Comparison of Guillain-Barre Syndrome Cases during and Prior to the COVID-19 Pandemic: A Multicentric Study.

BACKGROUND: Guillain-Barre syndrome (GBS) is one of the most common neurological manifestations associated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Although data for a strong causal association is lacking, anecdotal reports, case series and systematic reviews linking the two have emerged in the literature. This prompted us to compare the clinical features, electrophysiology, and outcomes of GBS cases presenting during the pandemic with cases reported during a similar time period prior to the pandemic. MATERIALS AND METHODS: Prospective data of GBS cases diagnosed as per the National Institute of Neurological Disorders and Stroke (NINDS) criteria was collected for a 6-month period (July-December 2021) at three tertiary care teaching hospitals during the coronavirus pandemic and compared with retrospective records-based data of cases prior to the pandemic (January-July 2019). RESULTS: A total of 40 cases were included in the cases, out of which 17 were in the prepandemic and 23 in the postpandemic period. A total of three cases temporally related to coronavirus disease 2019 (COVID-19) infection and four cases following COVID-19 vaccination were seen in the pandemic cohort. The clinical features, electrophysiological features, and outcomes were comparable during both periods. A slightly higher rate of in-hospital complications and single mortality was reported in the postpandemic period. DISCUSSION: The number of GBS hospital admissions, clinical presentation, electrodiagnostic features, and short-term outcomes did not differ significantly between the prepandemic and postpandemic periods; a slightly higher incidence of in-hospital complications was observed during the pandemic period. How to cite this article: Panicker P, R D, V AG, et al. Comparison of Guillain-Barre Syndrome Cases during and Prior to the COVID-19 Pandemic: A Multicentric Study. J Assoc Physicians India 2023;71(9):69-71.

Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features.

Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%-80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use.