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Comprehensive Summary: In vivo monitoring of bioelectrical and biochemical signals with implanted electrodes has received great interest over the past decades. However, this faces huge challenges because of the severe mechanical mismatch between conventional rigid electrodes and soft biological tissues. In recent years, the emergence of flexible and stretchable electrodes offers seamless and conformable biological‐electronic interfaces and has demonstrated significant advantages for in vivo electrochemical and electrophysiological monitoring. This review first summarizes the strategies for electrode fabrication from the point of substrate and conductive materials. Next, recent progress in electrode functionalization for improved performance is presented. Then, the advances of flexible and stretchable electrodes in exploring bioelectrical and biochemical signals are introduced. Finally, we present some challenges and perspectives ranging from electrode fabrication to application. Key Scientists: In 2001, a seminal work by Kipke et al. first showed flexible polyimide‐based intracortical electrode arrays.[1] This electrode was further expanded to 252‐channel using microelectromechanical systems technology by Stieglitz et al. in 2009 and achieved large‐scale cortical recordings.[2] Later, Lieber et al. created mesh electronics that allow for seamless and minimally invasive three‐dimensional interpenetration with nerve tissues, opening up unique applications for flexible electronics.[3] Subsequently, Rogers et al. described bioresorbable electronics for transient electrical activity recordings in 2016.[4] And Frank et al. proposed polymer electrode arrays capable of resolving single neurons in 2019.[5] It wasn't until 2020 that a significant breakthrough in biochemical signals monitoring by Peng et al. demonstrated functionalized carbon nanotube fibre bundles for multiple disease biomarkers monitoring.[6] Later on, Mooney et al. established the first fully viscoelastic electrode arrays for neural recordings from the brain and heart in 2021.[7] Recently, Bao et al. presented tissue‐mimicking, stretchable neurotransmitter interfaces for monitoring the brain and gut.[8] [ABSTRACT FROM AUTHOR]

Comprehensive Summary: In vivo monitoring of bioelectrical and biochemical signals with implanted electrodes has received great interest over the past decades. However, this faces huge challenges because of the severe mechanical mismatch between conventional rigid electrodes and soft biological tissues. In recent years, the emergence of flexible and stretchable electrodes offers seamless and conformable biological‐electronic interfaces and has demonstrated significant advantages for in vivo electrochemical and electrophysiological monitoring. This review first summarizes the strategies for electrode fabrication from the point of substrate and conductive materials. Next, recent progress in electrode functionalization for improved performance is presented. Then, the advances of flexible and stretchable electrodes in exploring bioelectrical and biochemical signals are introduced. Finally, we present some challenges and perspectives ranging from electrode fabrication to application. Key Scientists: In 2001, a seminal work by Kipke et al. first showed flexible polyimide‐based intracortical electrode arrays.[1] This electrode was further expanded to 252‐channel using microelectromechanical systems technology by Stieglitz et al. in 2009 and achieved large‐scale cortical recordings.[2] Later, Lieber et al. created mesh electronics that allow for seamless and minimally invasive three‐dimensional interpenetration with nerve tissues, opening up unique applications for flexible electronics.[3] Subsequently, Rogers et al. described bioresorbable electronics for transient electrical activity recordings in 2016.[4] And Frank et al. proposed polymer electrode arrays capable of resolving single neurons in 2019.[5] It wasn't until 2020 that a significant breakthrough in biochemical signals monitoring by Peng et al. demonstrated functionalized carbon nanotube fibre bundles for multiple disease biomarkers monitoring.[6] Later on, Mooney et al. established the first fully viscoelastic electrode arrays for neural recordings from the brain and heart in 2021.[7] Recently, Bao et al. presented tissue‐mimicking, stretchable neurotransmitter interfaces for monitoring the brain and gut.[8] [ABSTRACT FROM AUTHOR]

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings. [ABSTRACT FROM AUTHOR]

In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this paper, taking the electrophysiological differential equation as an example, the characteristic parameters of ion channel and pump kinetics are determined using BioE-PINN. The results demonstrate that the numerical solution of the differential equation is calculated by the parameters predicted by BioE-PINN, the Root Mean Square Error (RMSE) is between 0.01 and 0.3, and the Pearson coefficient is above 0.87, which verifies the effectiveness and accuracy of BioE-PINN. Moreover, real measured membrane potential data in animals and plants are employed to determine the parameters of the electrophysiological equations, with RMSE 0.02-0.2 and Pearson coefficient above 0.85. In conclusion, this framework can be applied not only for differential equation parameter determination of physiological processes but also the prediction of time-varying parameters of equations in other fields. [ABSTRACT FROM AUTHOR]

