Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Silk Fabric Functionalized by Nanosilver Enabling the Wearable Sensing for Biomechanics and Biomolecules

Integrating biomechanical and biomolecular sensing mechanisms into wearable devices is a formidable challenge and key to acquiring personalized health management. To address this, we have developed an innovative multifunctional sensor enabled by plasma functionalized silk fabric, which possesses multimodal sensing capabilities for biomechanics and biomolecules. A seed-mediated in situ growth method was employed to coat silver nanoparticles (AgNPs) onto silk fibers, resulting in silk fibers functionalized with AgNPs (SFs@Ag) that exhibit both piezoresistive response and localized surface plasmon resonance effects. The SFs@Ag membrane enables accurate detection of mechanical pressure and specific biomolecules during wearable sensing, offering a versatile solution for comprehensive personalized health monitoring. Additionally, a machine learning algorithm has been established to specifically recognize muscle strain signals, potentially extending to the diagnosis and monitoring of neuromuscular disorders such as amyotrophic lateral sclerosis (ALS). Unlike electromyography, which detects large muscles in clinical medicine, sensing data for tiny muscles enhance our understanding of muscle coordination using the SFs@Ag sensor. This detection model provides feasibility for the early detection and prevention of neuromuscular diseases. Beyond muscle stress and strain sensing, biomolecular detection is a critical addition to achieving effective health management. In this study, we developed highly sensitive surface-enhanced Raman scattering (SERS) detection for wearable health monitoring. Finite-difference time-domain numerical simulations ware utilized to analyze the efficacy of the SFs@Ag sensor for wearable SERS sensing of biomolecules. Based on the specific SERS spectra, automatic extraction of signals of sweat molecules was also achieved. In summary, the SFs@Ag sensor bridges the gap between biomechanical and biomolecular sensing in wearable applications, providing significant value for personalized health management.

OECT - Inspired electrical detection

Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.

Consensus for experimental design in electromyography (CEDE) project: Checklist for reporting and critically appraising studies using EMG (CEDE-Check)

The diversity in electromyography (EMG) techniques and their reporting present significant challenges across multiple disciplines in research and clinical practice, where EMG is commonly used. To address these challenges and augment the reproducibility and interpretation of studies using EMG, the Consensus for Experimental Design in Electromyography (CEDE) project has developed a checklist (CEDE-Check) to assist researchers to thoroughly report their EMG methodologies. Development involved a multi-stage Delphi process with seventeen EMG experts from various disciplines. After two rounds, consensus was achieved. The final CEDE-Check consists of forty items that address four critical areas that demand precise reporting when EMG is employed: the task investigated, electrode placement, recording electrode characteristics, and acquisition and pre-processing of EMG signals. This checklist aims to guide researchers to accurately report and critically appraise EMG studies, thereby promoting a standardised critical evaluation, and greater scientific rigor in research that uses EMG signals. This approach not only aims to facilitate interpretation of study results and comparisons between studies, but it is also expected to contribute to advancing research quality and facilitate clinical and other practical applications of knowledge generated through the use of EMG.

Potential Use of the Cholesterol Transfer Inhibitor U18666A as a Potent Research Tool for the Study of Cholesterol Mechanisms in Neurodegenerative Disorders

Cholesterol is an essential component of mammalian cell membranes and a precursor for crucial signaling molecules. The brain contains the highest level of cholesterol in the body, and abnormal cholesterol metabolism links to many neurodegenerative disorders. The results indicate that faulty cholesterol metabolism is a common feature among people living with neurodegenerative conditions. The researchers suggest that restoring cholesterol levels may become a beneficial new strategy in treating certain neurodegenerative conditions. Several neurodegenerative disorders, such as Alzheimer's disease (AD), Niemann-Pick type C (NPC) disease, and Parkinson's disease (PD), have been connected to abnormalities in brain cholesterol metabolism. Consequently, using a lipid research tool is vital to study further and understand the effect of lipids in neurodegenerative disorders such as NPC, AD, PD, and Huntington's disease (HD). U18666A, also known as 3-(2-(diethylamino) ethoxy) androst-5-en-17-one, is a pharmaceutical drug that suppresses cholesterol trafficking and is a well-known class-2 amphiphile. U18666A has performed many functions, allowing for essential discoveries in lipid studies and shedding light on the pathophysiology of neurodegenerative disorders. Additionally, U18666A prevented the downregulation of low-density lipoprotein (LDL) receptors that are induced by LDL and led to the buildup of cholesterol in lysosomes. Numerous studies show that U18666A impacts the function of cholesterol trafficking to control the metabolism and transport of amyloid precursor proteins (APPs). Treating cortical neurons with U18666A may provide a new in vitro model system for studying the underlying molecular process of NPC, AD, HD, and PD. In this article, we review the mechanism and function of U18666A as a vital tool for studying cholesterol mechanisms in neurological diseases related to abnormal cholesterol metabolism, such as AD, NPC, HD, and PD.

N. M, Choi W, Pan S. ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

Exploration and Application of a Muscle Fatigue Assessment Model Based on NMF for Multi-Muscle Synergistic Movements

Muscle fatigue significantly impacts coordination, stability, and speed in daily activities. Accurate assessment of muscle fatigue is vital for effective exercise programs, injury prevention, and sports performance enhancement. Current methods mostly focus on individual muscles and strength evaluation, overlooking overall fatigue in multi-muscle movements. This study introduces a comprehensive muscle fatigue model using non-negative matrix factorization (NMF) weighting. NMF is employed to analyze the duration multi-muscle weight coefficient matrix (DMWCM) during synergistic movements, and four electromyographic (EMG) signal features in time, frequency, and complexity domains are selected. Particle Swarm Optimization (PSO) optimizes feature weights. The DMWCM and weighted features combine to calculate the Comprehensive Muscle Fatigue Index (CMFI) for multi-muscle synergistic movements. Experimental results show that CMFI correlates with perceived exertion (RPE) and Speed Dynamic Score (SDS), confirming its accuracy and real-time tracking in assessing multi-muscle synergistic movements. This model offers a more comprehensive approach to muscle fatigue assessment, with potential benefits for exercise training, injury prevention, and sports medicine.

Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals' quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5-15 Hz range to identify significant differences. Upon analysis of the 5-15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering.

Accurate wavelet thresholding method for ECG signals

Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.

Intrinsically Stretchable, Self-Healing, and Large-Scale Epidermal Bioelectrode Arrays for Electrophysiology and Gesture Recognition

Electrophysiological (EP) signals, referred to as low-level biopotentials driven by active or passive human movements, are of great importance for kinesiology, rehabilitation, and human-machine interaction. To capture high-fidelity EP signals, bioelectrodes should possess high conductivity, high stretchability, and high conformability to skin. While traditional metal bioelectrodes are endowed with stretchability via complex structural designs, they are vulnerable to external or internal inference due to their low fracture strain and large modulus. Here, we report a self-healing elastic composite of silver nanowire (AgNW), graphite nanosheet, and styrene-block-poly(ethylene-ran-butylene)-block-polystyrene, which exhibits high stretchability of ε = 500%, high conductivity of σ = ∼1923 S·cm-1, and low resistance change (ΔR/R0) of 0.14 at ε = 40% while its resistance increases ∼0.8% after a 24 h stretching operation at ε = 50%. We employed the elastic composites for accurate and stable monitoring of electrocardiograph and surface electromyography (sEMG) signals. Further, we demonstrate an all-solution and printable process to obtain a large-scale sEMG bioelectrode array, enabling highly conformal adhesion on skin and high-fidelity gesture recognition.

Editorial: Biomedical Data in Human-Machine Interaction

Analysis of biomedical data can provide useful information regarding human condition and as a result-analysis of these signals has become one of the most popular diagnostic methods [...].

Relationship between EMG and fNIRS during Dynamic Movements

In the scientific literature focused on surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS), which have been described together and separately many times, presenting different possible applications, researchers have explored a diverse range of topics related to these advanced physiological measurement techniques. However, the analysis of the two signals and their interrelationships continues to be a focus of study in both static and dynamic movements. The main purpose of this study was to determine the relationship between signals during dynamic movements. To carry out the analysis described, the authors of this research paper chose two sports exercise protocols: the Astrand-Rhyming Step Test and the Astrand Treadmill Test. In this study, oxygen consumption and muscle activity were recorded from the gastrocnemius muscle of the left leg of five female participants. This study found positive correlations between EMG and fNIRS signals in all participants: 0.343-0.788 (median-Pearson) and 0.192-0.832 (median-Spearman). On the treadmill, the signal correlations between the participants with the most active and least active lifestyle achieved the following medians: 0.788 (Pearson)/0.832 (Spearman) and 0.470 (Pearson)/0.406 (Spearman), respectively. The shapes of the changes in the EMG and fNIRS signals during exercise suggest a mutual relationship during dynamic movements. Furthermore, during the treadmill test, a higher correlation was observed between the EMG and NIRS signals in participants with a more active lifestyle. Due to the sample size, the results should be interpreted with caution.

The Use of Sports Rehabilitation Robotics to Assist in the Recovery of Physical Abilities in Elderly Patients with Degenerative Diseases: A Literature Review

The increase in the number of elderly patients with degenerative diseases has brought additional medical and financial pressures, which are adding to the burden on society. The development of sports rehabilitation robotics (SRR) is becoming increasingly sophisticated at the technical level of its application; however, few studies have analyzed how it works and how effective it is in aiding rehabilitation, and fewer individualized exercise rehabilitation programs have been developed for elderly patients. The purpose of this study was to analyze the working methods and the effects of different types of SRR and then to suggest the feasibility of applying SRR to enhance the physical abilities of elderly patients with degenerative diseases. The researcher's team searched 633 English-language journal articles, which had been published over the past five years, and they selected 38 of them for a narrative literature review. Our summary found the following: (1) The current types of SRR are generally classified as end-effector robots, smart walkers, intelligent robotic rollators, and exoskeleton robots-exoskeleton robots were found to be the most widely used. (2) The current working methods include assistant tools as the main intermediaries-i.e., robots assist patients to participate; patients as the main intermediaries-i.e., patients dominate the assistant tools to participate; and sensors as the intermediaries-i.e., myoelectric-driven robots promote patient participation. (3) Better recovery was perceived for elderly patients when using SRR than is generally achieved through the traditional single-movement recovery methods, especially in strength, balance, endurance, and coordination. However, there was no significant improvement in their speed or agility after using SRR.

Clinical application and research progress of extracellular slow wave recording in the gastrointestinal tract

The physiological function of the gastrointestinal (GI) tract is based on the slow wave generated and transmitted by the interstitial cells of Cajal. Extracellular myoelectric recording techniques are often used to record the characteristics and propagation of slow wave and analyze the models of slow wave transmission under physiological and pathological conditions to further explore the mechanism of GI dysfunction. This article reviews the application and research progress of electromyography, bioelectromagnetic technology, and high-resolution mapping in animal and clinical experiments, summarizes the clinical application of GI electrical stimulation therapy, and reviews the electrophysiological research in the biliary system.