All-Nanofiber-Based Janus Epidermal Electrode with Directional Sweat Permeability for Artifact-Free Biopotential Monitoring.

Epidermal electronics have been developed with gas/sweat permeability for long-term wearable electrophysiological monitoring. However, the state-of-the-art breathable epidermal electronics ignore the sweat accumulation and immersion at the skin/device interface, resulting in serious degradation of the interfacial conformality and adhesion, leading to signal artifacts with unstable and inaccurate biopotential measurements. Here, the authors present an all-nanofiber-based Janus epidermal electrode endowed with directional sweat transport properties for artifact-free biopotential monitoring. The designed Janus multilayered membrane (≈15 µm) of superhydrophilic-hydrolyzed-polyacrylonitrile (HPAN)/polyurethane (PU)/Ag nanowire (AgNW) can quickly (less than 5 s) drive sweat away from the skin/electrode interface while resisting its penetration in the reverse direction. Along with the medical adhesive (MA)-reinforced junction-nodes, the adhesion strength among the heterogeneous interfaces can be greatly enhanced for robust mechanical-electrical stability. Therefore, their measured on-body electromyography (EMG) and electrocardiography (ECG) signals are free of sweat artifacts with negligible degradation and baseline drift compared to commercial Ag/AgCl gel electrodes and hydrophilic textile electrodes. This work paves a way to design novel directional-sweat-permeable epidermal electronics that can be conformally attached under sweaty conditions for long-term biopotential monitoring and shows the potential to apply epidermal electronics to many challenging conditions.

State-independent and state-dependent patterns in the rat default mode network.

Resting-state studies have typically assumed constant functional connectivity (FC) between brain regions, and these parameters of interest provide meaningful descriptions of the functional organization of the brain. A number of studies have recently provided evidence pointing to dynamic FC fluctuations in the resting brain, especially in higher-order regions such as the default mode network (DMN). The neural activities underlying dynamic FC remain poorly understood. Here, we recorded electrophysiological signals from DMN regions in freely behaving rats. The dynamic FCs between signals within the DMN were estimated by the phase locking value (PLV) method with sliding time windows across vigilance states [quiet wakefulness (QW) and slow-wave and rapid eye movement sleep (SWS and REMS)]. Factor analysis was then performed to reveal the hidden patterns within the DMN. We identified distinct spatial FC patterns according to the similarities between their temporal dynamics. Interestingly, some of these patterns were vigilance state-dependent, while others were independent across states. The temporal contributions of these patterns fluctuated over time, and their interactive relationships were different across vigilance states. These spatial patterns with dynamic temporal contributions and combinations may offer a flexible framework for efficiently integrating information to support cognition and behavior. These findings provide novel insights into the dynamic functional organization of the rat DMN.

Design and validation of an electrophysiological based tool to assess chronic stress. Case study: burnout syndrome in caregivers.

Caregiver burnout syndrome is an increasingly seen condition, although the subjective nature of self-administered psychometric tests and the lack of a consensual diagnostic tool might hinder a proper diagnosis. The availability of objective psychosomatic measures of stress might facilitate the early diagnosis and clinical management of these patients. For this reason, the aim of this work was to develop a quantitative tool to evaluate the stress level of caregivers in a noninvasive and repeatable manner. An observational, controlled, matched study was designed including a group of 38 principal caregivers of chronic patients and a control group of 38 non-caregivers. Psychometric, biochemical, and electrophysiological data were analyzed along with sociodemographic data. A quantitative chronic stress reference scale (CSRs) was constructed based on the weighted contribution of several psychometric and biochemical variables and afterwards, a predictive psychosomatic model (ESBSm) correlated with CSRs was elaborated from extracted variables of several electrophysiological signals monitored for 10 min. The resulting CSR scale shows a high power to discriminate caregivers from the control group while the ESBSm shows a 79% correlation with the CSR scale validated through a 5-fold process. Therefore, the results demonstrate that the ESBS model is an objective and validated tool to diagnose the degree of stress linked to burnout in caregivers of chronic patients from a 10-min session of noninvasive monitoring with a reliability equivalent to the questionnaires currently used to quantify stress in caregivers.

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators.

Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient's health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data's regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

Recent Advances of Stretchable Nanomaterial-Based Hydrogels for Wearable Sensors and Electrophysiological Signals Monitoring.

