

While progress has been made in design optimization of concentric ring electrodes maximizing the accuracy of the surface Laplacian estimation, it was based exclusively on the negligible dimensions model of the electrode. Recent proof of concept of the new finite dimensions model that adds the radius of the central disc and the widths of concentric rings to the previously included number of rings and inter-ring distances provides an opportunity for more comprehensive design optimization. In this study, the aforementioned proof of concept was developed into a framework allowing direct comparison of any two concentric ring electrodes of the same size and with the same number of rings. The proposed framework is illustrated on constant and linearly increasing inter-ring distances tripolar concentric ring electrode configurations and validated on electrocardiograms from 20 human volunteers. In particular, ratios of truncation term coefficients between the two electrode configurations were used to demonstrate the similarity between the negligible and the finite dimension models analytically (p = 0.077). Laplacian estimates based on the two models were calculated on electrocardiogram data for emulation of linearly increasing inter-ring distances tripolar concentric ring electrode. The difference between the estimates was not statistically significant (p >> 0.05) which is consistent with the analytic result.

Laplacian electroencephalogram signal from novel and noninvasive tripolar concentric ring electrodes has been demonstrated to have superior performance compared to the electroencephalogram from conventional disc electrodes due to its unique capabilities which allow automatic attenuation of common movement and muscle artifacts in applications including brain-computer interface, seizure onset detection, and detection of high-frequency oscillations and seizure onset zones. This review paper covers the recent advances in the fields of high-frequency oscillations and seizure onset detection based on tripolar Laplacian electroencephalography in animal models and human data to improve the diagnostic yield of electroencephalography for epilepsy. Progression of methodologies utilized including integration of multiple sensors using exponentially embedded families and results obtained including a comparison to the results of others as well as to the performance of the same detector on simultaneously recorded electroencephalogram via conventional disc electrodes is discussed in detail. Specific advantages of using this particular sensor for these particular applications are highlighted. Promising directions for the future work and an overview of currently ongoing research are discussed along with the potential of combining the two detectors and using automatically detected high-frequency oscillations that have been shown to be indicative of early seizure development as auxiliary features for the seizure onset detection.