Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent updates on metabolite composition and medicinal benefits of mangosteen plant

Background Mangosteen (Garcinia mangostana L.) fruit has a unique sweet-sour taste and is rich in beneficial compounds such as xanthones. Mangosteen originally been used in various folk medicines to treat diarrhea, wounds, and fever. More recently, it had been used as a major component in health supplement products for weight loss and for promoting general health. This is perhaps due to its known medicinal benefits, including as anti-oxidant and anti-inflammation. Interestingly, publications related to mangosteen have surged in recent years, suggesting its popularity and usefulness in research laboratories. However, there are still no updated reviews (up to 2018) in this booming research area, particularly on its metabolite composition and medicinal benefits. Method In this review, we have covered recent articles within the years of 2016 to 2018 which focus on several aspects including the latest findings on the compound composition of mangosteen fruit as well as its medicinal usages. Result Mangosteen has been vastly used in medicinal areas including in anti-cancer, anti-microbial, and anti-diabetes treatments. Furthermore, we have also described the benefits of mangosteen extract in protecting various human organs such as liver, skin, joint, eye, neuron, bowel, and cardiovascular tissues against disorders and diseases. Conclusion All in all, this review describes the numerous manipulations of mangosteen extracted compounds in medicinal areas and highlights the current trend of its research. This will be important for future directed research and may allow researchers to tackle the next big challenge in mangosteen study: drug development and human applications.
Detailed Electromechanical Model of Ventricular Wedge

We developed a three-dimensional computational model for describing electro-mechanical behavior of wedge-shaped preparation extracted from the left ventricular wall including excitation wave propagation, high-resolution geometry and fiber orientation. The cardiac tissue is simulated by an incompressible hyperplastic material. We used non-linear partial differential equations describing the deformation of the cardiac tissue, and a detailed “Ekaterinburg-Oxford” (EO) cellular model of the electrical and mechanical activity of the cardiomyocytes in the tissue. Electro-mechanical coupling in the model accounts for mechano-electric feedbacks both in the cells and in the tissue. Numerical experiments with the model of the wedge preparation formed of initially identical cardiomyocytes revealed that electrical and mechanical interaction between the cells, as well as intracellular mechanoelectric feedbacks caused pronounced nonuniformity of their behavior.
Forcing Transmembrane Voltages to Decrease Slowly: A Temporal Regularization for ECG Imaging

ECG imaging aims to reconstruct the cardiac electrical activity from non-invasive measurements of body surface potentials (BSP) by finding unique and physiologically meaningful solutions to the inverse problem of electrocardiography. This can be accomplished using regularization, which reduces the space of admissible solutions by demanding solution properties that are already known beforehand. Messnarz et. al. proposed a regularization scheme that requires transmembrane voltages (TMV) to not decrease over time. We suggest a generalization of this method that forces TMVs to decrease only slowly and as a result can also be applied to irregular cardiac activity. We first develop the method using a simplified spherical geometry and then show its benefit for imaging fibrillatory activity on a realistic geometry of the atria.
Spectral-Based Analysis of Progressive Dynamical Changes in the Fetal Heart Rate Signal During Labor by Using Empirical Mode Decomposition

In this work, we propose to study the progressive fetal response along the fetal heart rate (FHR) signal by using empirical mode decomposition and time-varying spectral-based analysis. The main idea is to investigate if a particular FHR signal episode in the time-domain reflects dynamical changes in the frequency-domain that can help to assess the fetal condition. Results show that the spectral components associated with the neural sympathetic fetal reactivity exhibit significant spectral energy differences between normal and acidotic fetuses.
Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals

We propose an end-to-end deep learning method to detect sleep arousals, especially non-apnea sleep arousals, which is the aim of Physionet/CinC Challenge 2018. We use filtered multi-physiological signals as the input of the network without any other hand-crafted features. The network automatically selects the best features to match arousal targets that we want to identify, and outputs the test result. The proposed network architecture is a 35-layer convolutional neural network (CNN) with three parts: a linear spatial filtering with 1 CNN layer, 33-layer Residual Networks (ResNets), and 1 fully connected layer. For the multi-physiological signals provided in the dataset we choose the 6-channel electroencephalography (EEG) and the 3-channel electroencephalography (EMG) signals, since these signals can better represent the characteristics of non-apnea sleep arousals. In the prediction phase, we use a sliding window method to maximize the performance of sleep arousals detection. For the training set, the result of the area under the precision-recall curve (AUPRC) is 0.3173; the area under the receiver operating characteristic curve (AUROC) is 0.8646. For the final test subset, the result of AUPRC is 0.315; AUROC is 0.858.
Unsupervised Artefact Detection and Screening Using Emfit Sensor in Patients With Sleep Apnea