Electrophysiological monitoring is a commonly used medical procedure designed to capture the electrical signals generated by the body and promptly identify any abnormal health conditions. Wearable sensors are of great significance in signal acquisition for electrophysiological monitoring. Traditional electrophysiological monitoring devices are often bulky and have many complex accessories and thus, are only suitable for limited application scenarios. Hydrogels optimized based on nanomaterials are lightweight with excellent stretchable and electrical properties, solving the problem of high-quality signal acquisition for wearable sensors. Therefore, the development of hydrogels based on nanomaterials brings tremendous potential for wearable physiological signal monitoring sensors. This review first introduces the latest advancement of hydrogels made from different nanomaterials, such as nanocarbon materials, nanometal materials, and two-dimensional transition metal compounds, in physiological signal monitoring sensors. Second, the versatile properties of these stretchable composite hydrogel sensors are reviewed. Then, their applications in various electrophysiological signal monitoring, such as electrocardiogram monitoring, electromyographic signal analysis, and electroencephalogram monitoring, are discussed. Finally, the current application status and future development prospects of nanomaterial-optimized hydrogels in wearable physiological signal monitoring sensors are summarized. We hope this review will inspire future development of wearable electrophysiological signal monitoring sensors using nanomaterial-based hydrogels.

High-Performance Bidirectional Microelectrode Array for Assessing Sevoflurane Anesthesia Effects and In Situ Electrical Stimulation in Deep Brain Regions.

Precise assessment of wakefulness states during sevoflurane anesthesia and timely arousal are of paramount importance to refine the control of anesthesia. To tackle this issue, a bidirectional implantable microelectrode array (MEA) is designed with the capability to detect electrophysiological signal and perform in situ deep brain stimulation (DBS) within the dorsomedial hypothalamus (DMH) of mice. The MEA, modified with platinum nanoparticles/IrOx nanocomposites, exhibits exceptional characteristics, featuring low impedance, minimal phase delay, substantial charge storage capacity, high double-layer capacitance, and longer in vivo lifetime, thereby enhancing the sensitivity of spike firing detection and electrical stimulation (ES) effectiveness. Using this MEA, sevoflurane-inhibited neurons and sevoflurane-excited neurons, together with changes in the oscillation characteristics of the local field potential within the DMH, are revealed as indicative markers of arousal states. During the arousal period, varying-frequency ESs are applied to the DMH, eliciting distinct arousal effects. Through in situ detection and stimulation, the disparity between these outcomes can be attributed to the influence of DBS on different neurons. These advancements may further our understanding of neural circuits and their potential applications in clinical contexts.

Ultraconformal Skin-Interfaced Sensing Platform for Motion Artifact-Free Monitoring.

Capable of directly capturing various physiological signals from human skin, skin-interfaced bioelectronics has emerged as a promising option for human health monitoring. However, the accuracy and reliability of the measured signals can be greatly affected by body movements or skin deformations (e.g., stretching, wrinkling, and compression). This study presents an ultraconformal, motion artifact-free, and multifunctional skin bioelectronic sensing platform fabricated by a simple and user-friendly laser patterning approach for sensing high-quality human physiological data. The highly conductive membrane based on the room-temperature coalesced Ag/Cu@Cu core-shell nanoparticles in a mixed solution of polymers can partially dissolve and locally deform in the presence of water to form conformal contact with the skin. The resulting sensors to capture improved electrophysiological signals upon various skin deformations and other biophysical signals provide an effective means to monitor health conditions and create human-machine interfaces. The highly conductive and stretchable membrane can also be used as interconnects to connect commercial off-the-shelf chips to allow extended functionalities, and the proof-of-concept demonstration is highlighted in an integrated pulse oximeter. The easy-to-remove feature of the resulting device with water further allows the device to be applied on delicate skin, such as the infant and elderly.

DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification.

Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person's physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/.

MMASleepNet: A multimodal attention network based on electrophysiological signals for automatic sleep staging.

Pandemic-related sleep disorders affect human physical and mental health. The artificial intelligence (AI) based sleep staging with multimodal electrophysiological signals help people diagnose and treat sleep disorders. However, the existing AI-based methods could not capture more discriminative modalities and adaptively correlate these multimodal features. This paper introduces a multimodal attention network (MMASleepNet) to efficiently extract, perceive and fuse multimodal features of electrophysiological signals. The MMASleepNet has a multi-branch feature extraction (MBFE) module followed by an attention-based feature fusing (AFF) module. In the MBFE module, branches are designed to extract multimodal signals' temporal and spectral features. Each branch has two-stream convolutional networks with a unique kernel to perceive features of different time scales. The AFF module contains a modal-wise squeeze and excitation (SE) block to adjust the weights of modalities with more discriminative features and a Transformer encoder (TE) to generate attention matrices and extract the inter-dependencies among multimodal features. Our MMASleepNet outperforms state-of-the-art models in terms of different evaluation matrices on the datasets of Sleep-EDF and ISRUC-Sleep. The implementation code is available at: https://github.com/buptantEEG/MMASleepNet/.

