We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

BACKGROUND: Standard autonomic testing includes a 10-minute head-up tilt table test to detect orthostatic hypotension. Although this test can detect delayed orthostatic hypotension (dOH) between 3 and 10 minutes of standing, it cannot detect late-onset dOH after 10 minutes of standing. METHODS: To determine whether Valsalva maneuver responses can identify patients who would require prolonged head-up tilt table test to diagnose late-onset dOH; patients with immediate orthostatic hypotension (onset <3 minutes; n=176), early-onset dOH (onset between 3 and 10 minutes; n=68), and late-onset dOH (onset >10 minutes; n=32) were retrospectively compared with controls (n=114) with normal head-up tilt table test and composite autonomic scoring scale score of 0. RESULTS: Changes in baseline systolic blood pressure at late phase 2 (∆SBP VM2 ), heart rate difference between baseline and phase 3 (∆HR VM3 ), and Valsalva ratio were lower and pressure recovery time (PRT) at phase 4 was longer in late-onset dOH patients than in controls. Differences in PRT and ∆HR VM3 remained significant after correcting for age. A PRT ≥2.14 s and ∆HR VM3 ≤15 bpm distinguished late-onset dOH from age- and sex-matched controls. Patients with longer PRT (relative risk ratio, 2.189 [1.579–3.036]) and lower ∆HR VM3 (relative risk ratio, 0.897 [0.847–0.951]) were more likely to have late-onset dOH. Patients with longer PRT (relative risk ratio, 1.075 [1.012–1.133]) were more likely to have early-onset than late-onset dOH. CONCLUSIONS: Long PRT and short ∆HR VM3 can help to identify patients who require prolonged head-up tilt table test to diagnose late-onset dOH.

Background: Center of pressure (COP) is a useful measure for dynamic balance, which directly represents the ability of weight shift and control of the foot, however, there is no study analyzing COP during whole stance phase in the elderly with acute stroke. Methods: A total of 30 acute ischemic stroke patients (mean age±standard deviation [SD], 75.0±5.6 years; 11 females) and 30 propensity score matched controls (mean age±SD, 72.8 ±5.9 years; 17 females) using age, sex, and Korean version of Mini-Mental State Examination score were finally included. Participants performed barefoot walking trials on a pressure-sensitive mat until 20 footfalls in each limb were corrected. To evaluate gait function based on COP, mean and coefficient of variance as variability were calculated for 3 types of variables as followings: (1) durations of sub-phases in stance phase, (2) mean locations and displacement ranges of COP, and (3) velocities. Results: Significant difference was observed in many of both mean and variability variables. Especially, stroke patients had an increased duration, posterior location of COP, wide range of mediolateral and anteroposterior displacement range, and slow velocity in foot flat phase belonging to the double limb stance. In variability variables, stroke patients had significantly larger variabilities of COP velocities in all sub-phases except in foot flat phase and late propulsive phase were stood out. Conclusions: The elderly with acute ischemic stroke had inefficiency during double limb stance and reduced functional balance in single limb stance in both sides, implying impaired gait ability on COP excursion in these patients.