The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.

In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.

Objective. This research attempted to explore the neuroprotective effect of choline and establish evidence for future dietary recommendations and nutritional interventions to maintain a proper cognitive function among elders aged >60 years in the US. Method. This cross-sectional study retrieved data of 2,393 eligible elderly participants from the 2011-2014 National Health and Nutrition Examination Survey. Combining dietary and supplement choline intake, total choline intake was evaluated using the 24-hour dietary recall method and the dietary supplement questionnaire. Total choline intake was categorized into tertiles, which ranged at 399.50 mg/day (T3). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning subtest, Animal Fluency (AF) test, and Digit Symbol Substitution test (DSST) was used to measure cognitive function. Participants who scored the lowest 25^th percentile in each cognitive test were classified in the low cognitive function (LC) group. Logistic regression models were implemented to examine the association between total choline intake and the incidence of LC. Results. In the CERAD test, the risk of LC was significantly lower in T2 than T1 (OR: 0.668, 95% CI: 0.493-0.904, and ) when adjusted for age, gender, BMI, alcohol consumption, and hypertension. Similarly, T2 was associated with a significantly lower risk of LC when assessed by the AF test (OR: 0.606, 95% CI: 0.580-0.724, and ) and DSST (0.584, 95% CI: 0.515-0.661, and ). In all three cognitive measures, the T3 of the total choline intake was not associated with cognitive function compared to T1. Conclusion. Total choline intake at 187.06-399.50 mg/day reduces the risk of LC by approximately 50% compared to intake at <187.6 mg/day. The findings of this research may be used to establish dietary recommendations and nutritional interventions to optimize the cognitive function among elders.

In medical visualization, nursing notes contain rich information about a patient’s pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all ). Multivariate COX analysis exhibited that sentiment polarity (HR: 0.499, 95% CI: 0.409-0.610, ) and sentiment subjectivity (HR: 0.710, 95% CI: 0.559-0.902, ) were inversely associated with in-hospital 28-day mortality, while the SAPS-II score (HR: 1.034, 95% CI: 1.029-1.040, ) was positively correlated with in-hospital 28-day mortality. The median death time of patients with was significantly earlier than that of patients with (13.5 vs. 49.8 days, ). In conclusion, sentiments in nursing notes are associated with the in-hospital 28-day mortality and survival of sepsis patients.

Background. To analyze the association between serum 25-hydroxyvitamin D level (25(OH)D) and stroke risk based on the National Health and Nutrition Examination Survey (NHANES). Methods. Between 2007 and 2018, the baseline information of participants from NHNES was collected. Univariate analysis was used to identify the covariates. Multivariate logistic regression model was used to analyze the association between serum 25(OH)D level and the stroke risk. Results. Of the 8,523 participants, there were 310 participants with stroke and 8,213 participants without stroke. The multivariate logistic analysis showed that serum 25(OH)D deficiency (odds ratio (OR): 1.993, 95% confidence intervals (CI): 1.141-3.481, and ) was the significant risk factors for stroke. Subgroup analysis showed that non-Hispanic whites with serum 25(OH)D deficiency (OR: 2.501, 95% CI: 1.094-5.720, and ) and insufficiency (OR: 1.853, 95% CI: 1.170-2.934, and ) were associated with a higher risk of stroke than those with normal 25(OH)D levels. Conclusions. Serum 25(OH)D deficiency may be associated with an increased risk of stroke.

Epilepsy is a neurological disorder and generally detected by electroencephalogram (EEG) signals. The manual inspection of epileptic seizures is a time‐consuming and laborious process. Extensive automatic detection algorithms were proposed by using traditional approaches, which show good accuracy for several specific EEG classification problems but perform poorly in others. To address this issue, the authors present a novel model, named SeizureNet, for robust detection of epileptic seizures using EEG signals based on convolutional neural network. Firstly, they utilise two convolutional neural networks to extract time‐invariant features from single‐channel EEG signals. Then, a fully connected layer is employed to learn high‐level features. Finally, these features are supplied to a softmax layer to classify. They evaluated the model on a benchmark database provided by the University of Bonn and adopted a ten‐fold cross‐validation approach. The proposed model has achieved the accuracy of 98.50–100.00% in classifying non‐seizure and seizure, 97.00–99.00% in classifying healthy, inter‐ictal and ictal, and 95.84% in classifying among five‐class EEG states.