Development of metal free carbon catalyst derived from Parthenium hysterophorus for the electrochemical detection of dopamine.

Parthenium hysterophorus, one of the seven most hazardous weeds is widely known for its allergic, respiratory and skin-related disorders. It is also known to affect biodiversity and ecology. For eradication of the weed, its effective utilization for the successful synthesis of carbon-based nanomaterial is a potent management strategy. In this study, reduced graphene oxide (rGO) was synthesized from weed leaf extract through a hydrothermal-assisted carbonization method. The crystallinity and geometry of the as-synthesized nanostructure are confirmed from the X-ray diffraction study, while the chemical architecture of the nanomaterial is ascertained through X-ray photoelectron spectroscopy. The stacking of flat graphene-like layers with a size range of ∼200-300 nm is visualized through high-resolution transmission electron microscopy images. Further, the as-synthesized carbon nanomaterial is advanced as an effective and highly sensitive electrochemical biosensor for dopamine, a vital neurotransmitter of the human brain. Nanomaterial oxidizes dopamine at a much lower potential (0.13 V) than other metal-based nanocomposites. Moreover, the obtained sensitivity (13.75 and 3.31 µA µM-1 cm-2), detection limit (0.6 and 0.8 µM), the limit of quantification (2.2 and 2.7 µM) and reproducibility calculated through cyclic voltammetry/differential pulse voltammetry respectively outcompete many metal-based nanocomposites that were previously used for the sensing of dopamine. This study boosts the research on the metal-free carbon-based nanomaterial derived from waste plant biomass.

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.

Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.

Data Clustering Using Moth-Flame Optimization Algorithm.

A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.

Densely connected convolutional networks-based COVID-19 screening model.

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Normal limits of the electrocardiogram in Indians.

AIMS: The objective of the study was to develop normal limits of the ECG in an apparently healthy population of South Asians living in India. METHODS: Three centres contributed to recording 12 lead ECGs on identical digital electrocardiographs. Apparently healthy volunteers were recruited and ECGs were first transferred to a local database and then to Glasgow where all ECGs were analysed by the same University of Glasgow ECG Interpretation Program. RESULTS: A total of 963 individuals were recruited into the study (30.4% female) with an age range of 18-83 years. QRS duration was longer in males than females, QT interval was longer in females than males, and QRS voltages in general were higher in males than females and in younger compared to older individuals. CONCLUSION: Findings in general paralleled those in other populations and suggested that criteria for a white Caucasian population could be applied to a South Asian Indian population.

Heterogenization of Ionic Liquid on Multiwalled Carbon Nanotubes for Lead(II) Ion Detection.

The presence of lead(II) ion poses a significant threat to water systems due to their toxicity and potential health hazards. The detection of Pb2+ ions in contaminated water is very crucial. The ionic liquid functionalized multiwalled carbon nanotubes (IL@MWCNT) nanocomposite was fabricated using ionic liquid (IL) 1-methyl-3-(4-sulfobutyl)-imidazolium chloride and multiwalled carbon nanotubes (MWCNTs) for detection of lead(II) ions. It is a novel method to heterogenize the layer of IL on the surface of MWCNTs. The XPS and FTIR analyses confirm that the ionic liquid is not decomposed during annealing process. Moreover, the XRD analysis shows the presence of MWCNTs and carbon quantum dots (CQDs). The HRTEM results exhibit the aggregation of MWCNTs with IL, and formation of small distorted round shaped flakes of CQDs. Further, the successful heterogenization of IL on the surface of MWCNTs is also confirmed by TGA-DSC analysis. The quenching phenomenon of nanocomposite was observed by UV-Visible spectroscopy. The nanocomposite exhibits high performance for the selective detection of lead(II) ions in comparison to other metal ions. The presence of lead(II) ions eventually reduced the intensity of absorption. A limit of detection (LOD) of 9.16 nM was attained for Pb2+ ions in a concentration range of 0-20 nM.

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.

Parthenium hysterophorus derived nanostructures as an efficient carbocatalyst for the electrochemical sensing of mercury(II) ions.

