Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Music Therapy Interventions on Heart Rate Variability in Premature Infants

Premature infants are at risk of developing serious complications after birth. Communicative interventions performed in neonatal intensive care units (NICUs), such as music therapy interventions, can reduce the stress experienced by these infants and promote the development of their autonomic nervous system. In this study we investigated the effects of music therapy interventions, consisting of singing, humming, talking or rhythmic reading, on premature infants by investigating the effects on their heart rate variability (HRV). A total of 27 communicative intervention from 18 patients were included in this study. The NN-intervals were extracted from the ECG and the mean ± SEM values for the 6 different features (HR, SDNN, RMSSD, pNN50, pDec and SDDec) was investigated. Median feature values for the pre- and communicative intervention were compared using the Wilcoxon signed-rank test. An increase in values for the SDNN, RMSSD and pNN50 was found in the 20 minutes preceding the communicative intervention, when caregiving activities were performed, and was followed by an immediate decrease at the start of the intervention. Features' variability during the intervention appeared to be smaller than in the pre-communicative intervention, indicating improved autonomic regulation. This difference was, however, not statistically significant possibly due to different types of activities applied during the communicative intervention per patient.
A Single-Stage Charger for LEV Application based on Quadratic Buck-Boost Converter Topology

A LEV (Light Electric Vehicle) charger based on quadratic step up-down gain is presented in this work. The model used for a LEV charger involves a single stage charger, which uses a quadratic voltage gain relationship that is able to provide very high step up-down along with unity power factor operation and low supply current distortions. The presented model uses inductors operated in discontinuous conduction mode (DCM) and hence is very vital in reducing the size of inductors and therefore the cost of the charger as a whole. The design of the charger is carried out and the performance is analyzed by using simulations, which involves running the model for high voltage fluctuations and battery charging current variations to ensure safe operation in worse cases and also both constant current (CC) and constant voltage (CV) charging methods of the battery. Overall, the presented system is able to give low supply current THD and also unity power factor at the supply side.
A Network-based Modeling Approach to Identify the Common Disease Classes in Patients with Late-stage Prostate Cancer

Prostate cancer (PCa) is one of the most common non-cutaneous cancers, and the second major cause of cancer death among men in the United States. The comorbidities study of PCa patients can inform treatment priority management and patient-oriented care. In this study, using the SEER-Medicare database, we adopt a network-based modeling approach to identify the most common disease classes among male with late-stage (stage D) PCa. The dataset comprises patients diagnosed with stage D PCa between January 1, 2010, and December 31, 2015, and a control group consisted of an equal size random sample of men without cancer. For both stage D PCa and control subpopulations, we created a set of 116 networks. Each network in the set represents a disease category identified using subsections of ICD-9 disease codes. In these networks, diseases and complications (identified using ICD-9 codes) are used as networks' nodes. Two nodes are connected if their related diseases' co-occurrences are observed in the dataset. Furthermore, the edges are weighted by the co-occurrence probability of complication pairs. A statistical approach was adopted to compare the differences between corresponding networks in the two groups. Out of 116 subsections, 59 had more than 30 distinct pairs, from which 51 subsections were statistically different between the control and the stage D PCa subpopulations (p - value < 0.001). Besides, a set of patient-level networks was developed for each patient in the two groups. The properties and components of patient-level networks were used as features in machine learning algorithms for predicting stage D PCa. The results show that the characteristics of the co-occurring diseases obtained from network modeling can help discover significant health issues in patients' subpopulations,
Neural network based algorithm for a spectrogram classification of wrist-type PPG using high-order harmonics processing

The importance of accurate and continuous heart rate monitoring during workout cannot be overestimated. Supporting heart rate in the desired range, you strengthen the heart muscle and the overall fitness level. Therefore, the precise heart rate measurements are important at each stage: before workout, during exercises and after completion. One of the most important problems for precise heart rate measurement during high intensive exercises with the help of wearable devices is the movement artifacts. Even the smallest movement of the muscles, the turn of the wrist, the movement of the fingers or even the displacement of the fitness tracker per millimeter - all this very much distorts the useful signal. To solve the problem of motion artifacts, both digital signal processing approaches are used, as well as deep learning methods. In this paper, we offer a new method of processing the signal of wearable devices during workout, in particular to solve the motion artifacts problem, using deep neural networks. A distinctive feature of the model is the use of higher signal harmonics to determine the shape and type of signal. In particular, a method is proposed for classifying the signal spectrogram to noise, movement and useful component. During cross-validation on an available datasets, we compared the effectiveness of the proposed approach for spectrogram classification and received an improvement of averaged ROC AUC (area under the receiver operating characteristic curve) and F1 Score by 5% by using higher harmonics. Clinical relevance— This work aims to provide an approach for a wearable devices PPG signal spectrogram classification using neural networks and high-order harmonics processing.
N170 component analysis of single-trial EEG based on electrophysiological source imaging

