Epileptic Tissue Localization through Skewness-Based Functional Connectivity in the High-Frequency Band of Intracranial EEG.

Functional connectivity analysis of intracranial electroencephalography (iEEG) plays an important role in understanding the mechanism of epilepsy and seizure dynamics. However, existing connectivity analysis is only suitable for low-frequency bands below 80 Hz. High-frequency oscillations (HFOs) and high-frequency activity (HFA) in the high-frequency band (80-500 Hz) are thought to be specific biomarkers in epileptic tissue localization. However, the transience in duration and variability of occurrence time and amplitudes of these events pose a challenge for conducting effective connectivity analysis. To deal with this problem, we proposed skewness-based functional connectivity (SFC) in the high-frequency band and explored its utility in epileptic tissue localization and surgical outcome evaluation. SFC comprises three main steps. The first step is the quantitative measurement of amplitude distribution asymmetry between HFOs/HFA and baseline activity. The second step is functional network construction on the basis of rank correlation of asymmetry across time. The third step is connectivity strength extraction from the functional network. Experiments were conducted in two separate datasets which consist of iEEG recordings from 59 patients with drug-resistant epilepsy. Significant difference (p<0.001) in connectivity strength was found between epileptic and non-epileptic tissue. Results were quantified via the receiver operating characteristic curve and the area under the curve (AUC). Compared with low-frequency bands, SFC demonstrated superior performance. With respect to pooled and individual epileptic tissue localization for seizure-free patients, AUCs were 0.66 (95% confidence interval (CI): 0.63-0.69) and (0.63 95% CI 0.56-0.71), respectively. For surgical outcome classification, the AUC was 0.75 (95% CI 0.59-0.85). Therefore, SFC can act as a promising assessment tool in characterizing the epileptic network and potentially provide better treatment options for patients with drug-resistant epilepsy.

Automatic Epileptic Tissue Localization through Spatial Pattern Clustering of High Frequency Activity.

High-frequency activity (HFA) in intracranial electroencephalography recordings are diagnostic biomarkers for refractory epilepsy. Clinical utilities based on HFA have been extensively examined. HFA often exhibits different spatial patterns corresponding to specific states of neural activation, which will potentially improve epileptic tissue localization. However, research on quantitative measurement and separation of such patterns is still lacking. In this paper, spatial pattern clustering of HFA (SPC-HFA) is developed. The process is composed of three steps: (1) feature extraction: skewness which quantifies the intensity of HFA is extracted; (2) clustering: k-means clustering is applied to separate column vectors within the feature matrix into intrinsic spatial patterns; (3) localization: the determination of epileptic tissue is performed based on the cluster centroid with HFA expanding to the largest spatial extent. Experiments were conducted on a public iEEG dataset with 20 patients. Compared with existing localization methods, SPC-HFA demonstrates improvement (Cohen's d > 0.2) and ranks top in 10 out of 20 patients in terms of the area under the curve. In addition, after extending SPC-HFA to high-frequency oscillation detection algorithms, corresponding localization results also improve with effect size Cohen's d ≥ 0.48. Therefore, SPC-HFA can be utilized to guide clinical and surgical treatment of refractory epilepsy.

BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System.

Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07 s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced. Schematics, source code, and other materials of BEATS are available at https://github.com/buptantEEG/BEATS.

EEG-based Driving Fatigue Detection during Operating the Steering Wheel Data Section.

Accurate and reliable detecting of driving fatigue using Electroencephalography (EEG) signals is a method to reduce traffic accidents. So far, it is natural to cut the part of operating the steering wheel data away for achieving the relatively high accuracy in detecting driving fatigue using EEG data. However, the data segment during operating the steering wheel also contains valuable information. Moreover, operating the steering wheel is a common practice during actual driving. In this study, we utilize the part of data operating the steering wheel to detecting fatigue. The feature used is the spectral band power calculates from the data. For each experiment and each experimental participant, the data and features are divided into sessions and subjects. Using the divided features, this work performs cross-session and cross-subject verification and comparison on the two classification methods of logistic regression and multi-layer perceptron. To compare the effect, the experiment is conducted on the data both operating the steering wheel and not operating the steering wheel. The result shows that the bias between the average accuracy of two types of data is only 2.27%, and the effect of using multi-layer perceptron is 10.37% better than using logistic regression. This proves that the data segment during operating the steering wheel also contains valid information and can be used for driving fatigue detection.

Tensor-Based EEG Network Formation and Feature Extraction for Cross-Session Driving Drowsiness Detection.

Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect driving drowsiness, to name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on features in each channel separately to identify drowsiness, making them vulnerable to variability across different sessions and subjects without sufficient data. In this paper, we propose a method called Tensor Network Features (TNF) to exploit underlying structure of drowsiness patterns and extract features based on tensor network. This TNF method first introduces Tucker decomposition to tensorized EEG channel data of training set, then features of training and testing tensor samples are extracted from the corresponding subspace matrices through tensor network summation. The performance of the proposed TNF method was evaluated through a recently published EEG dataset during a sustained-attention driving task. Compared with spectral power features and fuzzy entropy features, the accuracy of TNF method is improved by 6.7% and 10.3% on average with maximum value 17.3% and 29.7% respectively, which is promising in developing practical and robust cross-session driving drowsiness detection system.

Genetic polymorphism of KCNH2 confers predisposition of acquired atrial fibrillation in Chinese.

