Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable automatic sleep staging in the era of Big Data

Numerous automatic sleep staging approaches have been proposed to provide an eHealth alternative to the current gold-standard – hypnogram scoring by human experts. However, a majority of such studies exploit data of limited scale, which compromises both the validation and the reproducibility and transferability of such automatic sleep staging systems in real clinical settings. In addition, the computational issues and physical meaningfulness of the analysis are typically neglected, yet affordable computation is a key criterion in Big Data analytics. To this end, we establish a comprehensive analysis framework to rigorously evaluate the feasibility of automatic sleep staging from multiple perspectives, including robustness with respect to the number of training subjects, model complexity, and different classifiers. This is achieved for a large collection of publicly accessible polysomnography (PSG) data, recorded over 515 subjects. The trade-off between affordable computation and satisfactory accuracy is shown to be fulfilled by an extreme learning machine (ELM) classifier, which in conjunction with the physically meaningful hidden Markov model (HMM) of the transition between the different sleep stages (smoothing model) is shown to achieve both fast computation and the highest average Cohen’s kappa value of κ = 0.73 (Substantial Agreement). Finally, it is shown that for accurate and robust automatic sleep staging, a combination of structural complexity (multi-scale entropy) and frequency-domain (spectral edge frequency) features is both computationally affordable and physically meaningful.
Incentive Stackelberg Strategy for Weakly-Coupled Large-Scale Systems

An incentive Stackelberg game for a continuous-time weakly-coupled large-scale system with multiple leaders and multiple followers is investigated. The existence conditions on the incentive Stackelberg strategies are derived in terms of the solvabilty of a set of reduced order algebraic Riccati equations for leader's Nash strategy set. On the other hand, it is shown that the follower's strategy set can be computed by solving the parameter independent coupled matrix equations. An academic numerical example is provided to show the existence of the incentive Stackelberg strategies for the problem formulated.
Verification of the adaptive parameters of the relative positions of the leading leg and the whole body at foot contact during split-belt treadmill walking

The main purpose of this study was to demonstrate that adaptive walking re-establishes dynamic stability at the time of foot contact in split-belt treadmill walking. First, we examined whether the relative positions of the center of mass (COM) and the center of pressure (COP) were re-established at foot contact during walking adaptation. We found that the symmetry of the COM-COP angles was re-established more completely than any other parameters (foot and pelvis markers) on both the fast and slow belt sides during the adaptation phase. Moreover, we examined whether the dynamic relationship between the COM and the COP was the same during tied- and split-belt walking. Our results showed that the equations related to the trailing leg relative to the COM re-converged around 0 at foot contact on both the fast and slow sides during the adaptation phase. We also showed that the COM-COP angle at foot contact is an adaptive parameter and is consistent between the fast and slow belt sides. At foot contact, the relative positions between the leading leg and the whole body can also be seen to be adjusted during trial-and-error adaptation to split-belt perturbation in which the belts move at different speeds. Therefore, we suggest that the relative positions of the COM and the COP at foot contact is the spatial reference for the gait cycle contribution to dynamic stabilization during adaptive walking.
Dielectric modeling of oil-paper insulation systems at high DC voltage stress using a charge-carrier-based approach

It is state-of-the-art to describe the dielectric behavior of an insulation material by its permittivity and its specific electric conductivity in order to estimate the dielectric stress of an insulation system. Thus, the electric field at DC voltage stress is determined according to the stationary electrical conduction field with the electric conductivity of the insulation materials. However, at oil-insulated arrangements a higher field strength in front of bare metal electrodes at high DC voltage stress occurs, which is not explainable with this model. Therefore, a charge carrier-based approach is presented to describe the dielectric behavior of the oil-paper insulation. It describes the movement of charge carriers and their effect on the electric field strength. Their drift leads to an accumulation of charge carriers in front of electrodes which results in a higher field strength in these areas, which can be calculated numerically using the Poisson-Nernst-Planck equation system. Compared to the conductivity-based model fundamental differences can be shown. Breakdown experiments qualitatively confirm the expectations according to the charge carrier-based approach. The results show that the charge carrier-based field distribution has to be considered for modelling the electrical field strength distribution at high DC voltage stress. They also show, that the dielectric behavior of these arrangements cannot be explained according to the state-of-the-art model.
Sleep Staging Monitoring Based on Sonar Smartphone Technology

