Electrophysiological Information Research Articles

Magnetic Resonance-Guided StereotacticRadioablation for SeptalVentricular Tachycardias.

Stereotactic arrhythmia radioablation (STAR) was introduced to treat ventricular tachycardia (VT) refractory to catheter ablation. No data are now available in the septal VT substrate setting, representing a challenge when using conventional techniques. This study sought to evaluate the arrhythmic burden in patients with septal VT treated with magnetic resonance-guided STAR (MRgSTAR). We enrolled consecutive patients with septal VT substrate. The therapy target was achieved by combining anatomic/functional and electrophysiologic information. Patients were treated with a single fraction of 25Gy adopting MRgSTAR. All patients were clinically followed up, and all implantable cardiac devices were remotely monitored. The efficacy outcome included recurrences of any sustained VT beyond the 6-week blanking period after MRgSTAR. The safety outcome was the incidence of adverse events and atrioventricular block. We included 11 patients with septal substrate VT (median age: 68 years; Q1-Q3: 64.5-78 years; 100% male). Clinical presentation was an electrical storm in 81.8% of patients. No complications occurred after MRgSTAR, and 6(54.5%) patients were discharged on the same day of treatment. During a mean follow-up of 12 ± 6months, the efficacy outcome occurred in 3 (27.3%) cases. A significative reduction of implantable cardioverter-defibrillator (ICD) therapy (23.6 before MRgSTAR vs 1.7 after MRgSTAR; P< 0.001) was observed. Left ventricular ejection fraction increased significantly after treatment (38% [Q1-Q3: 33.5%-42.0%] before MRgSTAR vs 43.8% [Q1-Q3: 35%-47%] after MRgSTAR; P=0.04). No adverse effects were observed in the implantable cardioverter-defibrillator and lead system; in the 7 patients with preserved atrioventricular conduction, no atrioventricular block was reported. MRgSTAR represents a safe and effective strategy for treating septal VT.

Wearable, Battery-Free, Self-Powered Electrochemical Sensors for Personalized Health Monitoring

Recent advances in materials, mechanics, and device architectures form the foundations for rapidly emerging classes of sensors with mechanical characteristics that allow for conformal interfaces with the soft, curvilinear surfaces of the human body. While the field of tissue-integrated biophysical sensors exhibits commendable progress in capturing clinical quality thermal, kinematic, and electrophysiological information, the advancement of complementary biochemical sensors severely lags due to unique challenges associated with seamless integration of delicate biochemical receptors, transducing components, and suitable packaging materials. In this talk, I will discuss non-traditional approaches to address some of these grand challenges. Specifically, I will discuss three technologies that my lab is developing -1) wearable, biofuel cell-inspired sensors for sweat monitoring 2) wearable, soft microfluidics with in-built valving and pumping capabilities for precise, on-demand sensing, and 3) AI-powered, wearable wound monitors for early prediction of chronic wounds.

Effects of Low-Phosphorus Stress on Use of Leaf Intracellular Water and Nutrients, Photosynthesis, and Growth of Brassica napus L.

Phosphorus (P) deficiency is one of the main reasons limiting plant production of Brassica napus L. Exploring the dynamics of leaf intracellular substances and the correlations with photosynthesis and growth helps to understand the response mechanisms of B. napus L. to P deficiency. This study conducted experiments on B. napus L. plants by measuring the leaf electrophysiological parameters, leaf structure, elastic modulus (Em), photosynthesis, and growth indices under different P treatment conditions. The dynamics of leaf intracellular water and nutrients of B. napus L. were calculated and analyzed by using the electrophysiological parameters, and the plant tolerance threshold to low-P stress was discovered. The results indicated that the status of the leaf intracellular water and nutrients remained stable when the P concentration was not lower than 0.250 mmol·L−1, but maximized the photosynthesis and growth at a P level of 0.250 mmol·L−1. The 0.125 mmol·L−1 P concentration significantly decreased the mesophyll cell volume, and the palisade–sponge ratio and tightness degree of leaf tissue structure were remarkably increased. This led to an increase in cell elastic modulus, and significantly improved the water retention capacity of leaf cells. At the same time, the intracellular water use efficiency and total nutrient transport capacity of leaves remained stable. As a result, the photosynthesis and growth of plants were maintained at the same level as that of the control group. However, photosynthesis and growth were clearly inhibited with a further decrease in P concentration. Therefore, 0.125 mmol·L−1 was the tolerance threshold of B. napus L. to low P. With the help of electrophysiological information, the effects of the dynamics of intracellular substances on photosynthesis and growth of B. napus L. under low-P stress can be investigated, and the plant’s adaptive response can be revealed. However, the findings of the current hydroponic study are not directly applicable to field conditions with naturally P-deficient soils.