INTRODUCTION: Nonfamiliar atrial fibrillation (AF) is usually associated with acquired structural heart disease, including valvular heart disease, coronary artery disease, and hypertension. Suggestive evidence indicates that these forms of acquired AF are more likely to occur in individuals with a genetic predisposition. We investigated the effect of the potassium channel voltage-gated subfamily member 2 (KCNH2) gene on the prevalence of acquired AF in a Chinese population. METHODS: In a pair-matched, hospital-based case control study (297 vs 297) conducted in Chinese Hans, we investigated 4 tagging single nucleotide polymorphisms (tSNPs), rs1805120, rs1036145, rs3807375, and rs2968857 in the KCNH2 gene, and determined their association with AF acquired from structural heart diseases. RESULTS: We did not observe the association of rs1036145, rs3807375, and rs2968857 with AF. However, we determined that the tSNP, rs1805120, in exon 6 confers the risk of AF in Chinese Hans. Both genotype and allele frequencies of rs1805120 were distributed differently in cases and controls (P = 0.0289 and P = 0.0172, respectively). The most significant association was observed under a recessive model for the minor GG genotype with a 1.45-fold risk of developing AF (95% confidence interval 1.09-1.93, P = 0.012). The significance remained after controlling for the covariates of age, smoking, BMI, hypertension, and diabetes. CONCLUSION: We report a new genetic variation (rs1805120) in the KCNH2 gene that predisposes Chinese Han individuals to the risk of acquired AF. Further genetic and functional studies are required to identify the etiological variants in linkage disequilibrium with this polymorphism.

Prevalence and factors associated with the consumption of betel-nut among military conscripts in Taiwan.

OBJECTIVE: This study evaluates the prevalence of betel-nut chewing among military personnel stationed on Taiwan's offshore islands. Furthermore, this study examines variables to identify which may predict a greater predilection toward betel-nut chewing among the conscript population studied. METHODS: A cross-sectional mass screening was conducted of compulsory military service personnel stationed on Taiwan's offshore islands between August 1 and December 31, 2001. A total of 7574 military employees were included in this survey. Information regarding betel-nut chewing habits were ascertained using a standard structured questionnaire, which including the level and duration of betel-nut chewing as well as respondents' knowledge, attitude and practices with regard to consumption of this product. RESULTS: Conscripts were found to be less likely to chew betel-nut regularly while performing military service. There are 1535 (20.3%) of respondents reporting to habitually chew betel-nut prior to active duty shrank to 1048 (13.8%) after going on active-duty. The most reasons to chew betel-nut among the recruits after military services are curiosity (33.3%) and as a stimulant (29.8%). About 46% of military employees who currently chew betel-nut report an interest to quit in the future. The risk factors for betel-nut chewing include individual factors (e.g., age, education, knowledge, and attitude toward betel-nut chewing), lifestyle habits (e.g., cigarette smoking), and familial factors (e.g., consumption of betel-nut by parents). More interesting, the recruits had the habit of cigarette smoking associated with increase risk for betel-nut chewing (OR: 7.18; 95% CI: 5.66-9.20). CONCLUSIONS: Although the military has made considerable progress in reducing betel-nut chewing on military campuses, the prevalence of betel-nut chewing is still relatively high and, in 2001, affected about one quarter of all military personnel stationed on the abovementioned offshore islands. In future efforts to lower betel-nut consumption among high risk groups, targeting the group of conscripted military personnel described in this study should be considered.

Insulin, proinsulin and insulin resistance status in relation to lipid profiles among school children in Taiwan-The Taipei Children Heart Study.

PURPOSE: Evidence suggests that there may be a metabolic syndrome characterized by hyperinsulinemia or insulin resistance associated with increased cardiovascular disease risk. The purpose of this study is to evaluate insulin, proinsulin or insulin resistance to determine which is the best parameter to predict lipid profiles among children in Taiwan. METHODS: After multi-stage sampling, we randomly included 852 school children (415 boys and 437 girls) with a mean age of 13 yr in this study. We measured insulin and intact proinsulin levels by RIA (<0.2% cross-reactivity) and estimated insulin resistance index (IRI) using the homeostatic model assessment (HOMA) method. We used standard methods to measure atherosclerotic lipid profiles including total cholesterol (CHOL), triglyceride (TG), HDL-C, apolipoprotein A (ApoA), apolipoprotein B (ApoB), and lipoprotein[a] and calculated LDL-C and TCHR (total cholesterol to HDL-C ratio) levels. RESULTS: Girls had higher CHOL, LDL-C, ApoA and ApoB levels than boys (p < 0.001). There was no significant difference in insulin, proinsulin and IRI status between boys and girls. Among boys, insulin, proinsulin and IRI were positively correlated with TG, ApoB and TCHR and negatively related to HDL-C. Among girls, these associations were attenuated and became insignificantly for TCHR and HDL-C. After adjusting for potential confounders, IRI and insulin were still positively associated with TG and ApoB levels and negatively associated with HDL-C in boys. However, in girls, proinsulin and insulin were positively associated with TG only. Finally, in the stepwise regression analyses, IRI was a better predictor of TG, HDL-C, and ApoB than insulin or proinsulin in boys. However, in girls, proinsulin was a stronger predictor than insulin or IRI for TG and TCHR. CONCLUSION: From this study, we found that IRI (in boys) and proinsulin (in girls) levels are generally more significant and stronger parameters than insulin for predicting lipid profiles among children in Taiwan.