Field Of Biomedical Engineering Research Articles

Precise processing of pure undiluted PEGDA via inkjet printing for drug release systems

Abstract Poly(ethylene glycol) diacrylate (PEGDA) is a common polymer in the field of biomedical engineering and can be used for the production of drug delivery systems (DDS). The main advantages of PEGDA are biocompatibility and the ability to alter the physical and chemical properties, thereby ensuring individualized drug release behaviour. The processing of PEGDA via inkjet printing is relevant for the production of DDS. This can be challenging due to the high viscosity of pure PEGDA. In this work, PEGDA, with a molecular weight of 250 g/mol (PEGDA250), was inkjet printed using a Nanoplotter 2.1 with a piezoelectric heatable NanoTip HV-J-H printhead (GeSiM mbH, Radeberg, Germany) at different voltages and temperatures. Droplet generation was analysed in terms of droplet volume and angle deviations. PEGDA250 can be inkjet printed reproducibly in a voltage range of 60 V - 80 V at room temperature (20 °C) or heated up to 38 °C. The average volume of heated (38 °C) PEGDA250 droplets was approximately 110 pl - 150 pl higher than the droplet volume of PEGDA250 in the unheated state. The average angle deviations of main and satellite droplets were mostly < 3°. Increasing voltage or excessive heating of more than 38 °C caused greater instabilities in the droplet generation as well as larger satellite droplets which can affect the accuracy negatively. The studies have shown that PEGDA250 can be processed via inkjet printing and thus can be used as a drug carrier for DDS without the need for mixing with a solvent.

Stomatocyte-discocyte-echinocyte transformations of erythrocyte modulated by membrane-cytoskeleton mechanical properties.

Stomatocyte-discocyte-echinocyte (SDE) transformations in human red blood cells (RBCs) have significant influences on blood dynamics and related disorders. The mechanical properties of the RBC membrane, such as shear modulus and bending elasticity, play crucial roles in determining RBC shapes. Recent biophysical findings reveal that building a comprehensive model capable of describing SDE shape transformations is a challenging problem. Based on dissipative particle dynamics, this study develops a two-component RBC model considering the detachment between the lipid bilayer and cytoskeleton, as well as the cytoskeletal reorganization during echinocyte formation. This model is validated by comparing RBCs' geometric shape and the apparent membrane tension with previous experimental measurements. Results indicate that a complete SDE sequence represented by six typical shapes can be obtained by modulating the model's mechanical and geometric parameters. Furthermore, a phase diagram based on reduced variables is obtained using principal-component analysis, demonstrating the phase transformations among SDE shapes. Our result suggests that the transformation from discocyte to stomatocyte is primarily influenced by dimensionless bending rigidity, whereas, during echinocyte formation, three key variables, i.e., dimensionless bending rigidity, targeting cytoskeleton shrinkage ratio, and connecting pattern, have joint impacts on the formation of spicules or bumps and the development of the cytoskeletal framework. The present two-component RBC model and the associated findings provide a perspective for a deeper understanding of the SDE transformation mechanism. This framework offers new insights into biological science and potential applications in the field of biomedical engineering.

A novel hydrodynamic semiconductor model under Hall current effect and laser pulsed excitation

This paper presents a novel investigation into the effects of a strong external magnetic field on a hydro-poroelastic semiconductor model within the framework of photo-thermoelasticity theory in two dimensions. This study focuses on generating a Hall current and its impact on the coupled behavior of thermal, mechanical, and electronic fields in a semiconductor medium saturated with fluid. Using the normal mode analysis, we derive and analyze the wave propagation characteristics of key physical fields, including the non-dimensional temperature, displacement, mechanical stresses, carrier density, and excess pore water pressure, in response to the imposed magnetic field. Boundary conditions relevant to real-world applications are incorporated to assess the interactions between these fields. The results provide insight into the dynamic coupling of electromagnetic, thermal, and mechanical phenomena in porous semiconductor materials and offer potential applications in the design of magneto-sensitive semiconductor devices as well as in geophysical and biomedical engineering fields where such multi-field interactions are critical. The results obtained are plotted with some comparisons.

Implications of compressive loading of the stomach wall: Interplay between mechanical deformation and microstructure.

