Search Results (70)

Health and well-being, both mental and physical, depend largely on adequate sleep. Many conditions arise from a disrupted sleep cycle, significantly deteriorating the quality of life of those affected. The analysis of the sleep cycle provide valuable information about sleep stages, which are employed in sleep medicine for the diagnosis of numerous diseases. The clinical standard for sleep data recording is polysomnography (PSG), which records electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and other signals during sleep activity. Recently, machine learning approaches have exhibited high accuracy in applications such as the classification and prediction of biomedical signals. This study presents a hybrid neural network architecture composed of convolutional neural network (CNN) layers, bidirectional long short-term memory (BiLSTM) layers, and attention mechanism layers in order to process large volumes of EEG data in PSG files. The objective is to design a framework for automated feature extraction. To address class imbalance, an epoch-level random undersampling (E-LRUS) method is proposed, discarding full epochs from majority classes while preserving the temporal structure, unlike traditional methods that remove individual samples. This method has been tested on EEG recordings acquired from the public Sleep EDF Expanded database, achieving an overall accuracy rate of 78.67% along with an F1-score of 72.10%. The findings show that this method proves to be effective for sleep stage classification in patients. Full article

Accurate sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. Recent research efforts on automated sleep staging have focused on complex deep learning architectures that have achieved modest improvements in classification accuracy but have limited real-world applicability due to the complexity of model training and deployment and a lack of interpretability. This paper presents an effective and interpretable sleep staging scheme that follows a classical machine learning pipeline. Multi-domain features were extracted from preprocessed electroencephalogram (EEG) signals, and novel electrooculogram (EOG) features were created to characterize different sleep stages. A two-step feature selection strategy combining F-score pre-filtering and XGBoost feature ranking was designed to select the most discriminating feature subset, which was then fed into an XGBoost model for sleep stage classification. Through a rigorous double-cross-validation procedure, our approach achieved competitive classification performance on the public Sleep-EDF dataset (accuracy 87.0%, F1-score 86.6%, Kappa coefficient 0.81) compared with the state-of-the-art deep learning methods and provided interpretability through feature importance analysis. These promising results demonstrate the effectiveness of the proposed sleep staging model and show its potential in practical applications due to its low complexity, interpretability, and transparency. Full article

In ball game sports, binocular visual function is important for accurately perceiving the distance of various objects in visual space. However, the temporal coordination of binocular eye movements during saccades has not been investigated extensively in athletes. The purpose of the present study was to compare the characteristics found in the interocular timing differences in horizontal saccades between ball game players. The participants included 32 university baseball players and 54 university soccer players. They were asked to shift their gaze to the onset of the light-emitting diodes located at 10 deg of visual field eccentricity to the left and right and alternated every 2 s. Horizontal movements of the left and right eyes were recorded separately with the electro-oculogram. Temporal variables for each eye were calculated with digital differentiation, and timing differences between the left and right eyes were compared between participant groups. The overall results showed significant interocular differences between left and right eye movements for the temporal variables of binocular saccades. The comparison between the participant groups revealed that baseball players had smaller interocular timing differences between the left and right eyes than soccer players in the onset time, time to peak velocity, duration, and peak velocity. These results suggest that baseball players have a higher degree of temporal coordination in binocular eye movements, particularly during the initial phase of horizontal saccades, compared to soccer players. This enhanced coordination might be attributable to the sport-specific visual demands of baseball, where players require precise stereoscopic vision to track a small high-speed ball within their visual space. Full article

Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain–Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model. Full article

This review covers the utility of electrophysiological studies relevant to inflammatory diseases of the retina in conditions such as acute posterior multifocal placoid pigment epitheliopathy, acute zonal occult outer retinopathy, Adamantiades–Behçet disease, autoimmune retinopathy and neuro-retinopathy, birdshot chorioretinopathy, multiple evanescent white dot syndrome, and Vogt–Koyanagi–Harada disease. Electrophysiological studies can help with the diagnosis, prognostication, evaluation of treatment effects, and follow-up for these conditions. Full article

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea–hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA. Full article

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals. Full article

Introduction: Achieving restorative sleep is crucial for overall well-being, yet sleep difficulties affect a substantial portion of the adult population. Sleep disturbances are associated with diminished quality of life, physical complaints, cognitive impairment, and emotional regulation challenges. Objective: This study explores the influence of an innovative experimental bed designed to generate rocking motions on sleep parameters. Methods: A prospective observational study enrolled 60 adult participants, assessing their sleep on a regular stationary bed and the Inoveris bed, providing gentle rocking movements. Polysomnography was conducted, recording electroencephalography, electrooculogram, electromyogram, respiratory effort, and other parameters. Results: The rocking bed significantly increased total sleep time (TST) and reduced N1 sleep stage duration (p < 0.001). Participants also experienced a quicker transition to the N2 sleep stage (p = 0.01), indicative of a faster shift from wakefulness to deeper sleep. Additionally, rocking led to a higher percentage of N1 sleep stages (p = 0.01) and a significant increase in N3 sleep stage duration (p = 0.004). While some results lacked statistical significance, notable trends in the rocking bed group have clinical relevance, consistently improving sleep parameters, including increased TST. The rocking bed also showed a trend towards higher sleep efficiency (SE) and sleep duration percentage, hinting at a potential overall enhancement in sleep quality. Conclusion: This study contributes valuable insights into the potential benefits of rocking motions on sleep architecture. Despite variations in outcomes across studies, our results underscore the potential of rocking beds as a non-pharmacological intervention for enhancing sleep quality. Notable improvements in total sleep time (TST), N1 sleep stage reduction, and accelerated transitions to deeper sleep stages highlight the clinical relevance of rocking interventions. Further research, collaboration, and addressing the identified limitations will advance our understanding of the therapeutic applications of rocking motions in sleep science. Full article