Sleep apnea is one of the most common sleep disorders. As sleep apnea is associated to adverse health outcomes, early screening is promoted through unobtrusive, cheap and simple systems for sleep monitoring. A commercial pressure sensor meeting these requirements is the Emfit QS, which was integrated in a bed of a specialized sleep center. The sensor is pressure based and highly sensitive to movement. This causes artefacts of different morphologies in the signal. An unsupervised artefact detection method was developed to avoid burdensome manual labelling of artefacts in the signal and enabling further analysis. Moreover, the percentage of detected artefacts was useful for assessment of the sleep apnea severity as movements partially originate from apneic arousals. Severe sleep apnea patients could be identified with a sensitivity of 80% and a specificity of 87%. The proposed approach offers an ambivalent tool for artefact detection and unobtrusive screening of sleep apnea patients at home.
Sensitivity Analysis of a Cardiorespiratory Model for the Study of Sleep Apnea

A novel integrated model of cardio-respiratory interactions is presented in this paper with the objective of understanding the acute physiological response due to events of sleep apneas and hypopneas in adults. A formal sensitivity analysis is proposed, focused on the chemoreflex and metabolism gas exchange components of this model, during the simulation of a 20-seconds obstructive apnea episode. During apnea, the most influent parameters were those related to metabolic rates. After the apnea, the most relevant parameters were the one related with the the amplitude gains of the central and peripheral chemoreflex. A first qualitative comparison has shown a close behaviour between experimental and simulated cardiorespiratory responses to apnea. These results highlight the influent components of chemoreflex control and the metabolic rates and provides key information towards the definition of patient-specific parameters.
Using Auxiliary Loss to Improve Sleep Arousal Detection With Neural Network

Our pipeline consists of a hand-crafted preprocessor and a neural network classifier. We applied transformations on the physiologic signals to gain features in both time- and frequency domains. The proposed algorithm was trained on 994 annotated records of polysomnographic signals. Most of the features were generated from the EEG signal such as power spectral density, and entropy. We extracted features from the EOG, EMG, airflow, and ECG signals too. All the features were normalized. These 68 features were resampled in 21 non-continuous moments around the current timestamp, and fed into a 3-layer neural network in order to assign a probability of arousal at each second. Arousal samples were enriched during training to battle data imbalance. Additional (auxiliary) losses can guide the network to learn high-level concepts, even though they will not be evaluated. We used sleep stages as additional training targets, which were easier to learn than arousals despite being multi-class. This approach slightly increased arousal AUPRC. Our submitted results for the entire test set were evaluated: AUPRC=0.42. Our 10-fold cross validation results for the AUPRC are the following: [0.47110, 0.41672, 0.44305, 0.42842, 0.44644, 0.47969, 0.45082, 0.49320, 0.45913, 0.41278] averaging 0.450.
Reverse Electrical Remodeling Assessed by High-Frequency QRS Dyssynchrony and QRS Duration

Reverse electrical remodeling (RER) is usually assessed by measuring the QRS duration (QRSd) before and after cardiac resynchronization therapy (CRT) during the stimulator off. The ventricular electrical depolarization dyssynchrony (DYS) can be assessed directly from ECG by high-frequency QRS analysis. The purpose of the study was to compare the RER assessment by the DYS and QRSd changes in CRT patients. Echocardiography and 12-lead ECG were conducted in 26 CRT recipients. The averaged V1-V6 QRS envelopes were calculated through frequency ranges from 150 to 950Hz. The DYS was assessed from the envelopes, and the QRSd was measured. All the ECG parameters were assessed before, after, and 6 months after the CRT during CRT off. Patients were divided to CRT responders/non-responders by the change of end-systolic volume of the left ventricle in a 6-month follow-up. Mean values of the DYS and QRSd parameters evince significantly shorter values in non-responders before CRT. When compared changes of DYS and QRSd, only DYS parameter is significantly shorter after 6-month follow-up compared with baseline DYS prior CRT. The DYS parameter change as a marker of RER corresponds with reverse structural remodeling assessed by echo cardiography.
Personalisation of Cellular Electrophysiology Models: Utopia?