Sensing bioelectrical signals is of great significance to understand human disease. Reliable bioelectronic interface is the guarantee of high‐quality bioelectrical signals. The unique electrochemical property and the mixed ionic and electrical conductivity of poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) make it an ideal material for the skin/tissue–electronic interface. However, pristine PEDOT:PSS‐based devices cannot meet the requirements for practical use. Toward this end, herein, the development of PEDOT:PSS‐based electrodes and their most recent advances in sensing bioelectrical signals are summarized. First, the generation mechanism of bioelectrical signals is introduced in detail. Then, according to the characteristics of bioelectrical signals, the requirements of bioelectrodes are discussed. Next, representative achievements for improving conductivity, stretchability, and stability of PEDOT:PSS are introduced. Bioelectrical signals such as electromyogram (EMG), electrocardiogram (ECG), electrooculogram (EOG), and electroencephalogram (EEG) are successfully recorded by these PEDOT:PSS‐based electrodes. Finally, a brief summary is provided, and the opportunities and challenges are also discussed. [ABSTRACT FROM AUTHOR]

Stable adhesion and comfort for long‐term use are the main challenges currently limiting wearable in real‐time health monitoring, especially in disturbed state such as exercise and sweating. Here, A biomimetic microneedles‐based adhesive electrode is designed, which utilizes the mechanical locking of the microneedle array and the adhesion property of the mimic tree frog foot pad microstructures on the rough and wet substrate to synergistically enhance the stable adhesion performance of electrode on the skin surface. This adhesion‐mechanical locking synergistic enhancement effect of the electrode through experiments is verified; the synergistic enhancement mechanism of the electrode is analyzed, and successfully electrocardiograph and electromyogram on human body skin is monitored. The electrode presented here provides a new path to solve the problem of stable signal acquisition under the condition of sweating and exercise, and realizing comfortable, low‐noise, and continuous health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents the process of converting the bioelectrical sEMG signal into an analog signal, which is useful from the point of view of motor or actuator control. The constructed measurement path for signal acquisition enables examining the "raw" signal from sEMG sensors and tracing the entire path of signal processing up to its "useful" form, i.e., the analog signal reflecting the degree of contraction of a selected muscle. This paper presents the successive signal processing and filtering stages and discusses the electrical circuits used in the signal acquisition process. [ABSTRACT FROM AUTHOR]

In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman's model while the accuracy of the Circumplex model is similar to the baseline methods. [ABSTRACT FROM AUTHOR]

Human-machine interface (HMI) techniques use bioelectrical signals to gain real-time synchronised communication between the human body and machine functioning. HMI technology not only provides a real-time control access but also has the ability to control multiple functions at a single instance of time with modest human inputs and increased efficiency. The HMI technologies yield advanced control access on numerous applications such as health monitoring, medical diagnostics, development of prosthetic and assistive devices, automotive and aerospace industry, robotic controls and many more fields. In this paper, various physiological signals, their acquisition and processing techniques along with their respective applications in different HMI technologies have been discussed. [ABSTRACT FROM AUTHOR]

Abstract Background It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5–500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.

An integrated vertical-flow constructed wetland-microbial fuel cell system (CW-MFC), consisting of an up-flow chamber and a down-flow chamber, was constructed to treat synthetic sewage wastewater. The performance of CW-MFCs filled with different substrates [i.e., ceramsite (CM-A), quartz (CM-B), and zeolite (CM-C) granules] under various hydraulic retention times (HRTs, 7.6, 4.0, and 2.8 d) was evaluated. Efficient and stable nitrogen (N) and phosphorus (P) removals were observed in CM-A under different HRTs, while the voltage outputs of the CW-MFCs was greatly reduced as the HRTs decreased. With an HRT of 2.8 d, the ammonium (NH4+-N) and orthophosphate (PO43–-P) removal efficiencies in CM-A were as high as 93.8 and 99.6%, respectively. Bacterial community analysis indicates that the N removal in the cathode area of CM-A could potentially benefit from the appearance of nitrifying bacteria (e.g., Nitrosomonas and Nitrospira) and relatively high abundance of denitrifiers involved in simultaneous nitrification and denitrification (e.g., Hydrogenophaga, Zoogloea, and Dechloromonas) and denitrifying sulfide removal (e.g., Thauera). Additionally, the difference in N removal efficiency among the CW-MFCs could be partly explained by higher iron (Fe) content in milled ceramsite granules and higher abundance of denitrifiers with nitrate reduction and ferrous ions oxidation capabilities in CM-A compared with that in CM-B and CM-C. Efficient PO43–-P removal in CM-A was mainly ascribed to substrate adsorption and denitrifying phosphorus (P) removal. Concerning the substantial purification performance in CM-A, ceramsite granules could be used to improve the nutrient removal efficiency in integrated vertical-flow CW-MFC.