Screening of New Dendrobium officinale Strains Adapted to Karst Forest Environmental Stress Based on Electrophysiological Detection Method

At present, the main methods of breeding Dendrobium officinale (D. officinale) include introduction and domestication, selective breeding, hybrid breeding, and mutation breeding. In the process, traditional methods of field investigation of agronomic traits are often used to select resistant varieties. Although these breeding methods are effective, they have a certain degree of subjectivity and empiricism, and the breeding cycle is long. Electrophysiological instruments were used in this experiment to test the material transport and metabolic capacity of D. officinale in a karst forest epiphytic environment and to quickly evaluate the suitability of different strains of D. officinale in a drought environment. These instruments detected the data on electrophysiological information of leaves of different strains of D. officinale under long-term drought conditions, providing immediate access to the inherent electrophysiological information of the leaves of these strains. Based on the electrophysiological parameters of D. officinale leaves as defined by the inherent electrophysiological information of plants, the water metabolism, nutrient transport, and metabolic capacity in different leaves were evaluated. The key electrophysiological indexes were verified by combining the results of chlorophyll fluorescence and chlorophyll content. The results indicate the following: (1) Parameters defined based on electrophysiological information effectively characterized the differences in intracellular water utilization, the nutrient transport status, and the metabolic capacity of different D. officinale strains. (2) The intrinsic physiological resistance, intrinsic physiological reactance, active nutrient transport capacity, and passive nutrient transport capacity were closely related to the growth status and chlorophyll function of D. officinale leaves. These electrophysiological parameters could serve as critical indicators for evaluating the drought resistance of D. officinale. (3) Under severe drought stress, strain LH1 exhibited less leaf damage, adequate water/nutrient supply, vigorous life activities, and excellent drought resistance. We found that strain LH1 demonstrates better adaptation to the arid environment of karst forest lands. The electrophysiological detection method employed in this study offers a new technique for screening wild-cultivated D. officinale resistance strains. The results indicate that the real-time online leaf electrophysiological information measured by the method in this study can characterize the energy and material metabolism of crops, greatly improving the efficiency of crop-variety selection and reducing costs. These conclusions can be used to obtain real-time information on the transmission of water and nutrients within plant cells and can provide theoretical support for studying the adaptation mechanisms of crops to adverse environments.

Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.

The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.

Impacts of Epihomobrassinolide and Thiamethoxam·Flutolanil·Azoxystrobin on the Continuous Cropping Stress of Pinellia ternata

Continuous cropping (CC) stress severely limits the growth and industrial development of Pinellia ternata. Epihomobrassinolide (EBR) is a natural product that widely participates in many the physiological activities of many plants. Thiamethoxam·flutolanil·azoxystrobin (TFA) has been registered as a seed coating agent in crop production. In this work, the effects of seeds soaked with EBR, seeds coated with TFA, and their co-application on the plant growth, electrophysiological information (as physiological activities related to plant electrical signals), leaf photosynthesis, plant resistance, bulb quality, and yield of CC P. ternata were evaluated. The aim of this work is to excogitate a practicable agronomic measure for ameliorating the growth of CC P. ternata. The results show that soaking the seeds with EBR or coating the seeds with TFA could effectively enhance the plant height, leaf area, and stem diameter of CC P. ternata, promote its emergence seedling ratio, and decrease its inverted seedling ratio, and their associated application was found to be more efficient. Additionally, their associated application effectively enhanced the intrinsic capacitance (IC), intracellular water metabolism, nutrient transport, and metabolic activity and decreased the intrinsic resistance (IR), impedance (IZ), capacitive reactance (IXc), and inductive reactance (IXL). Meanwhile, their associated application could reliably enhance the photosynthetic capacity and stress resistance, and effectively improve the bulb quality and yield. This study emphasizes that the associated application of seeds soaked with a 0.004% aqueous EBR solution diluted 1000 times and seeds coated with a 24% TFA flowable concentrate at 1.6 mL kg−1 seed can be used as a novel and practicable technology for alleviating the CC stress of P. ternata and ameliorating its growth, electrophysiological information, resistance, quality, and yield.

