Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy.

Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".

Single-fiber F waves compared with conventional surface F waves, and their utility in detecting early diabetic neuropathy.

INTRODUCTION: The single-fiber F-wave (SFF-wave) technique assesses the entire length of single motor fibers using a concentric needle. Herein we investigated the utility of this approach in the detection of early diabetes-related neuropathy, and compared it with the use of conventional surface F waves (CF waves). METHODS: Sixteen patients with diabetes and either no neuropathy or mild neuropathy were assessed and compared with 16 age- and height-matched control participants. RESULTS: Both CF and SFF waves were abnormal in all 5 patients who had mild neuropathy. However, SFF waves demonstrated subclinical abnormalities in 7 of 11 patients (64%) with no neuropathy, whereas only 2 of these patients (18%) had prolonged CF waves. Minimum F-wave latency was comparable between techniques, but maximum SFF-wave latency was more frequently prolonged, as these delayed motor units were better isolated, rather than buried among summated CF-wave responses. DISCUSSION: SFF waves highlight the segmental involvement in diabetic neuropathy, and use of the SFF-wave technique detects more abnormalities than with CF waves. Muscle Nerve 58: 665-670, 2018.

Fibular motor nerve conduction studies: Investigating the mechanism for compound muscle action potential amplitude drop with proximal stimulation.

INTRODUCTION: The compound muscle action potential (CMAP) amplitude of extensor digitorum brevis can show a drop with proximal stimulation in normal fibular nerves. METHODS: We assessed the contribution of the far-field potential recorded by the reference electrode (R-CMAP) to the fibular CMAP. Negative R-CMAP amplitude, peak-to-peak amplitude, and negative area were measured and correlated with the amplitude decrease. Fibular motor nerves from 14 healthy participants were studied. RESULTS: The mean CMAP amplitude drop with proximal stimulation was 14.0 ± 9.3%, including a >30% reduction in 1 study. All measured R-CMAP parameters correlated with the degree of amplitude drop. CONCLUSIONS: A greater R-CMAP contribution to the fibular CMAP leads to greater phase cancellation and temporal dispersion. The resulting amplitude drop seen in the proximal CMAP can be large enough to be classified incorrectly as "probable conduction block" by several different diagnostic criteria.

Water-soluble adhesive for stable long-term ambulatory EEG recordings.

OBJECTIVE: Conventional methods used to adhere EEG electrodes are often uncomfortable. Here, we present a polymer-based water-soluble EEG adhesive that can be maintained for up to 6 days. The primary outcome measure of this study is the median electrode impedance at day 6. METHODS: Impedance measurements for 841 EEG recordings using a 21 channel 10-20 configuration were remotely logged daily for 6 days after connection. A novel electrode adhesive was used to attach EEG electrodes. Patients were instructed to maintain their electrodes on day 4. RESULTS: Median electrode impedances were significantly below 10kOhms for each day of recording, with a median value on day 6 of 4.18kOhms. Impedance values were significantly lower on day 5 than on day 4, demonstrating that the maintenance process can reduce impedance. Except for day 4-5, the median impedance increased each day. No significant difference was found on the first or final day between clinics or residences from areas of different geographic remoteness. CONCLUSIONS: EEG is able to be recorded in patients homes for 6 days with acceptable impedance and no significant effect of regionality or patients age. SIGNIFICANCE: To the best of our knowledge, this is the first report in the literature of impedance data from long-term ambulatory EEG studies.