Biosignals in the Gut-Brain Axis Transmission: Function and Detection

The gut-brain axis (GBA) is an important information pathway connecting the brain, the central nervous system (CNS), and the gastrointestinal (GI) tract. On the one hand, gut microbiota can influence the function brain through GBA; on the other hand, the brain can also change the structural composition of gut microbiota via GBA. It contains a myriad of biosignals, such as monoamines, inflammatory cytokines, and macro-biomolecules, as the information carriers. Highly selective, sensitive, and reliable sensing techniques are essential to resolve the specific function of individual biosignals. This review summarizes the widely reported biosignals related to GBA and their functions, and organizes the latest sensing tools to provide feasible characterization ideas for GBA-related work. In addition, these low-cost, fast-responding sensors can also be used for early identification and diagnosis of GBA-related diseases (e.g., depression). Finally, the problems and deficiencies in this field are pointed out to provide a reference for the orientation of researchers in the sensing field.

NeuroBioSense: A multidimensional dataset for neuromarketing analysis

In the context of neuromarketing, sales, and branding, the investigation of consumer decision-making processes presents complex and intriguing challenges. Consideration of the effects of multicultural influences and societal conditions from a global perspective enriches this multifaceted field. The application of neuroscience tools and techniques to international marketing and consumer behavior is an emerging interdisciplinary field that seeks to understand the cognitive processes, reactions, and selection mechanisms of consumers within the context of branding and sales. The NeuroBioSense dataset was prepared to analyze and classify consumer responses. This dataset includes physiological signals, facial images of the participants while watching the advertisements, and demographic information. The primary objective of the data collection process is to record and analyze the responses of human subjects to these signals during a carefully designed experiment consisting of three distinct phases, each of which features a different form of branding advertisement. Physiological signals were collected with the Empatica e4 wearable sensor device, considering non-invasive body photoplethysmography (PPG), electrodermal activity (EDA), and body temperature sensors. A total of 58 participants, aged between 18 and 70, were divided into three different groups, and data were collected. Advertisements prepared in the categories of cosmetics for 18 participants, food for 20 participants, and cars for 20 participants were watched. On the emotion evaluation scale, 7 different emotion classes are given: Joy, Surprise, anger, disgust, sadness, fear, and neutral. This dataset will help researchers analyse responses, understand and develop emotion classification studies, the relationship between consumers and advertising, and neuromarketing methods.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity

BACKGROUND

/Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening.

METHODS

An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data.

RESULTS

Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants.

CONCLUSIONS

The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

Investigating the effects of stress on achievement: BIOSTRESS dataset

The effects of chronic stress on academic and professional achievement can have a substantial impact. This relationship is highlighted through a dataset that includes questionnaires and physiological data from a group of individuals. The questionnaire data of 48 individuals, the physiological data of 20 individuals during sessions with a psychologist, and the exam data of 8 individuals were analyzed. The questionnaire data collected includes demographic information and scores on the TOAD stress scale. Physiological data was captured using the Empatica e4, a wearable device, which measured various signals such as blood volume pulse, electrodermal activity, body temperature, interbeat intervals, heart rate, and 3-axis accelerometer data. These measurements were taken under different stress conditions, both high and low, during therapy sessions and an exam respectively. Overall, this study significantly contributes to our understanding of how stress affects achievement. By providing a large dataset consisting of questionnaires and physiological data, this research helps researchers gain a better understanding of the complex relationship between stress and achievement. It also enables them to develop innovative strategies for managing stress and enhancing academic and professional success.

On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review

Aim

Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design considerations for wearable devices for early detection of health conditions.

Subject and methods

This article provides designers with information to identify and develop smart wearable devices based on the data extracted from a literature survey on previously published research articles in the field of wearable devices for monitoring vital parameters.

Results

The key information available in this article indicates that quality signal acquisition, processing and longtime monitoring of vital parameters requires smart wearable devices. The development of smart wearable devices with the listed design criteria supports the developer to design a low power wearable device for continuous monitoring of patient health conditions.

Conclusion

The wide range of information gathered from the review indicates that there is a huge demand for smart wearable devices for monitoring health conditions at home. It further supports tracking heath status in the long term via monitoring the vital parameters with the support of wireless communication principles.

Automatic assessment of pain based on deep learning methods: A systematic review

BACKGROUND AND OBJECTIVE

The automatic assessment of pain is vital in designing optimal pain management interventions focused on reducing suffering and preventing the functional decline of patients. In recent years, there has been a surge in the adoption of deep learning algorithms by researchers attempting to encode the multidimensional nature of pain into meaningful features. This systematic review aims to discuss the models, the methods, and the types of data employed in establishing the foundation of a deep learning-based automatic pain assessment system.