Stroke and Stroke-Like Episodes: Recurrent Manifestations in GLUT1 Deficiency Syndrome

BackgroundSince the initial description of glucose transporter-1 deficiency syndrome (Glut1-DS) the phenotype of the condition has expanded, even leading to the recognition of atypical manifestations. We report on eight patients with Glut1-DS who experienced at least one episode of acute focal neurological deficits. MethodsWe conducted a retrospective analysis, collecting clinical, electrophysiological, neuroradiological, and genetic information. We focused in particular on three well-documented cases. ResultsAmong 42 patients with Glut1-DS, eight individuals aged between six and 38 years presented with an acute onset of neurological disturbances: dysarthria/aphasia, oral dyskinesia, swallowing difficulties, paresthesia, facial palsy, hemi/monoplegia, vomiting, headache, and behavioral disturbances. When performed, magnetic resonance imaging (MRI) revealed signs of venous congestion and hypoperfusion and electroencephalography showed focal contralateral slowing. Deficits were transient in all patients but one. Four patients (50%) were on a ketogenic diet (KD), and two of these patients had lower than usual ketonemia levels during the episode. In two patients, MRI demonstrated the presence of an ischemic brain lesion. ConclusionsIn Glut1-DS, stroke-like episodes are a recurrent manifestation, particularly during early adulthood, and they were reported in 19% of the patients in our cohort. Stroke mimics should be considered a key feature of Glut1-DS, as other paroxysmal disorders. It remains to be established whether a KD can prevent the recurrence of episodes and, if so, at what level of ketosis. Further observations are needed to confirm the correlation between Glut1-DS and ischemic stroke.

Individualised evaluation based on pathophysiology for moyamoya vasculopathy: application in surgical revascularisation

BackgroundAlthough bypass surgery is an effective treatment for moyamoya vasculopathy (MMV), the incidence of postoperative complications is still high. This study aims to introduce a novel evaluating system based on...

Cognitive performance in aged rats is associated with differences in distinctive neuronal populations in the ventral tegmental area and altered synaptic plasticity in the hippocampus.

Deterioration of cognitive functions is commonly associated with aging, although there is wide variation in the onset and manifestation. Albeit heterogeneity in age-related cognitive decline has been studied at the cellular and molecular level, there is poor evidence for electrophysiological correlates. The aim of the current study was to address the electrophysiological basis of heterogeneity of cognitive functions in cognitively Inferior and Superior old (19-20 months) rats in the ventral tegmental area (VTA) and the hippocampus, having Young (12 weeks) rats as a control. The midbrain VTA operates as a hub amidst affective and cognitive facets, processing sensory inputs related to motivated behaviours and hippocampal memory. Increasing evidence shows direct dopaminergic and non-dopaminergic input from the VTA to the hippocampus. Aged Superior and Inferior male rats were selected from a cohort of 88 animals based on their performance in a spatial learning and memory task. Using in vivo single-cell recording in the VTA, we examined the electrical activity of different neuronal populations (putative dopaminergic, glutamatergic and GABAergic neurons). In the same animals, basal synaptic transmission and synaptic plasticity were examined in hippocampal slices. Electrophysiological recordings from the VTA and hippocampus showed alterations associated with aging per se, together with differences specifically linked to the cognitive status of aged animals. In particular, the bursting activity of dopamine neurons was lower, while the firing frequency of glutamatergic neurons was higher in VTA of Inferior old rats. The response to high-frequency stimulation in hippocampal slices also discriminated between Superior and Inferior aged animals. This study provides new insight into electrophysiological information underlying compromised cerebral ageing. Further understanding of brain senescence, possibly related to neurocognitive decline, will help develop new strategies towards the preservation of a high quality of life.

Advances in conductive hydrogels for neural recording and stimulation.

The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal.

Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

Hoffmeister Effect Optimized Hydrogel Electrodes with Enhanced Electrical and Mechanical Properties for Nerve Conduction Studies.

Flexible epidermal electrodes hold substantial promise in realizing human electrophysiological information collections. Conventional electrodes exhibit certain limitations, including the requirement of skin pretreatment, reliance on external object-assisted fixation, and a propensity of dehydration, which severely hinder their applications in medical diagnosis. To tackle those issues, we developed a hydrogel electrode with both transcutaneous stimulation and neural signal acquisition functions. The electrode consists of a composite conductive layer (CCL) and adhesive conductive hydrogel (ACH). The CCL is designed as a laminated structure with high conductivity and charge storage capacity (CSC). Based on the optimization of Hoffmeister effect, the ACH demonstrates excellent electrical (resistivity of 3.56 Ω·m), mechanical (tensile limit of 1,650%), and adhesion properties (peeling energy of 0.28 J). The utilization of ACH as electrode/skin interface can reduce skin contact impedance and noise interference and enhance the CSC and charge injection capacity of electrodes. As a proof of concept, peripheral nerve conduction studies were performed on human volunteers to evaluate the as-fabricated hydrogel electrodes. Compared with the commercial electrodes, our hydrogel electrodes achieved better signal continuity and lower distortion, higher signal-to-noise ratio (~35 dB), and lower stimulation voltages (up to 27% lower), which can improve the safety and comfort of nerve conduction studies.