Event-related potentials (ERP) are brain-evoked potentials that reflect the neural activity of the brain. However, it is difficult to isolate the ERP components of our interest because single-trial EEG is disturbed by other signals, and the average ERP analysis in turn loses single-trial information. In this paper, we used electrophysiological source imaging (ESI) to analyze the N170 component of single-trial EEG triggered by face stimulation. The results show that ESI is feasible for the analysis of N170 and that there are left-right differences in the area of the fusiform gyrus associated with face stimulation in the brain. Clinical Relevance- Analysis of the N170 of single-trial EEG by ESI may help in the diagnosis of patients with prosopagnosia and may also help physicians clinically in determining whether the fusiform gyrus region is damaged.
Heart Rate Variability and its Association with Second Ventilatory Threshold Estimation in Maximal Exercise Test

During incremental exercise, two ventilatory thresholds (VT1, VT2) can normally be identified from gas exchange and ventilatory measurements, such as oxygen uptake, carbon dioxide production and ventilation. In this paper, we attempt to estimate the VT2 using HRV indices derived from a wearable electrocardiogram during a maximal exercise test. The exercise test is conducted on a treadmill that raises its speed by 0.5 km/h every minute. We have 42 measured exercise tests from 24 healthy male volunteers. Three experts determined the VT2 in each exercise test independently and we used principal component subspace reconstruction of their determinations to compute a collective VT2 for our machine learning model. The results demonstrate that the VT2 can be estimated from HRV using the proposed method with a reasonable performance during a maximal exercise test. In 28 out of 42 exercise tests, the HRV-derived threshold (HRVT) is within a minute (one phase) of the collective expert's determination.
Dynamic Packet Filtering Using Machine Learning

With the advent of the Internet, cyber-attacks and threats have become a major concern. Traditional methods of manual network monitoring and rule-based packet filtering are tedious and have become less effective against attacks. Filtering packets purely based on payload and pattern matching are also inefficient. There is need for a dynamic model which can learn the rules to filter packets. This paper proposes a machine learning-based packet filtering model using Neural Networks. After developing a classified model with training and validation data, the model can be utilized to support dynamic packet filtering. The proposed model provides the capability to filter the packets not purely based on static rule-based filtering, but on attributes in IP packets and previously learned rules from the model. The proposed model considers both payload and other attributes in the IP packet for filtering. The model can automatically update the firewall rules to enhance security.
Cybersecurity Issues in Communication-Based Electrical Protections

Cybersecurity is becoming a fundamental issue in Smart Grids. In the last past years, there have been remarkable advances in technologies for enhancing the security of electrical systems. Still, some systems shows severe vulnerabilities. One of them is represented by communication-based electrical protection. The present work analyzes vulnerabilities in such type of control networks. We analyze the attack models to communication-based electrical protection systems, discussing the impact of the implementation of IEC 62351 on these vulnerabilities. We also discuss possible countermeasures which can be useful to address the discussed vulnerabilities.
Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder

Mental disorders such as schizophrenia have been challenging to characterize due in part to their heterogeneous presentation in individuals. Most studies have focused on identifying groups differences and have typically ignored the heterogeneous patterns within groups. Here we propose a novel approach based on a variational autoencoder (VAE) to interpolate static functional network connectivity (sFNC) across individuals, with group-specific patterns between schizophrenia patients and controls captured simultaneously. We then visualize the original sFNC in a 2D grid according to the samples in the VAE latent space. We observe a high correspondence between the generated and the original sFNC. The proposed framework facilitates data visualization and can potentially be applied to predict the stage that a subject falls within a disorder continuum as well as characterize individual heterogeneity within and between groups.
Finite Element Simulations of Flip Chip Solder Bumps with Fractures Present using FreeFem++

In this paper, 3D finite element models are built for flip chip solder bumps with fractures present. Three types of cracks are simulated with one located around the top interface of the bumps, one within the bulk and one around the bottom interface of the bumps. The mechanic equilibrium equations are solved to find the elastic stresses and thus to find the locations with stress concentration. Modal analyses are performed to find the influences of cracks on the natural frequencies of the bumps. Simulation results show that the stress concentration at the tip of a type II crack is much smaller; while evident stress concentration at the tips of both type I and III cracks are found except for the case of a type III crack with an applied shear stress; stress concentration is also found on the top surface of the Cu pad, and the maximum of Mises stress there varies on a small scale as the percentage of the fracture area increases; the percentage of the fracture area is not shown to have an evident influence on the natural frequency until the structure of the joint is sufficiently damaged by the crack.
Computation of Activating Fields for Approximation of the Orientation-Specific Neural Response to Electrical Stimulation

