Scope and Arbitration in Machine Learning Clinical EEG Classification

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit the label of their parent recording. We hypothesised that window labels derived in this manner can be misleading for example, windows without evident abnormalities can be labelled `abnormal' disrupting the learning process and degrading performance. We explored two separable approaches to mitigate this problem: increasing the window length and introducing a second-stage model to arbitrate between the window-specific predictions within a recording. Evaluating these methods on the Temple University Hospital Abnormal EEG Corpus, we significantly improved state-of-the-art average accuracy from 89.8 percent to 93.3 percent. This result defies previous estimates of the upper limit for performance on this dataset and represents a major step towards clinical translation of machine learning approaches to this problem.Comment: 10 pages, 6 figure

Window Stacking Meta-Models for Clinical EEG Classification

Windowing is a common technique in EEG machine learning classification and other time series tasks. However, a challenge arises when employing this technique: computational expense inhibits learning global relationships across an entire recording or set of recordings. Furthermore, the labels inherited by windows from their parent recordings may not accurately reflect the content of that window in isolation. To resolve these issues, we introduce a multi-stage model architecture, incorporating meta-learning principles tailored to time-windowed data aggregation. We further tested two distinct strategies to alleviate these issues: lengthening the window and utilizing overlapping to augment data. Our methods, when tested on the Temple University Hospital Abnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from 89.8 percent to 99.0 percent. This breakthrough performance surpasses prior performance projections for this dataset and paves the way for clinical applications of machine learning solutions to EEG interpretation challenges. On a broader and more varied dataset from the Temple University Hospital EEG Corpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed performance ceiling set by variable inter-rater agreement on such datasets.Comment: 17 pages, 10 figure

Results of a randomized, double blind, placebo controlled, crossover trial of melatonin for treatment of Nocturia in adults with multiple sclerosis (MeNiMS)

© 2018 The Author(s). Background: Nocturia is a common urinary symptom of multiple sclerosis (MS) which can affect quality of life (QoL) adversely. Melatonin is a hormone known to regulate circadian rhythm and reduce smooth muscle activity such as in the bladder. There is limited evidence supporting use of melatonin to alleviate urinary frequency at night in the treatment of nocturia. The aim of this study was to evaluate the effect of melatonin on the mean number of nocturia episodes per night in patients with MS. Methods: A randomized, double blind, placebo controlled crossover trial was conducted. 34 patients with nocturia secondary to multiple sclerosis underwent a 4-day pre-treatment monitoring phase. The patients were randomized to receive either 2 mg per night (taken at bedtime) of capsulated sustained-release melatonin (Circadin®) or 1 placebo capsule for 6 weeks followed by a crossover to the other regimen for an additional 6 weeks after a 1-month washout period. Results: From the 26 patients who completed the study, there was no significant difference observed in the signs or symptoms of nocturia when taking 2 mg melatonin compared to placebo. The primary outcome measure, mean number of nocturia episodes on bladder diaries, was 1.8/night at baseline, and 1.4/night on melatonin, compared with 1.6 for placebo (Medians 1.70, 1.50, and 1.30 respectively, p = 0.85). There was also no significant difference seen in LUTS, QoL and sleep quality when taking melatonin. No significant safety concerns arose. Conclusions: This small study suggests that a low dose of melatonin taken at bedtime may be ineffective therapy for nocturia in MS. Trial registration: (EudraCT reference) 2012-00418321 registered: 25/01/13. ISRCTN Registry: ISRCTN38687869

Hydration of water- and alkali-activated white Portland cement pastes and blends with low-calcium pulverized fuel ash

Pastes of white Portland cement (wPc) and wPc-pulverized fuel ash (pfa) blends were studied up to 13 years. The reaction of wPc with water was initially retarded in the presence of pfa particles but accelerated at intermediate ages. Reaction with KOH solution was rapid with or without pfa. A universal compositional relationship exists for the C-A-S-H in blends of Pc with aluminosilicate-rich SCMs. The average length of aluminosilicate anions increased with age and increasing Al/Ca and Si/Ca; greater lengthening in the blends was due to additional Al3+ at bridging sites. The morphology of outer product C-A-S-H was always foil-like with KOH solution, regardless of chemical composition, but with water it had fibrillar morphology at high Ca/(Si+Al) ratios and foil-like morphology started to appear at Ca/(Si+Al) ≈1.2-1.3, which from the literature appears to coincide with changes in the pore solution. Foil-like morphology cannot be associated with entirely T-based structure