Standardized 2D atrial mapping and its clinical applications

The visualization and comparison of electrophysiological information in the atrium among different patients could be facilitated by a standardized 2D atrial mapping. However, due to the complexity of the atrial anatomy, unfolding the 3D geometry into a 2D atrial mapping is challenging. In this study, we aim to develop a standardized approach to achieve a 2D atrial mapping that connects the left and right atria, while maintaining fixed positions and sizes of atrial segments across individuals. Atrial segmentation is a prerequisite for the process. Segmentation includes 19 different segments with 12 segments from the left atrium, 5 segments from the right atrium, and two segments for the atrial septum. To ensure consistent and physiologically meaningful segment connections, an automated procedure is applied to open up the atrial surfaces and project the 3D information into 2D. The corresponding 2D atrial mapping can then be utilized to visualize different electrophysiological information of a patient, such as activation time patterns or phase maps. This can in turn provide useful information for guiding catheter ablation. The proposed standardized 2D maps can also be used to compare more easily structural information like fibrosis distribution with rotor presence and location. We show several examples of visualization of different electrophysiological properties for both healthy subjects and patients affected by atrial fibrillation. These examples show that the proposed maps provide an easy way to visualize and interpret intra-subject information and perform inter-subject comparison, which may provide a reference framework for the analysis of the atrial fibrillation substrate before treatment, and during a catheter ablation procedure.

Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton

In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.

(Invited) seamless Integration of Electronics, Electrochemistry, Microfluidics, and Materials for Next Generation Tissue-Integrated Sensors and Energy Devices

Recent advances in materials, mechanics, and device architectures form the foundations for rapidly emerging classes of sensors and energy devices with mechanical characteristics that allow for conformal interfaces with the soft, curvilinear surfaces of the human body. While the field of tissue-integrated biophysical sensors exhibits commendable progress in capturing clinical quality thermal, kinematic, and electrophysiological information, the advancement of complementary biochemical sensors severely lags due to unique challenges associated with seamless integration of delicate biochemical receptors, transducing components, and suitable packaging materials. Similarly, the vast majority of demonstrated tissue-mounted energy storage and energy harvesting systems unfortunately rely on toxic components that substantially diminish their attractiveness in bio-related applications. In this talk, I will discuss non-traditional approaches to address some of these grand challenges. Specifically, I will discuss three technologies that my lab is developing -1) wearable, self-powered sweat sensors for non-invasive health monitoring 2) wireless, implantable, battery-free sensors and optoelectronics for neuroscience research, and 3) fully resorbable batteries for broad applications in medicine and consumer electronics.

Electrically evoked auditory responses: A classification for brainstem implant placement in Neurofibromatosis Type 2

ObjectiveIn auditory brainstem implant (ABI) surgery, array placement may be optimized by electrophysiological information of adequate brainstem activation gained from electrically evoked auditory brainstem responses (EABR). This study aims 1) to characterize in detail the EABR from ABI implantation, 2) to introduce an EABR Classification Scheme, and 3) to analyze data for their correlation with individual patients’ findings. MethodsOut of a continuous series of 54 patients who received an ABI between 2005 and 2019, 23 Neurofibromatosis Type 2 patients with complete documentation of 154 recordings were selected for offline analysis and for development and evaluation of a new EABR Classification Scheme comprising Class A: three vertex positive peaks, Class B:two peaks, Class C: a combination of one peak and a second melted double peak, Class D: one sole vertex positive peak and Class E: no peaks. ResultsAll 23 subjects showed EABR at final ABI position and experienced auditory sensations at first activation. The most frequent morphology consisted of two peaks, Classes B and C. Identified mean latencies were for P1 0.42 ms (±0.095), P2 1.42 ms (±0.244) and P3 2.41 ms (±0.329). Peak latencies correlated positively with tumor extensions (p < 0.005). ConclusionsThis study provides clear instructions on optimal EABR performance and evaluation. SignificanceThe new EABR Classification Scheme relies on a fast “online” identification of vertex positive peaks at the estimated post-artifact phase. The variability in EABR morphology provides an individual snapshot of the actual structural and functional status of the brainstem.

Clinical magnetocardiography: the unshielded bet-past, present, and future.