This paper presents the validation results of a new non-contact ultrasonic technology, which employs inaudible Sonar to monitor the movements and respiration of a subject in bed. Sleep monitoring can be achieved by placing a smartphone onto the bedside table and starting a custom app. The app employs sophisticated and novel proprietary algorithms to identify sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), Rapid Eye Movement (REM) Sleep or Absence.The sleep staging performance of the app were assessed by testing it against expert manually scored polysomnography (PSG) of 38 subjects gathered in a sleep laboratory. As a secondary assessment, on the same dataset, the performance of the app is compared to that of a reference non-contact device, the S+ by ResMed.Performance across different sleep stage detections was balanced, exceeding the agreement typically reported for actigraphy based devices [1], [2] thanks to a significantly higher sensitivity for all sleep stages. Furthermore, the performance of the app was found to be comparable to the S+ by ResMed product [3], [4]. The combination of unobtrusive non-contact sensing and accurate sleep quality assessment, coupled with removal of the requirement to purchase a custom device to enable monitoring of sleep, enables consumers to measure their sleep in the home environment in a zero-cost and accessible manner, while providing sleep staging information not otherwise available with actigraphy based devices.
Fractal Analysis of Motor Control in Knee Arthroplasty Patients

Knee osteoarthritis is commonly treated through total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA) and therefore the assessment of postoperative differences in functional capabilities between TKA and UKA patients appears of primary importance. Throughout the years, fractal analysis has been applied to several biological time-series, revealing to be particularly useful for assessing human balance and motor control by quantifying complexity and repeatability of dynamic measures. In this study, fractal dimension (FD) has been computed on ground reaction force and momentum acquired during squatting movement in two groups of TKA and UKA patients and a control group of healthy subjects (CTRL). FD resulted able to discriminate between TKA and both CTRL and UKA group, showing significant differences in all the considered measures. Outcomes of this study could help to gain further information about functional recovery after different knee arthroplasty procedures, in order to improve the choice of rehabilitative treatment.
RTL Design for Time Efficient DDR3 Memory Interfaced with RTG4 FPGA

The new age of remote detecting instrument typically creates a gigantic measure of information, which must be transmitted to the earth station for handling. In this paper, a RTL Design is proposed in Libero SP3 Microsemi Software for data processing. This design contains FDDR memory controller of DDR3 memory of RTG4 FPGA. The interfacing of memory controller and user module is done by AXI (Advanced eXtensible Interface) Bus having 64 bit data-bus and 32 bit address-bus. Input data frequency is 160MHz and the output data rate is 320Mbps as in DDR3 memory data transfer done in burst format and also in both the clock edges. The proposed design is synthesizable and also verified with simulation result in Microsemi ModelSim Pro. From result it is analyzed that the read time is reduced by 46% then write time.
Toward Comparison of Cortical Activation with Different Motor Learning Methods Using Event-Related Design: EEG-fNIRS Study

Recently, motor imagery brain-computer interface (MI-BCI) has been studied as a motor learning method and evaluated by comparing with conventional passive and active training. Most functional near-infrared spectroscopy (fNIRS) studies adopted block design for comparing those motor learning methods, including MI-BCI. Compared to the block design, event-related design would be more appropriate for estimating cortical activation in MI-BCI which provides feedback for each trial. This paper is a preliminary study to check the feasibility whether event-related design can be applicable for MI-BCI. To this end, three different motor learning methods involving MI-BCI were compared. In hemodynamic response, MI-BCI showed significantly stronger cortical activation than passive training (PT), and weaker than active training (AT), which conforms most existing studies. The results demonstrate that event-related design could be applied to investigate cortical effects of MI-BCI and comparing hemodynamic responses of different motor learning methods.
Interplay between nanoparticles and water on dielectric properties of nanofluids

This paper deals with the interaction of nanoparticles (NPs) in transformer oil and water in the oil and its effect on the electrical properties of the nanoffuid (NF). The breakdown strength and dielectric spectrum measurements under AC for various concentrations of natural SiO2 and water in mineral oil were studied. Mineral oil-based SiO2 NFs exhibit significantly higher breakdown voltage than that of base oil under high water content. However, there is an optimal range of NP concentration and water content for maximum breakdown voltage. The results of dielectric spectrum indicate that the dielectric constants of NFs and base oil are greatly affected by water content. The real relative permittivity of oil samples increases exponentially with water content at decreasing frequency (f≤10 Hz). The imaginary relative permittivity of NFs is less than that of the base oil in all cases. Based the experimental results reported, a model on the interplay between NPs and water in transformer oil is presented. The model can broadly explain the influence of the coexistence of NPs and water on the dielectric properties of transformer oil.
Digital Implementation of Fractional Order PID-Type Controller for Boost DC–DC Converter