Coordinated contractions and motility patterns unique to each gastrointestinal organ facilitate the digestive process. These motor activities are coordinated by bioelectrical events, sensory and motor nerves, and hormones. The motility problems in the gastrointestinal tract known as functional gastrointestinal disorders (FGIDs) are generally caused by impaired neuromuscular activity and are highly prevalent. Their diagnosis is challenging as symptoms are often vague and difficult to localize. Therefore, the underlying pathophysiological factors remain unknown. However, there is an increasing level of research and clinical evidence suggesting a link between FGIDs and altered bioelectrical activity. In addition, electroceuticals (bioelectrical therapies to treat diseases) have recently gained significant interest. This paper gives an overview of bioelectrical signatures of gastrointestinal organs with normal and/or impaired motility patterns and bioelectrical therapies that have been developed for treating FGIDs. The existing research evidence suggests that bioelectrical activities could potentially help to identify the diverse etiologies of FGIDs and overcome the drawbacks of the current clinically adapted methods. Moreover, electroceuticals could potentially be effective in the treatment of FGIDs and replace the limited existing conventional therapies which often attempt to treat the symptoms rather than the underlying condition. [ABSTRACT FROM AUTHOR]

An integrated vertical-flow constructed wetland-microbial fuel cell system (CW-MFC), consisting of an up-flow chamber and a down-flow chamber, was constructed to treat synthetic sewage wastewater. The performance of CW-MFCs filled with different substrates [i.e., ceramsite (CM-A), quartz (CM-B), and zeolite (CM-C) granules] under various hydraulic retention times (HRTs, 7.6, 4.0, and 2.8 d) was evaluated. Efficient and stable nitrogen (N) and phosphorus (P) removals were observed in CM-A under different HRTs, while the voltage outputs of the CW-MFCs was greatly reduced as the HRTs decreased. With an HRT of 2.8 d, the ammonium (NH4+-N) and orthophosphate (PO43–-P) removal efficiencies in CM-A were as high as 93.8 and 99.6%, respectively. Bacterial community analysis indicates that the N removal in the cathode area of CM-A could potentially benefit from the appearance of nitrifying bacteria (e.g., Nitrosomonas and Nitrospira) and relatively high abundance of denitrifiers involved in simultaneous nitrification and denitrification (e.g., Hydrogenophaga , Zoogloea , and Dechloromonas) and denitrifying sulfide removal (e.g., Thauera). Additionally, the difference in N removal efficiency among the CW-MFCs could be partly explained by higher iron (Fe) content in milled ceramsite granules and higher abundance of denitrifiers with nitrate reduction and ferrous ions oxidation capabilities in CM-A compared with that in CM-B and CM-C. Efficient PO43–-P removal in CM-A was mainly ascribed to substrate adsorption and denitrifying phosphorus (P) removal. Concerning the substantial purification performance in CM-A, ceramsite granules could be used to improve the nutrient removal efficiency in integrated vertical-flow CW-MFC. [ABSTRACT FROM AUTHOR]