3D Neural Network Composed of Neurospheroid and Bionanohybrid on Microelectrode Array to Realize the Spatial Input Signal Recognition in Neurospheroid.

There have been several studies for demonstration of 2D neural network using living cells or organic/inorganic molecules, but to date, there is no report of development of a 3D neural network in vitro. Based on developed bionanohybrid composed of protein, DNA, molybdenum disulfide nanoparticles, and peptides for controlling electrophysiological states of living cells, here, the in vitro 3D neural network composed of the bionanohybrid, 3D neurospheroid and the microelectrode array (MEA) is developed. After production of the 3D neurospheroid derived from human neural stem cells, the bionanohybrid developed on the MEA successfully semi-penetrates the neurites of the 3D neurospheroid and forms the 3D neural network. The developed 3D neural network successfully exhibited the electrophysiological output signals of the 3D neurospheroid by transmitting the input signal applied by the bionanohybrid. Moreover, by using the selectively immobilized bionanohybrid on the MEA, the spatial input signal recognition in the neurospheroid of 3D neural network is realized for the first time. This newly developed in vitro 3D neural network provides a promising strategy to be applied in brain-on-a-chip, brain disease-related drug efficacy evaluation, bioelectronics, and bioelectronic medicine.

Classification of various nutrient deficiencies in tomato plants through electrophysiological signal decomposition and sample space reduction.

Plants leave testimonies of undergoing physical state by depicting distinct variations in their electrophysiological data. Adequate nutrition of plants signifies their role in the growth and a plentiful harvest. Plant signal data carries enough information to detect and analyse nutrient deficiency. Classification of nutrient deficiencies through signal decomposition and bilevel measurements has not been reported earlier. The proposed work explores tomato plants in four-time cycles (Early Morning, Morning, After Noon, Night) of macronutrients Calcium (Ca), Nitrogen (N) and micronutrients Manganese (Mn), Iron (Fe). Using the Empirical Mode Decomposition method (EMD), signals are decomposed into Intrinsic Mode Functions (IMF) in 10-levels. Further, Intrinsic mode functions are grouped into two clusters to extract descriptive data statistics and bi-level measurements. Then a novel sample selection method is proposed to achieve a better classification rate by reducing sample space. A binary classification model is built to train and test 15 features individually using discriminant analysis and naïve-Bayes classifier variants. The reported results achieved a classification rate up to 98% after 5-fold cross-validation. Attained findings endorse novel pathways for detection and classification of nutrient deficiencies in the early stages, consequently promoting prevention and treatment approaches earliest to the appearance of symptoms, also helping to enhance plant growth.

PEDOT:PSS-Based Bioelectronic Devices for Recording and Modulation of Electrophysiological and Biochemical Cell Signals.

To understand the physiology and pathology of electrogenic cells and the corresponding tissue in their full complexity, the quantitative investigation of the transmission of ions as well as the release of chemical signals is important. Organic (semi-) conducting materials and in particular organic electrochemical transistor are gaining in importance for the investigation of electrophysiological and recently biochemical signals due to their synthetic nature and thus chemical diversity and modifiability, their biocompatible and compliant properties, as well as their mixed electronic and ionic conductivity featuring ion-to-electron conversion. Here, the aim is to summarize recent progress on the development of bioelectronic devices utilizing polymer polyethylenedioxythiophene: poly(styrene sulfonate) (PEDOT:PSS) to interface electronics and biological matter including microelectrode arrays, neural cuff electrodes, organic electrochemical transistors, PEDOT:PSS-based biosensors, and organic electronic ion pumps. Finally, progress in the material development is summarized for the improvement of polymer conductivity, stretchability, higher transistor transconductance, or to extend their field of application such as cation sensing or metabolite recognition. This survey of recent trends in PEDOT:PSS electrophysiological sensors highlights the potential of this multifunctional material to revolve current technology and to enable long-lasting, multichannel polymer probes for simultaneous recordings of electrophysiological and biochemical signals from electrogenic cells.

Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph.

Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.