During gastric surgery, the stomach wall is compressed with clamps and sutures or staple lines. These short- and long-term deformations can severely compromise the integrity of the tissue and make it difficult for the stomach wall to respond and remodel to the new loading conditions. Consequently, serious intra- and postoperative complications such as the formation of leaks during bariatric surgeries, can be associated with these immense tissue deformations. Hence, the study aimed to investigate the effects of compressive loading of the stomach wall in the radial direction. This was done by macroscopic mechanical loading of the stomach wall in each region of the stomach and evaluating the microstructural changes inflicted in the tissue. For this purpose, several imaging techniques were used, i.e., a histological analysis, second-harmonic generation microscopy, and X-ray micro-computed tomography. The combination of these three methods allowed us to investigate the gradual compression of the different stomach layers as well as the local reorientation and deformation of the main microstructural components, e.g., collagen fibers and muscle bundles. Importantly, this study found that the collagen bundles in the stomach wall straighten and reorient toward the circumferential-longitudinal plane and partially fan out with increased radial compressive deformation. The 3D scans of the stomach wall indicated a deterioration of the blood vessels and buckling of the mucosal glands due to compression. Statement of significance Unfortunately, little is known about the load transfer in the stomach wall during gastric surgery and the associated deformations on the macro- and microscale. The present study investigates the structural changes of the stomach wall, its layers and the inherent biological building blocks using histology, multi-photon microscopy, and micro-computed tomography. For the first time, the layer-specific response to stepwise radial compression of the stomach wall was studied, the related collagen fiber parameters were estimated, and a 3D sample structure was visualized. This clinically-oriented study links the structural changes within the wall to the postoperative remodel- ing process and the irreversibly altered gastric motility, thereby underscoring its relevance to the field of biomedical engineering, e.g., the development and improvement of surgical instruments.

Artificial Intelligence Integration in Biochips: Enhancing Diagnostics and Precision Medicine

Abstract: The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi- functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

Magnetohydrodynamic micropolar nanofluid flow with pulsation in a vertical permeable channel with Brownian motion, thermophoresis, and thermal radiation: a Buongiorno model approach

Abstract The current paper investigates the magnetohydrodynamic pulsating flow of micropolar nanofluid in a vertical porous channel by using Buongiorno’s nanofluids approach and utilizing entropy generation. The effects of thermal radiation, viscous dissipation, Brownian motion, and thermophoresis are considered. This finding holds implications for the fields of biomedical engineering, food processing systems, cancer treatment, pressure spikes, and nano-drug delivery in arteries. In this case, the
governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) using the perturbation approach and we used Runge-Kutta fourth order method for the numerical approach. Graphical representations illustrate the
effects of nanoparticle concentration, velocity, temperature, and micro-rotation across various standard physical parameters. Furthermore, analysis and tabular presentation of the heat and mass transfer rates are provided. The data obtained indicate
that while raising the Hartmann number and coupling parameter decreases velocity profiles then increasing the Grashof number enhances velocity. The temperature of a micropolar nanofluid rises with increased Brownian motion and thermophoresis,
and falls with increased magnetic field and thermal radiation. The concentration of micropolar nanofluid is reducing by enhancing the thermophoresis and chemical reaction parameter. The rate of heat transfer is increasing as a result of increases
in thermal radiation, viscous dissipation, thermophoresis, and Brownian motion values. The heat transfer rate rises with increase values of the viscous dissipation, thermal radiation, the Brownian motion, and the thermophoresis parameters. This
study is useful for various real life applications like biological systems, biotechnology, pharmaceutical medicine, nano-drug development, and engineering industries.

The effect of alkaline heat treatment on titanium

The effect of alkaline heat treatment on titanium

Design Strategy, On-Demand Control, and Biomedical Engineering Applications of Wet Adhesion.