[Display omitted] • Liquid metal circuits are prepared based on laser-induced selective metallization. • This stretchable liquid metal circuit has a unique layered structure (LM-Ag-Cu). • Liquid metal reacts with Cu/Ag layers to form CuGa 2 and AgIn 2. • Liquid metal circuits have real-time bioelectrical signal monitoring capabilities. • Liquid metal circuits also exhibit excellent antimicrobial properties. Liquid metal (LM) was generally used to construct flexible wearable electronics to solve the stretchability-conductivity dilemma of traditional electronic devices. Herein, ultra-flexible and stretchable LM circuits with a unique layered structure (LM-Ag-Cu) are prepared by laser-induced selective metallization combined with the highly selective reaction wetting. The Ag-Cu layer is first prepared as templates using the laser-induced selective metallization technology on transparent flexible polymer films, and the LM circuit is obtained by brushing LM onto the Ag-Cu template. The prepared LM circuit demonstrates excellent conductivity and good electrical heating properties even under significant bending and stretching. Moreover, it also exhibits excellent antimicrobial resistance (>99 % antimicrobial activity against E. coli and S. aureus). When designing wearable bioelectrodes, the electrodes based on this LM circuit show accurate and stable real-time monitoring performance of biomechanical (motion detection) and bioelectrical signals (electrocardiography and electromyography signals). Because laser-induced selective metallization technology has characteristics of high-precision preparation of metal circuits, the obtained Ag-Cu layer is accurate and can be reactively infiltrated with LM, so that this new LM-Ag-Cu circuit is born with a layered structure. The LM circuit prepared through our strategy provides a practical and innovative approach to fabricating intelligent skin bioelectrodes. [ABSTRACT FROM AUTHOR]

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Background: It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals.Methods: To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments.Results: To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented.Conclusions: The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5-500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments. [ABSTRACT FROM AUTHOR]

Bioelectrical signals can reflect physiological state of organs or tissues in plants and have a significant potential value in research of plant stress tolerance. In order to study the relationship between environment factors and electrical signals in plant, a portable multi-channel physiological signal acquisition system which relevant in plant physiology research was developed. Environment parameters and electrical signals can be measured in different channels by the acquisition system simultaneously and the measurement data will be displayed in an embedded integrated touch screen which is the system processing core. The system was validated to be stable and reliable after the calibration and repeated experiments of recording electrical signals in Helianthus annuus L.

Coordinated contractions and motility patterns unique to each gastrointestinal organ facilitate the digestive process. These motor activities are coordinated by bioelectrical events, sensory and motor nerves, and hormones. The motility problems in the gastrointestinal tract known as functional gastrointestinal disorders (FGIDs) are generally caused by impaired neuromuscular activity and are highly prevalent. Their diagnosis is challenging as symptoms are often vague and difficult to localize. Therefore, the underlying pathophysiological factors remain unknown. However, there is an increasing level of research and clinical evidence suggesting a link between FGIDs and altered bioelectrical activity. In addition, electroceuticals (bioelectrical therapies to treat diseases) have recently gained significant interest. This paper gives an overview of bioelectrical signatures of gastrointestinal organs with normal and/or impaired motility patterns and bioelectrical therapies that have been developed for treating FGIDs. The existing research evidence suggests that bioelectrical activities could potentially help to identify the diverse etiologies of FGIDs and overcome the drawbacks of the current clinically adapted methods. Moreover, electroceuticals could potentially be effective in the treatment of FGIDs and replace the limited existing conventional therapies which often attempt to treat the symptoms rather than the underlying condition.

The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%.

This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E). [ABSTRACT FROM AUTHOR]

• A unique automatised measurement system for the measurement of contact impedance magnitude and phase is proposed. • Several conductive textile materials in terms of impedance at the artificial electrode–skin interface are investigated. • Best textile materials were further compared to conventional Ag/AgCl electrodes while measuring ECG on living subjects. • Signal quality metrics calculated in the time and frequency domain revealed a high level of similarity between the ECG signals measured by both types of textile electrodes and conventional Ag/AgCl electrodes. Information about the physiological state of the human organism can be obtained by detecting bioelectrical signals. The detection electrodes are a critical point in the measurement chain. The use of textile electrodes is advantageous due to comfort and less stressful measurement of bioelectrical signals for the patient. A unique automatized measurement system was proposed to measure contact impedance magnitude and phase. This research investigated several conductive textile materials in terms of impedance at the artificial electrode–skin interface to select the most appropriate material for ECG signal acquisition. The most suitable samples for further investigation appeared to be polyamide fibre covered with pure silver, without/with antioxidant treatment. These materials were further compared to conventional Ag/AgCl electrodes while measuring ECG on living subjects. Signal quality metrics show a high level of similarity between the ECG signals measured by textile electrodes and conventional Ag/AgCl electrodes. [ABSTRACT FROM AUTHOR]