METHODS

The systematic review was conducted by identifying original studies searching digital libraries, namely Scopus, IEEE Xplore, and ACM Digital Library. Inclusion and exclusion criteria were applied to retrieve and select those of interest, published until December 2021.

RESULTS

A total of one hundred and ten publications were identified and categorized by the number of information channels used (unimodal versus multimodal approaches) and whether the temporal dimension was also used.

CONCLUSIONS

This review demonstrates the importance of multimodal approaches for automatic pain estimation, especially in clinical settings, and also reveals that significant improvements are observed when the temporal exploitation of modalities is included. It provides suggestions regarding better-performing deep architectures and learning methods. Also, it provides suggestions for adopting robust evaluation protocols and interpretation methods to provide objective and comprehensible results. Furthermore, the review presents the limitations of the available pain databases for optimally supporting deep learning model development, validation, and application as decision-support tools in real-life scenarios.

sEMG-based technology for silent voice recognition

Silent speech recognition (SSR) is a system that implements speech communication when a sound signal is not available using surface electromyography (sEMG)-based speech recognition. Researchers have used surface electrodes to record the electrically-activated potential of human articulation muscles to recognize speech content. SSR can be used for pilot-assisted speech recognition, communication of individuals with speech impairment, private communication, and other fields. In this feasibility study, we collected sEMG data for ten single Mandarin numeric words. After reducing power frequency interference and power supply noise from the sEMG signal, short-term energy (STE) was used for voice activity detection (VAD). The power spectrum features were extracted and fed into the classifier for final identification results. We used the Hold-out method to divide the data into training and test sets on a 7-3 scale, with an average accuracy of 92.3% and a maximum of 100% using a support vector machine (SVM) classifier. Experimental results showed that the proposed method has development potential, and is effective in identifying isolated words from the sEMG signal of the articulation muscles.

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review

BACKGROUND

Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field.

METHOD

A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review.

RESULT

From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field.

CONCLUSION

Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

The Internet of Bodies-alive, connected and collective: the virtual physical future of our bodies and our senses

This paper is going to discuss, what will be called, 'The Internet of Bodies'. Our physical and virtual worlds are blending and shifting our understanding of three key areas: (1) our identities are diversifying, as they become hyper-enhanced and multi-sensory; (2) our collaborations are co-created, immersive and connected; (3) our innovations are diverse and inclusive. It is proposed that our bodies have finally become the interface.

NeuroKit2: A Python toolbox for neurophysiological signal processing

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.

Bimodal affect recognition based on autoregressive hidden Markov models from physiological signals

BACKGROUND AND OBJECTIVE

Affect provides contextual information about the emotional state of a person as he/she communicates in both verbal and/or non-verbal forms. While human's are great at determining the emotional state of people while they communicate in person, it is challenging and still largely an unsolved problem to computationally determine the emotional state of a person.

METHODS

Emotional states of a person manifest in the physiological biosignals such as electrocardiogram (ECG) and electrodermal activity (EDA) because these signals are impacted by the peripheral nervous system of the body, and the peripheral nervous system is strongly coupled with the mental state of the person. In this paper, we present a method to accurately recognize six emotions using ECG and EDA signals and applying autoregressive hidden Markov models (AR-HMMs) and heart rate variability analysis on these signals. The six emotions include happiness, sadness, surprise, fear, anger, and disgust.

RESULTS

We evaluated our method on a comprehensive new dataset collected from 30 participants. Our results show that our proposed method achieves an average accuracy of 88.6% in distinguishing across the 6 emotions.

CONCLUSIONS

The key technical depth of the paper is in the use of the AR-HMMs to model the EDA signal and the use of LDA to enable accurate emotion recognition without requiring a large number of training samples. Unlike other studies, we have taken a hierarchical approach to classify emotions, where we first categorize the emotion as either positive or negative and then identify the exact emotion.

Knowledge Discovery on IoT-Enabled mHealth Applications

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Design of an optimum Poincaré plane for extracting meaningful samples from EEG signals

Biosignals are considered as important sources of data for diagnosing and detecting abnormalities, and modeling dynamics in the body. These signals are usually analyzed using features taken from time and frequency domain. In theory' these dynamics can also be analyzed utilizing Poincaré plane that intersects system's trajectory. However' selecting an appropriate Poincaré plane is a crucial part of extracting best Poincaré samples. There is no unique way to choose a Poincaré plane' because it is highly dependent to the system dynamics. In this study, a new algorithm is introduced that automatically selects an optimum Poincaré plane able to transfer maximum information from EEG time series to a set of Poincaré samples. In this algorithm' EEG time series are first embedded; then a parametric Poincaré plane is designed and finally the parameters of the plane are optimized using genetic algorithm. The presented algorithm is tested on EEG signals and the optimum Poincaré plane is obtained with more than 99% data information transferred. Results are compared with some typical method of creating Poinare samples and showed that the transferred information using with this method is higher. The generated samples can be used for feature extraction and further analysis.