In this paper, the problem of designing a fractional order PID-type controller is considered for a boost converter. By using the output capacitance and input inductance values this paper characterizes integer order PID-type control gains which will make the closed-loop system transfer function approximately equal to a first order system with a unit DC gain and prescribed time constant $\tau $ . Next, a procedure to compute the design parameters of a fractional order PID-type controller is given together with a descritized control algorithm for DSP implementation. By using a floating-point DSP, the proposed control algorithm is implemented in real time. Finally, experimental results are given to show the practical feasibility and effectiveness of the proposed fractional order PID-type control system under several operating conditions. The results illuminate that the proposed controller can be better than a conventional integer order PID-type controller.
Biosignal-Based Multimodal Emotion Recognition in a Valence-Arousal Affective Framework Applied to Immersive Video Visualization

Many emotion recognition schemes have been proposed in the state-of-the-art. They generally differ in terms of the emotion elicitation methods, target emotional states to recognize, data sources or modalities, and classification techniques. In this work several biosignals are explored for emotion assessment during immersive video visualization, collecting multimodal data from Electrocardiography (ECG), Electrodermal Activity (EDA), Blood Volume Pulse (BVP) and Respiration sensors. Participants reported their emotional state of the day (baseline), and provided self-assessment of the emotion experienced in each video through the Self-Assessment Manikin (SAM), in the valence-arousal space. Multiple physiological and statistical features extracted from the biosignals were used as inputs to an emotion recognition workflow, targeting user-independent classification with two classes per dimension. Support Vector Machines (SVM) were used, as it is considered one of the most promising classifiers in the field. The proposed approach lead to accuracies of 69.13% for arousal and 67.75% for valence, which are encouraging for further research with a larger training dataset and population.
Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System

This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<0.001) and alpha moments decreased significantly (P<0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable.
Quantitative Assessment of Ataxic Gait using Inertial Sensing at Different Walking Speeds

In this study, we employed wearable sensor technology to identify the kinematic features that are associated with the gait abnormalities seen in cerebellar ataxia. Measurements were made at self-selected slow, preferred and fast walking speeds with three sensors attached to the participant’s body. Velocity irregularity and resonant frequency characteristics were identified as key features of interest concerning truncal and lower limb movements. Using the principal component analysis, differentiating features of both trunk and lower limb movements were combined to produce an enhanced distinction between the patients and the normal subjects, in addition to obtaining a better correlation with the expert clinician’s assessments. The different speed of walking contributed to separation and correlation with medical severity rating scales such as SARA to varying degrees The results of truncal movement in the medio-lateral plane (at the slow gait speed) and antero-posterior movement (at all 3 gait speeds) provided the effective metrics in the diagnosis and severity rating of ataxic gait of CA patients. Furthermore, from the selected dominant features of the trunk and lower limb, principal component description suggested that overall clinical assessments are predominantly influenced by the lower body peripheral movements particularly at higher walking speeds.
4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.
A Novel Real-time Automatic Angioectasia Detection Method in Wireless Capsule Endoscopy Video Feed

The development of Wireless Capsule Endoscopy (WCE) revolutionized the examination of the small bowel for diseases. Upon swallowing a capsule (a microscopic camera that resembles an ordinary pill in both shape and size), images of the patient’s gastrointestinal (GI) tract are wirelessly transmitted from it to an external recorder. The inspection of these images is, to this day, still manually performed by medical professionals - a lengthy, and especially prone to errors, process. One of the most common diagnoses is the presence of angioectasias, i.e. ectatic vessels on the GI tract that are predisposed to bleeding. In this paper, a novel method for automatic detection of these lesions is proposed, using a combination of low-level image processing, feature detection and machine learning, that can run in real-time without the need for specialized hardware or graphics cards, achieving 92.7% sensitivity and 99.5% specificity to angioectasias. This method can also be expanded to include more pathologies.
Analysis of NOMA for Future Cellular Communication