The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in the present work, we have made the effective management of Parthenium weeds by converting them into carbon-based nanomaterial through hydrothermal treatment followed by heating in a tube furnace under the nitrogen atmosphere. The XPS studies confirm the natural presence of nitrogen and oxygen-containing functional groups in the biomass-derived carbon. The nanostructure has adopted a layered two-dimensional structure, clearly indicated through HRTEM images. Further, the nanomaterials are analyzed for their ability towards the electrochemical detection of mercury, with a detection limit of 6.17 µM, while the limit of quantification and sensitivity was found to be 18.7 µM and 0.4723 µM µA-1 cm-2, respectively. The obtained two-dimensional architecture has increased the surface area, while the nitrogen and oxygen functional groups act as an active site for sensing the mercury ions. This study will open a new door for developing metal-free catalysts through a green and sustainable approach by recycling and utilization of waste biomass.

Nitrogen and sulfur functionalized microporous carbon nanomaterial derived from waste coconut husk for the efficient detection and removal of ofloxacin.

This study uses waste coconut husk to synthesize carbon quantum dots decorated graphene-like structure for sustainable detection and removal of ofloxacin. The XRD spectrum shows the carbon nanomaterial's layered structure with turbostratic carbon stacking on its surface. The FESEM and HRTEM studies claim the successful development of quantum dots decorated 2D layered structure of carbon nanomaterial. The functionalization of sulfur and nitrogen is well observed and studied through XPS, while Raman spectra have provided insight into the surface topology of the as-synthesized nanostructure. The BET surface area was found to be 1437.12 m2/g with a microporous structure (pore width 2.0 nm) which interestingly outcompete many reported carbon-based nanomaterials such as graphene oxide, reduced graphene oxide and quantum dots. The detection and removal processes are monitored through UV-visible spectroscopy and the obtained detection limit and adsorption capacity were 2.7 nM and 393.94 mg/L respectively. Additionally, 1 mg carbon nanomaterial has removed 49 % ofloxacin from water in just 1 h. In this way, this study has successfully managed the coconut husk waste after its utilization for environmental remediation purposes.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.

Efficient Evolving Deep Ensemble Medical Image Captioning Network.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management.

Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information. Medical care information requires robust security and privacy. Also there are other issues like confidentiality, interoperability, scalability, cost efficiency and timeliness that need to be addressed. To achieve these objectives, this paper proposes a novel-scalable patient centric yet privacy preserving framework for efficient and secure electronic medical records management. In addition, proposed system generates a unified trusted record and authentication role mapping for enforcing secure access control for medical records using complex encryption algorithms. This paper identifies 13 key performance factors for performance comparison of proposed framework with traditional models. Ethereum and Binance Smart Chain acted as a benchmark platform for performance evaluation of MRBSChain on the basis of three metrics (transaction cost, average block time and deployment cost).At last, a comparative analysis of MRBSChain with other state of art blockchain systems on the basis of execution time is presented in the paper.

Effect of countermeasure bodygear on cardiac-vascular-respiratory coupling during 6-degree head-down tilt: An earth-based microgravity study.

Prolonged exposure to microgravity causes physiological deconditioning in humans. Herein, a novel designed countermeasure gravitational load modulation bodygear has been developed to deal with the ill effects of the microgravity environment. The bodygear is designed to provide the wearer an axial loading from the shoulder to the feet that simulate Earth's gravity. The present study aims to evaluate the effect of bodygear on cardiac, vascular and respiratory systems during head-down tilt (HDT) microgravity analogue. In this, 30 healthy male subjects have volunteered and their average age, height and weight were 24.56 ± 3.87 yr, 168.4 ± 9.17 cm and 65.9 ± 10.51 kg respectively. The physiological signals such as electrocardiogram (ECG), blood pressure (BP) and respiration were recorded non-invasively using Biopac MP100. The signals were sampled at 1,000 Hz and processed using MATLAB 2018b. The signals were analysed in linear well as non-linear domains. The ECG and BP were used to derive R-R interval (RRI) and systolic blood pressure (SBP). The respiration time series (RSP) was derived by extracting R-peaks from the ECG signal and using these peaks to find the respiration amplitude. The non-linear domain analysis was used for the detection and quantification of information flow among the recorded signals. Repeated measure analysis of variance with Bonferroni post-hoc paired t-test was used for statistical analysis with the p < 0.05. The experimental results show that the 6-degree HDT activates the parasympathetic system and decreased the RRI effect on SBP (p = 0.005). Interestingly with the bodygear usage, the sympathetic system activated, mean RRI decreased (p = 0.018) and blood pressure increased (p = 0.031) as compared to baseline. Further, it was also observed that the effect of RRI on SBP (p = 0.029) and SBP on RRI (p = 0.012) was increased with bodygear as compared to HDT without bodygear. The conditional entropy technique aided in analyzing the effect of bodygear on information flow variation in the cardiovascular system of the human body.