Magnetocardiography (MCG), which is nowadays 60 years old, has not yet been fully accepted as a clinical tool. Nevertheless, a large body of research and several clinical trials have demonstrated its reliability in providing additional diagnostic electrophysiological information if compared with conventional non-invasive electrocardiographic methods. Since the beginning, one major objective difficulty has been the need to clean the weak cardiac magnetic signals from the much higher environmental noise, especially that of urban and hospital environments. The obvious solution to record the magnetocardiogram in highly performant magnetically shielded rooms has provided the ideal setup for decades of research demonstrating the diagnostic potential of this technology. However, only a few clinical institutions have had the resources to install and run routinely such highly expensive and technically demanding systems. Therefore, increasing attempts have been made to develop cheaper alternatives to improve the magnetic signal-to-noise ratio allowing MCG in unshielded hospital environments. In this article, the most relevant milestones in the MCG's journey are reviewed, addressing the possible reasons beyond the currently long-lasting difficulty to reach a clinical breakthrough and leveraging the authors' personal experience since the early 1980s attempting to finally bring MCG to the patient's bedside for many years thus far. Their nearly four decades of foundational experimental and clinical research between shielded and unshielded solutions are summarized and referenced, following the original vision that MCG had to be intended as an unrivaled method for contactless assessment of the cardiac electrophysiology and as an advanced method for non-invasive electroanatomical imaging, through multimodal integration with other non-fluoroscopic imaging techniques. Whereas all the above accounts for the past, with the available innovative sensors and more affordable active shielding technologies, the present demonstrates that several novel systems have been developed and tested in multicenter clinical trials adopting both shielded and unshielded MCG built-in hospital environments. The future of MCG will mostly be dependent on the results from the ongoing progress in novel sensor technology, which is relatively soon foreseen to provide multiple alternatives for the construction of more compact, affordable, portable, and even wearable devices for unshielded MCG inside hospital environments and perhaps also for ambulatory patients.

Soft conductive nanocomposites for recording biosignals on skin

Soft conductive nanocomposites have introduced significant breakthroughs in bio-integrated electronics by mitigating the mechanical mismatch between the body and the device. Compared with conventional wearable sensors based on rigid electronic materials, the wearable sensors based on soft nanocomposites are advantageous to long-term and high-quality biosignal recordings. Materials used for the synthesis of the nanocomposites, especially nanofillers, are critical for determining the quality of recorded biosignals and the performance of the nanocomposites. In this review, we focus on recent advances in soft conductive nanocomposites, mainly on their electrical and mechanical properties according to the types of nanofillers, and present their applications to wearable biosignal recording devices. We have classified the nanofillers into four categories: carbon-based nanomaterials, conducting polymers, metal-based nanomaterials, and liquid metals. We then introduce the applications of nanocomposites as wearable sensors that record various biosignals, including electrophysiological, strain, pressure, and biochemical information. In conclusion, a brief outlook on the remaining challenges for future nanomaterial-based bioelectronics is provided.

Automated sleep classification with chronic neural implants in freely behaving canines

Objective. Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function. Approach. Here we develop and validate an automated iEEG-based sleep–wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep–wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep–wake classifier in freely behaving canines. Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen’s Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night; p < 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night; p < 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night; p < 0.001). Significance. These results support the feasibility and accuracy of automated iEEG sleep–wake classifiers for canine behavior investigations.

A MEMS-Based Wearable, Flexible Embedded High-Density Sensor With Self-Adhesive PEDOT:PSS Gel for HD-sEMG Monitoring

The gold standard for dysphagia detection is the videofluoroscopic swallowing study (VFSS), which is expensive equipment, radioactive, and carries the risk of inadvertent aspiration of contrast agents. Surface electromyography (sEMG) provides low-cost, noninvasive access to electrophysiological information and accurate temporal resolution but does not provide good spatial resolution. Five-point and nine-point Laplacian methods (FPM and NPM) can improve spatial resolution. However, metallic interfaces have high impedance and motion artifacts, especially for skin with similar neck curvature, and suffer from modulus mismatch. We designed an embedded high-density sEMG (EWHD-sEMG) sensor and reported a self-adhesive poly(3,4-ethylenedioxythiophene)/poly(styrene-sulfonate) (PEDOT:PSS) gel that can withstand 250 g of adhesion with low modulus, low contact impedance (5 kΩ) and motion artifacts, and high signal-to-noise ratio (SNR, 26 dB). The sensor is made of biocompatible materials and is structured to wear comfortably. FPM and NPM improve the SNR and reduce the correlation from 1 to 0.01. In addition, the sensor can stretch 76.9% of the strain in one thousand repetitions and withstand multiple repetitions and long-term use of numerous sterilization methods. The sensor proposed in this thesis contributes to advancing and developing an sEMG-based clinical assessment of dysphagia.