Classification of bruxism based on time-frequency and nonlinear features of single channel EEG.

BACKGROUND: In the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in bruxism classification, but the classification performance based on single channel EEG signal is still understudied. We investigate the efficacy of single EEG channel in bruxism classification. METHODS: We have extracted time-domain, frequency-domain, and nonlinear features from single EEG channel to classify bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features (sample entropy) of different EEG frequency bands were analyzed from five EEG channels of each participant. Fine tree algorithm was trained and tested for classifying sleep bruxism with healthy controls using five-fold cross-validation. RESULTS: Our results demonstrate that the C4P4 EEG channel was most effective for classification of sleep bruxism that yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV). CONCLUSIONS: Our results illustrate the feasibility of sleep bruxism classification using single EEG channel and provides an experimental foundation for the development of a future portable automatic sleep bruxism detection system.

A three-dimensional culture system for generating cardiac spheroids composed of cardiomyocytes, endothelial cells, smooth-muscle cells, and cardiac fibroblasts derived from human induced-pluripotent stem cells.

Cardiomyocytes (CMs), endothelial cells (ECs), smooth-muscle cells (SMCs), and cardiac fibroblasts (CFs) differentiated from human induced-pluripotent stem cells (hiPSCs) are the fundamental components of cell-based regenerative myocardial therapy and can be used as in-vitro models for mechanistic studies and drug testing. However, newly differentiated hiPSC-CMs tend to more closely resemble fetal CMs than the mature CMs of adult hearts, and current techniques for improving CM maturation can be both complex and labor-intensive. Thus, the production of CMs for commercial and industrial applications will require more elementary methods for promoting CM maturity. CMs tend to develop a more mature phenotype when cultured as spheroids in a three-dimensional (3D) environment, rather than as two-dimensional monolayers, and the activity of ECs, SMCs, and CFs promote both CM maturation and electrical activity. Here, we introduce a simple and reproducible 3D-culture-based process for generating spheroids containing all four cardiac-cell types (i.e., cardiac spheroids) that is compatible with a wide range of applications and research equipment. Subsequent experiments demonstrated that the inclusion of vascular cells and CFs was associated with an increase in spheroid size, a decline in apoptosis, an improvement in sarcomere maturation and a change in CM bioenergetics.

Blending Human and Machine: Feasibility of Measuring Fatigue Through the Aviation Headset.

OBJECTIVE: To determine viability of drowsiness detection, researchers study the feasibility of photoplethysmogram (PPG) data collection from the geography of the aviation headset, correlating to electrocardiogram (ECG) reference. BACKGROUND: Fatigue has been a probable cause, contributing factor, or a finding in 20% of transportation incidents and accidents studied between January 2001 and December 2012. This operational hazard is particularly troublesome within aviation and airline operations. METHOD: PPG and ECG data were collected synchronously from Federal Aviation Administration (FAA) commercially rated pilots during flight simulation in the window of circadian low (WOCL). Valid PPG and ECG data from 14 participants were analyzed, which yielded approximately 2 hr of data per participant for fatigue-related analysis. RESULTS: The results of the study demonstrate clear trends toward decreased heart rate for both ECG and PPG and suggest progression of drowsiness between four separate periods (T1, T2, T3, and T4) selected during the study; however, the mean heart rate change from T1 to T4 was statistically significant. CONCLUSION: The results suggest that ECG and PPG data can be an important tool to observe conditions where drowsiness or fatigue may add risk to the operation. In addition, the data show high correlation between ECG and PPG data, further suggesting that a simpler PPG sensor, mounted within the geography of the aviation headset, may streamline the operationalization of important physiological data. APPLICATION: Incorporation of PPG sensors and associated signal processing methods into facilitating equipment, such as the aviation headset, may add a layer to operational safety.

EEG-based Classification of Microsleep by Means of Feature Selection: An Application in Aviation.

This paper presents a method for classification of microsleep (MS) from baseline utilizing linear and non-linear features derived from electroencephalography (EEG), which is recorded from five brain regions: frontal, central, parietal, occipital, and temporal. The EEG is acquired from sixteen commercially-rated pilots during the window of circadian low (2:00 am-6:00 am). MS events are annotated using the Driver Monitoring System and further verified using electrooculogram (EOG). A total of 55 features are extracted from EEG. A subset of these features is then selected using a wrapper-based method. The selected features are fed into a linear or quadratic discriminant analysis (LDA or QDA) classifier to automatically differentiate baseline from MS states. The overall classification performance of the best-proposed algorithm is 87.11% in terms of F1 score. This preliminary result highlights the potential of the proposed method towards automatic drowsiness detection which could assist mitigating aviation accidents in the future, pending hardware development to record such EEG signals from the confines of the aviation headset.