S185. Using surface electromyography to differentiate between generalized tonic-clonic seizures and psychogenic non-epileptic seizures

Abstract

Introduction It is estimated that 25% of patients previously diagnosed with epilepsy who are not responding to anti-epileptic drugs are found to have psychogenic non-epilepsy seizures (PNES). Using frequency and amplitude analyses, the utility of this device can be expanded to provide diagnostic information that may help in the differentiation between captured GTCS and PNES recordings. Methods In a multicenter, phase III trial of the sEMG monitoring device with concomitant vEEG recording, 29 GTC seizures and 11 motor PNES were classified by three ABPN certified epileptologists (via vEEG) while patients were wearing the sEMG recording device. Of these events, 1 of the motor PNES events had electrode issues that affected the sEMG recording. The sEMG for the remaining 10 PNES and 10 randomly selected GTCS were divided into 2-min epochs for feature extraction and analysis. The primary analysis technique involved the transformation of the sEMG data into frequency-driven traces. The two traces were summated wavelet-transform output coefficients from high frequency, 150–260 Hz, and low frequency, 6–70 Hz, ranges, and provide relative magnitude in arbitrary units, (a.u.). An algorithm was developed for extracting features from the traces, most notably the area under the curve (AUC). The AUC of each the high and low frequency traces, as well as the ratio of high/low AUCs, were calculated for the regions within each epoch that met the criteria of being above a threshold that defines baseline data. The AUC ratios for GTCS and PNES were compared using unpaired student’s t-test with Bonferoni correction and found to be significant if p Results The AUC for the high and low frequency traces, as well as the ratio between the high/low frequency traces for GTCS and PNES were calculated. For a GTC, the high frequency AUC was 3,078,615 ± 936,305 a.u., and the low frequency AUC was 16,921,510 ± 4,995,778 a.u. for events captured lasting 50.47 ± 2.48 s. For a PNES, the high frequency AUC was 5873 ± 4355 a.u., and the low frequency AUC was 1,336,344 ± 756,219 a.u. for an average of 87.41 ± 6.10 s captured. An average of the AUC ratios were found statically different between GTCS (0.26 ± 0.09 a.u.) and PNES (0.002 ± 0.002 a.u., p = .018). Conclusion sEMG data, recorded by the Brain Sentinel® device, is sufficient to differentiate muscle activity during GTCS from that recorded during PNES events. The most impactful attribute was the absence of high frequency data in PNES events, with a strong presence in GTCS, with predominance in the tonic phase. The AUC ratio mitigates comparison artifact that could arise from variable seizure energy amplitude or duration, and an important tool for seizure comparison. The features presented in this abstract will provide the tools necessary to differentiate GTCS and PNES using sEMG. Funded by Brain Sentinel®.