Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification.

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.

Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection.

Introduction: Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients' suffering. Methods: To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany. Results: The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%. Discussion: All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor's diagnostic process and provide a better medical experience for patients.

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.

Improved HHT-microstate analysis of EEG in nicotine addicts.

Background: Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. Methods: To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. Results: After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods. Conclusion: Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

A nanomesh electrode for self-driven perovskite photodetectors with tunable asymmetric Schottky junctions.

Self-driven photodetectors are essential for many applications where it is unpractical to provide or replace power sources. Here, we report a new device architecture for self-driven photodetectors with tunable asymmetric Schottky junctions based on a nanomesh electrode. The vertical-channel nanomesh scaffold is composed of a hexagonally ordered nanoelectrode array fabricated via the nanosphere lithography technique. The top and bottom nanoelectrodes are separated by only 30 nm and the areal ratio of the two nanoelectrodes can be fine-tuned, which effectively modifies the geometric asymmetricity of the Schottky junctions in the photodetector devices. The self-driven photodetectors are fabricated by depositing the (FAPbI3)0.97(MAPbBr3)0.03 (MA = methylammonium, FA = formamidinium) perovskite films onto the nanomesh electrodes. Under the self-driven mode, the optimized device demonstrates a high detectivity of 1.05 × 1011 Jones and a large on/off ratio of 2.1 × 103. This nanomesh electrode is very versatile and can be employed to investigate the optoelectronic properties of various semiconducting materials.

Humidity-Induced Defect-Healing of Formamidinium-Based Perovskite Films.

Formamidinium (FA)-based perovskite material holds great potential to deliver highly efficient commercial solar cells. However, the FA-based perovskite films are commonly processed under a strictly controlled environment, which would eventually hinder their way to commercialization. Herein, a systematic study is conducted to investigate the sequential deposition of FA-based perovskite films that are annealed under ambient conditions. Unexpectedly, the films prepared in low humidity condition possess less pinholes and defects and exhibit better device performances than those prepared in the moisture-free condition. A series of in situ and ex situ investigations are conducted which reveal defects in perovskite films are continuously healed during the film annealing process under the humid condition. This extraordinary effect is attributed to the interaction between water molecules and perovskite. The current study should shed light on the ambient fabrication of FA-based perovskite solar cells and foster their real-world applications.

EEG microstate in obstructive sleep apnea patients.

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

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.