The adhesion of tissues to external devices is fundamental to numerous critical applications in biomedical engineering, including tissue and organ repair, bioelectronic interfaces, adhesive robotics, wearable electronics, biomedical sensing and actuation, as well as medical monitoring, treatment, and healthcare. A key challenge in this context is that tissues are typically situated in aqueous and dynamic environments, which poses a bottleneck to further advancements in these fields. Wet adhesion technology (WAT) presents an effective solution to this issue. In this review, we summarize the three major design strategies and control methods of wet adhesion, comprehensively and systematically introducing the latest applications and advancements of WAT in the field of biomedical engineering. First, single adhesion mechanism under the frameworks of the three design strategies is systematically introduced. Second, control methods for adhesion are comprehensively summarized, including spatiotemporal control, detachment control, and reversible adhesion control. Third, a systematic summary and discussion of the latest applications of WAT in biomedical engineering research and education were presented, with a particular focus on innovative applications such as tissue-electronic interface devices, ingestible devices, end-effector components, in vivo medical microrobots, and medical instruments and equipment. Finally, opportunities and challenges encountered in the design and development of wet adhesives with advanced adhesive performance and application prospects are discussed.

An investigation of the surface topography of wire tool and the modelling of wire electro-spark machined Ti-alloy using ANN

Abstract Titanium and its alloys are in demand in the field of biomedical and aerospace engineering because of its high strength-to-weight ratio and anti-corrosive properties. WESM emerged as a processing method as traditional methods could not process because of strain hardening and low material conductivity; however, wire tool consumption contributes to high costs while processing with WESM. The current Article investigates the effect of input factors on wire consumption and their significance level on wire consumption. 81 experimental runs were conducted while taking input factors Spark on, Spark off time, peak current and servo voltage at three levels, each using a full factorial approach. Experimental results were analysed using ANOVA and MLR (Multi-Linear regression). Moreover, ANN modelling is done to model the wire consumption using a 4-10-1 architecture that increased the coefficient of correlation (R2) up to 0.991. Lastly, after processing, the wire tool surface is evaluated using a scanning electron microscope to assess the effect of various input energy levels and find input combinations for minimum wire consumption.

Exact solutions of a class of generalized nanofluidic models

Abstract Nanofluid, a significant branch of fluid mechanics, plays a pivotal role in thermal management, optics, biomedical engineering, energy harvesting, and other fields. The nanoparticles present in the fluid render the continuum mechanics ineffective, necessitating the adoption of fractional calculus to elucidate the effects of nanoparticles on the motion properties of the nanofluid. This article applies the modified extended tanh-function technique to solve two classical Schrödinger equations, the fractional Phi-4 model and the conformable fractional Boussinesq model, for nanofluids. Multiple exact solutions are obtained, and the corresponding graphical representations are provided to elucidate the basic properties of the nanofluid. This article provides new research perspectives for the development of nanofluids.

Biomedical Engineering Literature: Advanced Reading Skills for Research and Practice

Medical engineering is a field of study that combines biological and medical sciences with engineering to develop improved healthcare technologies. It is imperative that you possess a deep understanding of both the material and the technical language used in this rapidly advancing field in order to effectively grasp key topics and navigate the rapidly expanding literature. This paper examines the future of the biomedical engineering profession through the lens of advanced reading strategies. It starts with the assertion that biomedical engineering literature presents three challenges: the level of density is technical, the area is interdisciplinary, and the growth of knowledge is exponential. In the view of the above challenges, the paper discusses various advanced reading skills in relation to biomedical engineers. These skills include the ability to critically read scientific texts, both at a fast pace for an overview and in detail for deeper understanding, to identify key ideas and methodologies relevant to the field, and to assess the validity of scientific claims and data representations. The paper also aims at pointing out how separate elements like the use of systematic reviews, of meta-analysis, of data visualization tools that complement reading, help the researcher in the process of managing gathered information from various sources. This paper describes teaching these skills for both academic and professional practice, with examples of how improved reading competencies can enhance research quality and contribute to innovations in medical technologies, tissue engineering, and clinical trials. Furthermore, the paper aims to discuss the future trends due to the increasing incorporation of continuous learning for addressing the challenges posed by novel technologies including artificial intelligence and nanotechnology that define the future of biomedical engineering. biomedical engineering students require the acquisition of higher order reading skills in preparation for information reading: national and international publications in the engineering profession, policy and technology in support of health care delivery systems, product development, and other purposes. The ideas stated and demonstrated in this paper should be practical to both beginning scholars and veteran professionals in the biomedical engineering field as well as useful for seminar and workshop participants in seminars and workshops on biomedical engineering literature.