This manuscript describes an implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts cannot be avoided by simply discarding trials. The implementations provided to the reader illustrate, with slight modifications, previously proposed methods aimed at the partial or complete elimination of EEG channels or components obtained after independent component analysis (ICA) of EEG signals. These channels or components are those that resemble the electro-oculogram (EOG) signals in which artifacts are detected. In addition to the description of each of the provided functions, examples of utilization and illustrative figures will be included to show the expected results and processing pipeline. Full article

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States. Two methods are used to learn the causal structure of these data: the well-established Granger causality and “DYNOTEARS”, a modern approach that uses continuous optimisation to learn dynamic Bayesian networks (DBNs). The results from the two methods are then compared. Both methods produce graphs that have a number of similarities, including the mutual causation between electrooculogram (EOG) and electroencephelogram (EEG) signals and between sleeping position and SpO2 (blood oxygen level). However, DYNOTEARS, unlike Granger causality, frequently finds a causal link to sleeping position from the other variables. Following the creation of these causal graphs, the relationship between the discovered causal structure and the characteristics of the participants is explored. It is found that there is an association between the waist size of a participant and whether a causal link is found between the electrocardiogram (ECG) measurement and the EOG and EEG measurements. It is concluded that a person’s body shape appears to impact the relationship between their heart and brain during sleep and that Granger causality and DYNOTEARS can produce differing results on real-world data. Full article

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%). Full article

Inherited retinal dystrophies encompass a diverse group of disorders affecting the structure and function of the retina, leading to progressive visual impairment and, in severe cases, blindness. Electrophysiology testing has emerged as a valuable tool in assessing and diagnosing those conditions, offering insights into the function of different parts of the visual pathway from retina to visual cortex and aiding in disease classification. This review provides an overview of the application of electrophysiology testing in the non-macular inherited retinal dystrophies focusing on both common and rare variants, including retinitis pigmentosa, progressive cone and cone-rod dystrophy, bradyopsia, Bietti crystalline dystrophy, late-onset retinal degeneration, and fundus albipunctatus. The different applications and limitations of electrophysiology techniques, including multifocal electroretinogram (mfERG), full-field ERG (ffERG), electrooculogram (EOG), pattern electroretinogram (PERG), and visual evoked potential (VEP), in the diagnosis and management of these distinctive phenotypes are discussed. The potential for electrophysiology testing to allow for further understanding of these diseases and the possibility of using these tests for early detection, prognosis prediction, and therapeutic monitoring in the future is reviewed. Full article

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity. Full article

Disposable wearable sensors that ultrathin and conformable to the skin are of significant interest as affordable and easy-to-use devices for short-term recording. This study presents a facile and low-cost method for transferring spray-coated silver nanowire (AgNW) composite films onto human skin using glossy paper (GP) and liquid bandages (LB). Due to the moderately hydrophobic and rough surface of the GP, the ultrathin AgNWs composite film (~200 nm) was easily transferred onto human skin. The AgNW composite films conformally attached to the skin when applied with a LB, resulting in the stable and continuous recording of wearable electrophysiological signals, including electromyogram (EMG), electrocardiogram (ECG), and electrooculogram (EOG). The volatile LB, deposited on the skin via spray coating, promoted rapid adhesion of the transferred AgNW composite films, ensuring stability to the AgNWs in external environments. The AgNWs composite supported with the LB film exhibited high water vapor breathability (~28 gm⁻²h⁻¹), which can avoid the accumulation of sweat at the skin–sensor interface. This approach facilitates the creation of rapid, low-cost, and disposable tattoo-like sensors that are practical for extended use. Full article

The present study examined music-induced dopamine release, as measured by a proxy measure of spontaneous eye blinks. Specifically, we explored the effects of uplifting and sombre tones in different sections of Vivaldi’s Four Seasons to investigate the affective content of musical pieces within one composition. Seventeen participants listened to four concertos (Major modes: “Spring” and “Autumn”, Minor modes: “Summer” and “Winter”) and a silence condition while completing a three-stimulus odd-ball attention task. Electrooculograms were recorded from electrodes placed above and under the left eye. Self-reported arousal and music preference measures were also gathered during the testing session. In addition, the P3a Event-Related Potential (ERP) component was analysed as another potential index of dopamine function. Results revealed significant differences in the blink rates during music listening and silence, with the largest effect observed for the sad, melancholic “Winter” concerto. However, no significant correlation was found between blink rate and music preference or arousal. Furthermore, no reliable association was found between blink rate and the P3a ERP component, suggesting that these measures tap into different aspects of dopamine function. These findings contribute to understanding the link between dopamine and blink rate, particularly in response to classical music. Crucially, the study’s discovery that the “Winter” concerto, with its sorrowful tone, significantly increased the blink rate highlights the significance of sad music and perhaps the programmatic qualities of this concerto to induce a strong emotional response. Full article