This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

In this paper we report that electrodes made of multi wall carbon nanotubes (MWCNTs) incorporated on Polyvinylidene fluoride (PVDF) filters can be used for recording and sensing bio-electrical signals. We measured the electrical properties of MWCNTs incorporated on membranes made of cellulose (bond-paper) and PVDF filters. In addition, we performed Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) studies with the purpose to characterize the ultra-structural properties and distribution of MWCNTs on PVDF filters. We found that the electrodes made of MWCNTs on PVDF filters, have lower resistance and consequently better conductivity compared with other typical electro-conductive materials. We also found that the topographic distribution of the MWCNTs on the PVDF filter is chaotic and non-aligned; nevertheless, MWCNTs on PVDF filters showed a linear electrical resistance without rectification. Electrodes made of MWCNTs on PVDF filters were able to record electrocardiograms in humans with similar resolution than the ones recorded with silver electrodes. The relevance and application of these findings are discussed. [ABSTRACT FROM AUTHOR]

The membrane potential of a cell measured by typical electrophysiological methods is only an average magnitude and experimental techniques allowing a more detailed mapping of the cell surface have shown the existence of spatial domains with locally different electric potentials and currents. Electrical potentials in non-neural cells are regulated by the nonlinear conductance of membrane ion channels. Voltage-gated potassium channels participate in cell hyperpolarization/depolarization processes and control the electrical signals over the cell surface, constituting good candidates to study basic biological questions on a more simplified scale than the complex cell membrane. These channels show also a high heterogeneity, making it possible to analyze the effects of diversity in the electrical responses of channels localized on spatial domains. We use a phenomenological approach of voltage gating that reproduces the observed rectification characteristics of inward rectifying potassium channels and relate the threshold voltage heterogeneity of the channels to the establishment of spatial domains with different electrical sensitivities. Although our model is only a limited picture of the whole cell membrane, it shows that domains with different ion channels may permit or suppress steady state bioelectrical signals over the cell surface according to their particular voltage sensitivity. Also, the nonlinear electrical coupling of channels with different threshold potentials can lead to a rich variety of bioelectrical phenomena, including regions of membrane potential bi-stability. [ABSTRACT FROM AUTHOR]

On-line signal processing and feature extraction are limited by sophisticated and highly expensive instrumentation. Many clinical instruments as well as research purpose, such as electrocardiogram, electromyogram and encephalogram instrumentation system neglect certain critical of signal parameters, where most of the time, these parameters are critical to be analyzed in real-time to acquire a fast result. Furthermore, the result using conventional devices is relied to the highly trained person which sometimes could lead to the delayed of medical interpreting diagnostic and cause a high risk to the patient. This paper, the real-time application of bioelectrical signal processing and analysis is developed to assist users to get preliminary diagnosing results. The algorithm of these bioelectrical signal measurement systems is developed, which provide better interpretation of signal parameters in certain conditions. The overall system is comprised of hardware, mainly the bioelectrical circuitry as well as software for analysis, data logging and user interfacing. The developed system prototype will be able to monitor and analysis in real-time condition, which could be used for many applications. [ABSTRACT FROM AUTHOR]

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) in 2016 considered m-health as: "the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health". WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects' data. The best sub-band achieved an equal error rate (EER) of 12.42%. [ABSTRACT FROM AUTHOR]

Flexible electrodes applied to different positions of the human body to detect bioelectrical signals need to be conductive with respect to both the skin and the external circuit. However, electrodes fabricated with micromachining can only be made conductive on one side, which requires inserting connection wires, thus affecting skin adhesion and leading to a more fragile circuit on the soft substrate. This paper proposes an e-interface designed with an innovative folding transfer process, which can fold nanometer thick electrodes in a macroscopic way. Avoiding the difficulties of fabricating double layer as well as an insulation layer, the e-interface is not only skin-conformable for long-term wear but also provides a stable connecting pad for the subsequent circuit by its double-sided conductivity. The e-interface can be stretched to more than 25% of its original length and achieve electrical stability in the long-term signal acquisition. A responsive ECG signal is obtained by the e-interface, and the signal remains stable during exercise.

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions. [ABSTRACT FROM AUTHOR]