An enhanced method for real-time modelling of cardiac related biosignals using Gaussian mixtures

Cardiac related biosignals modelling is very important for detecting, classification, compression and transmission of such health-related signals. This paper introduces a new, fast and accurate method for modelling the cardiac related biosignals (ECG and PPG) based on a mixture of Gaussian waves. For any signal, at first, the start and end of the ECG beat or PPG pulse is detected, then the baseline is detected then subtracted from the original signal, after that the signal is divided into two signals positive and negative, each modelled separately then incorporated together to form the modelled signal. The proposed method is applied in the MIMIC, and MIT-BIH Arrhythmia databases available online at PhysioNet.

Biosignals learning and synthesis using deep neural networks

BACKGROUND

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.

METHOD

The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself.

RESULTS AND CONCLUSIONS

The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

A software framework for real-time multi-modal detection of microsleeps

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

Modulation of human multipotent and pluripotent stem cells using surface nanotopographies and surface-immobilised bioactive signals: A review

The ability to control the interactions of stem cells with synthetic surfaces is proving to be effective and essential for the quality of passaged stem cells and ultimately the success of regenerative medicine. The stem cell niche is crucial for stem cell self-renewal and differentiation. Thus, mimicking the stem cell niche, and here in particular the extracellular matrix (ECM), in vitro is an important goal for the expansion of stem cells and their applications. Here, surface nanotopographies and surface-immobilised biosignals have been identified as major factors that control stem cell responses. The development of tailored surfaces having an optimum nanotopography and displaying suitable biosignals is proposed to be essential for future stem cell culture, cell therapy and regenerative medicine applications. While early research in the field has been restricted by the limited availability of micro- and nanofabrication techniques, new approaches involving the use of advanced fabrication and surface immobilisation methods are starting to emerge. In addition, new cell types such as induced pluripotent stem cells (iPSCs) have become available in the last decade, but have not been fully understood. This review summarises significant advances in the area and focuses on the approaches that are aimed at controlling the behavior of human stem cells including maintenance of their self-renewal ability and improvement of their lineage commitment using nanotopographies and biosignals. More specifically, we discuss developments in biointerface science that are an important driving force for new biomedical materials and advances in bioengineering aiming at improving stem cell culture protocols and 3D scaffolds for clinical applications. Cellular responses revolve around the interplay between the surface properties of the cell culture substrate and the biomolecular composition of the cell culture medium. Determination of the precise role played by each factor, as well as the synergistic effects amongst the factors, all of which influence stem cell responses is essential for future developments. This review provides an overview of the current state-of-the-art in the design of complex material surfaces aimed at being the next generation of tools tailored for applications in cell culture and regenerative medicine.

STATEMENT OF SIGNIFICANCE

This review focuses on the effect of surface nanotopographies and surface-bound biosignals on human stem cells. Recently, stem cell research attracts much attention especially the induced pluripotent stem cells (iPSCs) and direct lineage reprogramming. The fast advance of stem cell research benefits disease treatment and cell therapy. On the other hand, surface property of cell adhered materials has been demonstrated very important for in vitro cell culture and regenerative medicine. Modulation of cell behavior using surfaces is costeffective and more defined. Thus, we summarise the recent progress of modulation of human stem cells using surface science. We believe that this review will capture a broad audience interested in topographical and chemical patterning aimed at understanding complex cellular responses to biomaterials.

BIT: Biosignal Igniter Toolkit

The study of biosignals has had a transforming role in multiple aspects of our society, which go well beyond the health sciences domains to which they were traditionally associated with. While biomedical engineering is a classical discipline where the topic is amply covered, today biosignals are a matter of interest for students, researchers and hobbyists in areas including computer science, informatics, electrical engineering, among others. Regardless of the context, the use of biosignals in experimental activities and practical projects is heavily bounded by the cost, and limited access to adequate support materials. In this paper we present an accessible, albeit versatile toolkit, composed of low-cost hardware and software, which was created to reinforce the engagement of different people in the field of biosignals. The hardware consists of a modular wireless biosignal acquisition system that can be used to support classroom activities, interface with other devices, or perform rapid prototyping of end-user applications. The software comprehends a set of programming APIs, a biosignal processing toolbox, and a framework for real time data acquisition and postprocessing.