As cell-level differences from person to person are gaining more attention, the idea of having personalised models of cell electrophysiology is growing ever more attractive. In this paper for the special session “Personalized medicine through integration of imaging and cardiac modeling”, I briefly review the different pathways to personalisation and the challenges they present.
Detection of Atrial Fibrillation from Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers

In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.
P-wave Analysis in Atrial Fibrillation Detection Using a Neural Network Clustering Algorithm

Absence of P-waves in ECG records with irregular interbeat intervals (R-R) is a sign of Atrial Fibrillation (AF). Detection of P-waves in ECG beats or even average beats could be challenging if the artifact resembles a P-wave, or an actual P-wave is buried in the artifact. We developed a neural network algorithm to generate the ECG beat clusters in segments of the record. Beats with matching QRS complexes were clustered using Self-Organizing Map (SOM) technique and then cross-correlated to combine and generate the dominant clusters. This process helps to eliminate the abnormal or artifact-corrupted beats. Fiducial points of the dominant average beat were measured by morphological techniques. If the P-wave was detected in the average beat, a smaller search window was defined for individual beats to exclude the potentially false P-waves. A set of P-wave features determined the presence of P-wave throughout an ECG segment. Our algorithm was tested on several datasets with annotated intervals for some cardiac rhythms. A decision tree ensemble of bagged trees classifier was developed and applied to the P-wave and interbeat interval features, resulting in AF/non-AF classification with average $F_{1}$ score of 96.0% in training subset and 95.6% in test subset of all records.
Machine Learning Based Classification of Myocardial Infarction Conditions Using Smartphone-Derived Seismo- and Gyrocardiography

In this paper, we attempt to classify the pre- and post-operation cardiac conditions of ST-elevation myocardial infarction (STEMI) utilizing seismocardiography (SCG) and gyrocardiography (GCG) signals recorded solely by a smartphone. SCG and GCG signals were recorded from 20 MI patients who were admitted to Emergency Department of Turku Hospital. Two measurements were recorded from each subject, one before they proceeded to percutaneous coronary intervention (pre-operation) and one afterwards (post-operation) with an average time interval of 2 days. Noise and artefact removal were applied to the signals and subsequently 25 features were extracted. Two classification algorithms, random forest (RF) and support vector machines (SVM), were deployed to discriminate the two cardiac conditions. Accuracy rates of 74% and 78% were obtained for RF and SVM, respectively. The results indicate that smartphone SCG-GCG based ischaemia analysis has clinical implications that warrants further investigations.
Sequential Electro-Anatomical Mapping Methodology and Preliminary Results for Reentry Vulnerability Index Estimation

Ventricular tachycardia (VT) recurrence after catheter ablation remains frequent and improved ablation strategies are needed. The re-entry vulnerability index (RVI) is an activation-repolarization marker to localize critical sites for VT initiation. Its use is limited since current electro-anatomical mapping systems (EAMS) cannot provide global measurement of activation and repolarization times within a single beat. We carried out a simulation study to assess a simple method to measure RVI using data collected by sequential EAMS and we investigated the effect of background noise, RT variability (σ RT) and ectopics on RVI estimation. The mean correlation coefficient between single ECG beats and a representative template is used as inclusion/exclusion criterion. Localization of the vulnerable region associated with 5% bottom RVI was accurate (sensitivity 80±8%, specificity> 99± 1%) for moderate to large repolarization variability (5 ≤ σRT ≤ 20 ms) and moderate level of noise (S N R ≥ 10 dB) but it deteriorated for σRT ≥ 25 ms and S N R < 5 dB. Sensitivity remained high even when RVI estimates were only moderately accurate (cc > 0.67 ± 0.05, MAE < 25 ± 1 ms). The number of ectopic beats did not affect the results. In the in-vivo case analyzed, the sites of low RVI and VT exit was close (5.1 mm).
Evaluation of Left Ventricular Diastolic Function Using 4D Flow MR Imaging