Enhancing biocompatibility and degradation control of biodegradable polymers through amino acid grafting: A study on 4arm-PLGA-Amino acid copolymers

Non-degradable polymers, when retained in the body for long periods, can cause chronic inflammation and immune responses. This issue increases the costs related to secondary surgeries and overall healthcare. Conversely, while biodegradable polymers such as PLGA offer an alternative, their degradation rate is challenging to precisely control. Additionally, their mechanical properties deteriorate during the degradation process, and the degradation by-products may lead to localized acidification or other adverse reactions. In this study, we introduce a new biodegradable copolymer called 4arm-PLGA-Amino acid (4aPLGA-Amino acid). This copolymer is synthesized by grafting amino acids onto 4arm-PLGA (4aPLGA) using triphosgene, which is both environmentally friendly and cost-effective. The incorporation of amino acids aims to enhance the biocompatibility and functionality of the copolymer. By varying the types of amino acids grafted, the degradation rate of 4aPLGA can be modulated, thereby improving its potential for applications in drug delivery systems. This modification also promotes cell attachment and proliferation, making it suitable for tissue engineering and regenerative medicine. The study demonstrated that 4aPLGA-Amino acid significantly improved biocompatibility, degradation control, and mechanical properties, broadening its applicability in various biomedical engineering fields.

Solidification of Polyurethane Model Foams via Catalyst Drainage from a Secondary Foam.

Due to their unique mechanical and thermal properties, polyurethane foams are widely used in multiple fields of applications, including cushioning, thermal insulation or biomedical engineering. However, the way polyurethane foams are usually manufactured - via chemical foaming - produces samples where blowing and gelling occur at the same time, resulting in a morphology control achieved by trial and error processes. Here, a novel strategy is introduced to build model homogeneous polyurethane foams of controlled density with millimetric bubbles from liquid templates. By producing a polyurethane foam via physical bubbling without a catalyst and gently depositing a secondary foam containing catalyst on the top of this first foam, it is possible to take advantage of drainage mechanisms to trigger the solidification of the bottom foam. The characterization of the samples performed by X-ray microtomography allows to study quantitatively the structure of the final solid foam, at the global and at the local scale. Using the tomographic 3D images of the foam architectures, the superimposed foam technique introduced in this article is shown to be promising to produce foams with a good homogeneity along the vertical direction, with a density controlled by varying the concentration of catalyst in the secondaryfoam.

Experimental investigation on tailoring compressive properties and energy absorption of 3D printed gradient star re-entrant hybrid auxetic structure

Purpose Fused filament fabrication (FFF) is a widely used 3D printing technique for the fabrication of mechanical metamaterials with intricate geometries. Gradient strategy is applied to geometric parameters of gradient star re-entrant hybrid auxetic (GSRA) structure. Deformation behaviour is studied under compressive loading. The purpose of this study is to investigate the influence of gradient geometric parameters on mechanical properties, namely, specific strength (SS), specific modulus (SM) and specific energy absorption (SEA). Design/methodology/approach Response surface methodology (RSM) is implemented for the design of experiments of gradient geometric parameters to minimize the number of experimental tests. Acrylonitrile butadiene styrene material is used for the fabrication of GSRA structures by FFF technique. The best set of gradient parameters has been optimized maximizing all three responses using RSM and artificial neural network optimization technique. Findings During compressive testing, row-wise deformation is observed with two-stage plateau regions, which results in increase in SEA of the structure. Furthermore, based on analysis of variance and 3D response plots, it is found that height gradient is the most influencing gradient geometric parameter on SS and SM, whereas the wall thickness gradient has maximum influence on SEA. Meanwhile, the interaction effect of wall thickness gradient and height gradient has maximum influence on SS, SM and SEA. Research limitations/implications This study of applying gradient strategy to geometric parameters is limited to GSRA structure under compressive loading. In addition, findings are valid within the selected range of gradient geometric parameters. These findings are useful for the selection of gradient geometric parameters to maximize SS, SM and SEA of GSRA structure simultaneously. These outcomes pave the way for designing light-weight gradient hybrid auxetic structures in the field of construction, aerospace, automobile and biomedical engineering. Originality/value Limited experimental study is available on investigating the influence of gradient geometric parameters on mechanical properties, namely, SS, SM and SEA, and deformation behaviours of hybrid auxetic structures. This study directly addresses the above research gaps.

Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features

This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

Zwitterionic Polymers for Biomedical Applications: Antimicrobial and Antifouling Strategies toward Implantable Medical Devices and Drug Delivery.

Poly(ethylene glycol) (PEG) is extensively utilized in biomedical applications due to its biocompatibility; however, its thermal instability and susceptibility to oxidative degradation significantly constrain its long-term effectiveness. Zwitterionic polymers, characterized by their distinctive structure, enhanced stability, and superior biocompatibility, offer a more advantageous alternative. These polymers exhibit super hydrophilicity, resist nonspecific protein adsorption, and maintain stability in biological environments due to their charge-neutral ionic nature. Zwitterionic polymers enhance anticancer drug delivery by precisely targeting tumor cells and facilitating an efficient drug release. Their inherent antifouling properties and prolonged circulation within the bloodstream render them highly suitable for redox-sensitive drug carriers, thereby augmenting the antitumor efficacy. Moreover, zwitterionic polymers markedly mitigate biofouling in implants, biosensors, and wound dressings, thereby improving both their functionality and their therapeutic outcomes. These advantages arise from the formation of robust hydration layers, which significantly enhance the hemocompatibility and inhibit the adhesion of proteins, platelets, and bacteria. Zwitterionic polymers, including sulfobetaine (SB), phosphorylcholine (PC), and carboxybetaine (CB), are increasingly employed in blood-contacting devices and as effective coating materials for implantable devices. This mini-review paper aims to explore the recent diverse biomedical applications of zwitterionic polymers and highlight their advantageous properties compared with unmodified polymers. We will cover their use in drug delivery systems, tumor targeting nanocarriers, antibiofouling and antibacterial activities in implantable devices, tissue engineering, and diagnostic devices, demonstrating how their unique properties can translate into different applications. Through this exploration, this Perspective will display the potential of zwitterionic polymers as innovative polymer materials in the field of biomedical engineering and beyond.

Genetically Engineered Filamentous Bacteriophages Displaying TGF-β1 Promote Angiogenesis in 3D Microenvironments.

Combined 3D cell culture in vitro assays with microenvironment-mimicking systems are effective for cell-based screening tests of drug and chemical toxicity. Filamentous bacteriophages have diverse applications in material science, drug delivery, tissue engineering, energy, and biosensor development. Specifically, genetically modified bacteriophages have the potential to deliver therapeutic molecules or genes to targeted tumor tissues. The engineered bacteriophages in this study significantly enhanced endothelial cell migration and tube formation within the extracellular matrix (ECM). Compared to TGF-β1 alone and non-modified phages, the presence of TGF-β1 on the bacteriophages demonstrated superior performance as a continuous stimulant in the microenvironment, effectively promoting these angiogenic processes. Assays, including RT-qPCR, ELISA, and fluorescence microscopy, confirmed the expression of angiogenic markers such as CD31, validating the formation of 3D angiogenic structures. Our findings indicate that the TGF-β1 displayed by bacteriophages likely acted as a chemotactic factor, promoting the migration, proliferation, and tube formation of endothelial cells (ECs) within the ECM. Although direct contact between ECs and bacteriophages was not explicitly confirmed, the observed effects strongly suggest that TGF-β1-RGD bacteriophages contributed to the stimulation of angiogenic processes. The formation of angiogenic structures by ECs in the ECM was confirmed as three-dimensional and regulated by the surface treatment of microfluidic channels. These results suggest that biocompatible TGF-β1-displaying bacteriophages could continuously stimulate the microenvironment in vitro for angiogenesis models. Furthermore, we demonstrated that these functionalized bacteriophages have the potential to be utilized as versatile biomaterials in the field of biomedical engineering. Similar strategies could be applied to develop angiogenic matrices for tissue engineering in in vitro assays.

Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

In the field of biomedical engineering, predicting Drug-Target Affinities (DTA) is crucial. However, current affinity prediction models are predominantly manually designed, which is a complex, time-consuming process that may not effectively accommodate the diversity and complexity of datasets. To address this challenge, we propose an adaptive learning framework for predicting drug-target affinity, called Adaptive-DTA, which integrates reinforcement learning with graph neural networks to automate the design of affinity prediction models. Adaptive-DTA defines the architecture search space using directed acyclic graphs and employs an reinforcement learning algorithm to guide the architecture search, optimizing parameters based on the entropy of sampled architectures and model performance metrics. Additionally, we enhance efficiency with a two-stage training and validation strategy, incorporating low-fidelity and high-fidelity evaluations. Our framework not only alleviates the challenges associated with manual model design but also significantly improves model performance and generalization. To evaluate the performance of our method, we conducted extensive experiments on DTA benchmark datasets and compared the results with nine state-of-the-art methods. The experimental outcomes demonstrate that our proposed framework outperforms these methods, exhibiting outstanding performance in predicting drug-target affinities. Our innovative approach streamlines the design of affinity prediction model, reduces reliance on manual crafting, and enhances model generalization. Its ability to automatically optimize network architectures represents a major step forward in the automation of computational drug discovery processes.

Magnetic Induction Tomography for Brain Tissue Imaging Based on Conductivity Distribution for Parkinson’s Disease Diagnosis

Parkinson's disease is a prevalent neurodegenerative complication defined by the accumulation of alpha synuclein lewy bodies in the brain. Misdiagnosis results widespread of Parkinson’s disease because clinical diagnosis is challenging, underlining a need of a better detection technique, such as non-invasive magnetic induction tomography (MIT) technique. Non-invasive techniques for biological tissues imaging are becoming popular in biomedical engineering field. Therefore, MIT technology as a non-invasive technique has been encouraged in a medical field due to its advancement of technology in diagnosing diseases. The measurement parameters in MIT are passive electromagnetic properties (conductivity, permittivity, permeability) for biological tissue and the most dominant parameter in MIT is conductivity properties. It is uses a phase shift between a primary magnetic field and an induced field caused by a target object's conductivity. As a function of conductivity, the phase shift between the applied and secondary fields is expressed. Thus, the phase shift can be used to characterize the conductivity of a target object. The phase shift between the excitation and induced magnetic fields (EMF and IMF) reflects the change in conductivity in biological tissues. This paper focuses on the virtual simulation by using COMSOL Multi-physics for the design and development of MIT system that emphasizes on single channel magnetic induction tomography for biological tissue (bran tissue) imaging based on conductivity distribution for Parkinson’s disease diagnosis. The develop system employs the use of excitation coils to induce an electromagnetic field (e.m.f) in the brain tissue, which is then measured at the receiving side by sensors. The proposed system is capable of indicating Parkinson’s disease based on conductivity distribution. This method provides the valuable information of the brain abnormality based on differences of conductivities of normal brain and Parkinson’s disease brain tissues.

Asymmetric measures of polar Chebyshev chaotic map for discrete/dimensional emotion recognition using PPG

Human emotion recognition is a challenge in both medical and non-medical fields. While there are several physiological signals available for detecting emotions, the Photoplethysmogram (PPG) signal has been disregarded, despite its non-invasive nature and ease of recording using common devices like smartphones. This study addresses the research question: How can we effectively analyze PPG signals to enhance emotion recognition? To tackle this question, we employed novel nonlinear measures based on the polar form of the Chebyshev chaotic map to analyze the dynamic behavior of PPG signals. What makes this research unique is the use of the polar form of the Chebyshev chaotic map for biosignal analysis, specifically in quantifying the map’s asymmetry using multiple indices. Our main goal is to improve our understanding of the nonlinear characteristics of PPG signals and gain new insights into their dynamic properties. While previous research has explored PPG-based emotion recognition, our study stands out by utilizing the Chebyshev chaotic map to characterize PPG signals. We evaluated the effectiveness of these features in discrete and dimensional emotion recognition tasks, using the Database for Emotion Analysis using Physiological signals (DEAP) as a benchmark. Our experimental results showed a maximum accuracy of 94.49% for discrete emotion recognition and 85.59% for dimensional emotion recognition. Experiments were also conducted on another public dataset to further validate the efficacy of the proposed approach. The significance of our study lies in providing new insights into the nonlinear characteristics of PPG signals and enhancing emotion recognition capabilities, thereby contributing to both the fields of emotional analysis and biomedical engineering.