Computational methods of determining the response of neural tissue to electrical stimulation have demonstrated value for the development of novel devices and the programming of neuromodulation therapies. Detailed biophysical models are excessively computationally intensive for many applications; simple metrics to approximate activation can speed up progress in this area. The activating function provides such a useful metric. However, this measure, defined for a specific axon orientation, is not immediately applicable to computed electric fields to assess their effects. We demonstrate a method for computation of the activating function generalized to a field in order to allow rapid computation of the effects of stimulation on neural tissue while preserving information on axon orientation. Clinical Relevance— This demonstrates a useful method of approximating the effect of electrical stimulation on nervous tissue for the development of devices and the optimization of parameters for electrical neuromodulation
Human body odors of happiness and fear modulate the late positive potential component during neutral face processing: a preliminary ERP study on healthy subjects

Human body odors (HBOs) are powerful stimuli that can affect emotional, cognitive and behavioral processes. However, the characterization of the physiological response to HBOs is still to be fully investigated. Here, we analyzed the self-assessed emotion perception and the EEG event-related potentials (ERP) on 17 healthy young women during a simultaneous visual-olfactory stimulation. Particularly, we evaluated the effect of happiness and fear HBO on the amplitude of ERP waveforms elicited by neutral face processing. In addition, we evaluated the subjective valence and arousal perception of the presented neutral faces by means of the self-assessment-manikin test. We observed a significant increase in the amplitude of the late positive potential (LPP) for central left sites (i.e., C3) during the administration of HBOs with respect to clean air. On the other hand, we did not observe any significant change in the subjective valence and arousal scores as well as for the early components of the ERP (i.e., P100, N170, Vertex-Positive-Potential). Our preliminary results suggest that fear and happiness HBO can induce a protracted increase in the LPP, and possibly reflect an automatic and sustained engagement with emotionally significant content.
A High-Efficiency Mobility Load Balancing System Architecture for Hybrid Mobile Cell Network

The fifth-generation network increases the speed of data transmission by providing more bandwidth than in the past. The larger bandwidth also increases the number of device connections in a same size area. However, people may be concentrated in a specific area for various reasons. This can easily cause the base station load to become heavy. If the connected device exceeds the load that the base station can accommodate, it will affect the connection efficiency and transmission quality. Therefore, this study proposes a mobile load balancing system architecture based on macro cell and small cell hybrid networks, which is used to implement an effective load balancing to maximize the load capacity of the overall network.
Malignancy Suspicious Region Guided Deep Neural Networks for Gastric Ulcer Classification

Malignant transformation of gastric ulcer can result in gastric cancer, hence an accurate gastric ulcer classification method is of vital importance. Despite marvelous progress has been achieved in recent years, there are still many challenges in diagnosis of gastric ulcer. In this paper, we propose a mechanism to mimic gastroenterologist's behaviours based on deep learning techniques, by integrating the segmented malignancy suspicious masks with gastroscopic images for gastric ulcer classification, which instructs the model to focus on the area where symptoms occur for gastric ulcer diagnosis. Specifically, a U-Net-type deep neural network is built to segment the suspicious pathological regions from gastroscopic images, then the segmented regions are treated as an attention channel of gastroscopic images for the gastric ulcer classification by a ResNet-type deep neural network. Experiments on a real gastroscopic dataset with 900+ patient cases demonstrate that our proposed approach achieves much better performance for gastric ulcer diagnosis, compared with standard method with only gastroscopic images.
Elimination of pseudo-HFOs in iEEG using sparse representation and Random Forest classifier

High-Frequency Oscillation (HFO) is a promising biomarker of the epileptogenic zone. However, sharp artifacts might easily pass the conventional HFO detectors as real HFOs and reduce the seizure onset zone (SOZ) localization. We hypothesize that, unlike pseudo-HFOs, which originates from artifacts with sharp changes or arbitrary waveform characteristic, real HFOs could be represented by a limited number of oscillatory waveforms. Accordingly, to distinguish true ones from pseudo-HFOs, we established a new classification method based on sparse representation of candidate events that passed an initial detector with high sensitivity but low specificity. Specifically, using the Orthogonal Matching Pursuit (OMP) and a redundant Gabor dictionary, each event was represented sparsely in an iterative fashion. The approximation error was estimated over 30 iterations which were concatenated to form a 30-dimensional feature vector and fed to a random forest classifier. Based on the selected dictionary elements, our method can further classify HFOs into Ripples (R) and Fast Ripples (FR). In this scheme, two experts visually inspected 2075 events captured in iEEG recordings from 5 different subjects and labeled them as true-HFO or Pseudo-HFO. We reached 90.22% classification accuracy in labeled events and a 21.16% SOZ localization improvement compared to the conventional amplitude-threshold-based detector. Our sparse representation framework also classified the detected HFOs into R and FR subcategories. We reached 91.24% SOZ accuracy with the detected $\mathrm{R}+\text{FR}$ events. Clinical Relevance---This sparse representation framework establishes a new approach to distinguish real from pseudo-HFOs in prolonged iEEG recordings. It also provides reliable SOZ identification without the selection of artifact-free segments.
Effect of Functional Electrical Stimulation on Capillary Blood Flow to Muscle