NOMA is the emerging and most suitable wireless technology for next generation communication system, due to its inherent capability to meet diverse exigency. The parameters which make 5G technology superior over existing technology are minimum delay, high reliability, enormous connectivity and high data rate. This technology facilitates access to several clients simultaneously and within identical frequency range using differential power domain concept. To mitigate the energy consumption and provide the viable green communication the 5G scheme has been introduced. The proposed work focuses on the energy efficient resource optimization for NOMA. There are multiple NOMA procedures such as power and code-domain. The researchers across the globe have shown that NOMA can be utilized optimally to fulfill both system level and clients experience prerequisites of fifth generation innovations. Apropos, this paper does elaborate studies on the current evolution of NOMA in 5G structure, scrutinizing on the cutting edge limit examination, control selection technique, client requirement, and client complementing scheme in NOMA.
Hearables: Feasibility and Validation of In-Ear Electrocardiogram

Out-of-clinic, continuous monitoring of vital signs is envisaged to become the backbone of future e-health. The emerging wrist worn devices have already proven to be a success in the measurement of pulse, however, a susceptibility to artefacts and missing data caused by regular motion in everyday activities, and the inability to continuously acquire the electrocardiogram call into question the utility of this technology in future e-Health. With this in mind, the head, and in particular the ear canals, have been investigated as possible locations for wearable devices. The ears offer a stable position relative to the vital signs during everyday activities, such as sitting, walking, running and sleeping, as well as being a practical and widely accepted base for wearable accessories. This all suggests that the ear canals are a most natural location for physiological sensing in the community. This work addresses the feasibility of recording the ECG from the ear canals, from a one-fits-all, user-friendly device. For rigour and clarity, we quantitatively compare the timings of the identified P-, QRS-, and T-waves within Ear-ECG and standard arm-ECG. Finally, to depict a future e-Health scenario for the Ear-ECG technology, a case study with an abnormal heart condition, the ventricular bigeminy, is presented. A comprehensive study over ten subjects demonstrates conclusively the possibility of in-ear cardiac monitoring in normal daily life.
A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus

Modelling of the gluco-regulatory system in response to an oral glucose tolerance test (OGTT) has been the subject of research for decades. This paper presents an adaptation to the well-established oral minimal model that is identifiable from glucose data only and is able to capture the dynamics of glucose following both OGTT and mixed meal consumption. The model is in the form of low-dimensional differential equations with a recently introduced input function consisting of Gaussian shaped components. It was identified from glucose data recorded from six subjects without diabetes, prediabetes and type 2 diabetes under controlled conditions. The inferred parameters of the model are shown to have physiological meaning and produce realistic steady state behavior. This model may be useful in the development of clinical advisory tools for the treatment and prevention of non-insulin dependent type 2 diabetes mellitus.
Estimating Local Therapeutic Hypothermia in Case of Ischemic Stroke Using a 1D Hemodynamics Model and an Energetic Temperature Model

In Western countries, stroke is the third-most widespread cause of death. 80% of all strokes are ischemic and show a mortality rate of about 25%. Furthermore, 35-55% of affected patients retain a permanent disability. Therapeutic hypothermia (TH) could decrease inflammatory processes and the stroke-induced cerebral damage. Currently, the standard technique to induce TH is cooling of the whole body, which can cause several side effects. A novel cooling sheath uses intra-carotid blood cooling to induce local TH. Unfortunately, the control of the temporal and spatial cerebral temperature course requires invasive temperature measurements. Computational modeling could be used to predict the resulting temperature courses instead. In this work, a detailed 1D hemodynamics model of the cerebral arterial system was coupled with an energetic temperature model. For physiological conditions, 50% and 100% M1-stenoses, the temperatures in the supply area of the middle cerebral artery (MCA) and of the systemic body was analyzed. A 2K temperature decrease was reached within 10min of cooling for physiological conditions and 50% stenosis. For 100% stenosis, a significant lower cooling effect was observed, resulting in a maximum cerebral temperature decrease of 0.7K after 30min of cooling. A significant influence of collateral flow rates on the cooling effect was observed. However, regardless of the stenosis degree, the temperature decrease was strongest within the first 20min of cooling, which demonstrates the fast and effective impact of intra-carotid blood cooling.