Functional electrical stimulation (FES) modifies red blood cells (RBCs) flux in blood capillaries of muscle. In this work, we aim to investigate changes in the RBC flux in small and large capillaries due to FES using zinc oxide nanowires (ZnO NWs) based electrode at different stimulation parameters. The RBC flux was significantly increased immediately after stimulation, which was evident from decreasing light intensity measured in the region of interest. Clinical Relevance— FES has numerous forms and functions. The benefit of FES is the increased blood flow to a muscle which is contracted abnormally. This work explores the use of FES to increase the blood flow and RBC flux in blood capillaries of stimulated muscle as FES generate muscle contraction and absorption.
Multi-granular Analysis and Physiological Interpretations of Heart Rate Variability Metrics During VR-Shooting Difficulty Induced Stress

Physiological sensing of virtual reality (VR)-induced stressors are increasingly utilized to improve human training and assess the impact of gaming difficulty-induced stress on a person's health and well-being. However, the prior art sparsely explores the multi-level cardiovascular dynamics for psychophysiological demands in a VR environment. This treatise discusses the experimental findings and physiological interpretations of various heart rate variability (HRV) metrics extracted from 31 participants during a Go/No-Go VR-based shooting task across multiple timeframes. The VR-shooting exercise consists of firing at the enemy targets while sparing the friendly ones for different shooting difficulty levels: low-difficulty and high-difficulty with in-between baselines. Ex-perimental results demonstrate consistent shooting difficulty-induced stress patterns at multi-granular levels in response to the heterogeneous inputs (exogenous and endogenous factors). The physiological interpretations highlight the intricate inter-play between cardio-physiological components: sympathetic and parasympathetic response across multiple timescales (sessions and blocks) and shooting difficulty levels.
A fast and accurate learning-based decoding algorithm for the classification of cardiovascular and respiratory challenges using intraneural electrodes in the pig vagus nerve

Bioelectronic medicine is a new approach for developing closed-loop neuromodulation protocols on the peripheral nervous system (PNS) to treat a wide range of disorders currently treated with pharmacological approaches. Algorithms need to have low computational cost in order to acquire, process and model data for the modulation of the PNS in real time. Here, we present a fast learning-based decoding algorithm for the classification of cardiovascular and respiratory functional alterations (i.e., challenges) by using neural signals recorded from intraneural electrodes implanted in the vagus nerve of 5 pigs. Our algorithm relies on 9 handcrafted features, extracted following signal temporal windowing, and a multi-layer perceptron (MLP) for feature classification. We achieved fast and accurate classification of the challenges, with a computational time for feature extraction and prediction lower than 1.5 ms. The MLP achieved a balanced accuracy higher than 80 % for all recordings. Our algorithm could represent a step towards the development of a closed-loop system based on a single intraneural interface with both the potential of real time classification and selective modulation of the PNS.
Enhanced Vascular Features in Porcine Gastrointestinal Endoscopy Using Multispectral Imaging

Endoscopic investigation is a predominant stan-dard while assessing the gastrointestinal tract. Even though it has been rigorously used in diagnostics for many decades, a high miss rate has been recorded. Advanced endoscopic imaging still has not found solutions to problems like early cancer detection, polyp generality, disease classification, etc. One of the less explored techniques to study early cancer detection is spectral imaging which deals with the absorption and reflection spectra of various wavelengths of light by different layers of tissue. To study tissues under various illumination, a multi-spectral light source unit that can be used along with an endoscopy system was developed with 10 different LEDs of very narrow bandwidths. Using this light source, a feasibility study was per-formed on an animal in which the upper GI tract of a porcine model was imaged and sample images were taken for processing from five different sections. Some wavelengths showed better contrast enhancements for visualization of vascular structures. Wavelength 420 nm (violet light) showed better contrast and the gradient of the line profile histogram showed the highest intensity change between the blood vessels and the surrounding mucosa. These enhancements showed that spectral imaging can potentially help in studying tissues for early cancer detection and improved visualization of the G I tract using endoscopy.