Aim: to 1) evaluate the ability of 4D flow MRI to assess left ventricular (LV) diastolic function, as compared to ultrasound (US), 2) analyse simultaneously LV filling and aortic stiffness in aging. Method: We studied 36 healthy subjects (52±18 years) who underwent MRI and US exams. A custom software was used for LV segmentation and calculation of transmitral flow-rate curves in a 4D flow MRI stack of slices located between the mitral valve and the apex and perpendicular to the LV long axis. Finally, the early (E) and late (A) flow-rate peaks were automatically detected and the E/A ratio was computed. Aortic pulse wave velocity (PWV) was also calculated from the 4D flow images. Results: A significant and high correlation was found between 4D flow and US E/A (R=0.71, p<0.001). Besides, a higher correlation was obtained between E/A and age when using 4D flow than US (E/A vs. age: R4D=-0.87, p<0.001, RUS =-0.69, p<0.001). Thanks to 4D flow MRI, simultaneous variation of aortic PWV and diastolic function with age was further illustrated. Conclusion: 4D flow MRI provided consistent LV E/A measurements in terms of comparison against reference US and associations with age. It also provided an insight into aorta/left heart coupling.
Robust H∞ output-feedback control of state-multiplicative stochastic systems with delay

Linear, state delayed, continuous-time systems are considered with both stochastic and norm-bounded deterministic uncertainties in the state-space model. The problem of robust dynamic H ∞ output-feedback control is solved, for the stationary case, via the input-output approach where the system is replaced by a nonretarded system with additional deterministic norm-bounded uncertainties. In this problem, a cost function is defined which is the expected value of the standard H ∞ performance cost with respect to the stochastic parameters. An example is given that demonstrates the applicability of the theory.
Detection of ST-Segment Variation in ECG Using Transfer Entropy

Many severe heart dysfunctions cause changes in the ST-segment of ECG. We hypothesize that the change in ST affects the degree of coupling between the QT time intervals and the heart rate (HR, inverse of the inter-beat interval RR). Therefore, we analyze the informational transfer between the coupled dynamics of QT and RR to detect the ST segment variation. We use the transfer entropy method allowing for a quantitative and uni-directional measure of coupling between two temporal processes. We analyze the data from ST variation patients and normal individuals. We show that the RR-to-QT information transfer for the ST group is larger than the corresponding transfer for the normal group. This indicates a larger degree of uncertainty in QT dependence on RR. Moreover, on average the segments of ST-episodes have the associated RR-to-QT transfer larger than the non-ST episodes. Finally, we demonstrate that the ratio between intra- and inter-subject diversity of the QT-RR relationship can have a characteristic value for the segments of ST episodes. We conclude that the degree of inter-dependence between QT and RR can be a marker of the ST variation pathology.
Praktijktesten: van onderzoeksmethode naar beleidsinstrument tegen discriminatie?

De voorbije jaren kwamen praktijktesten uitgebreid in de media. Academici brachten grootschalige discriminatie op de woning- en arbeidsmarkt aan het licht door middel van praktijktesten. Verschillende middenveldorganisaties en politici pleiten dan weer om praktijktesten te gebruiken om discriminerende bedrijven of verhuurders aan te pakken. Dit overzichtsartikel wil een brug slaan tussen beide werelden en nagaan in welke mate praktijktesten gebruikt kunnen worden als wetenschappelijke methode en als beleidsinstrument in de strijd tegen discriminatie. We maken eerst op basis van drie criteria een typologie op van de technieken om discriminerend gedrag te testen: situatietesten, correspondentietesten, mystery calls/visits en mystery mails. We staan hierbij telkens stil bij hun ontstaansgeschiedenis, toepassing en voor- en nadelen. Deze typologie is zeker niet algemeen aanvaard, maar ze kan toekomstig onderzoek conceptueel sturen. Vervolgens bespreken we hoever het onderzoek naar praktijktesten als beleidsinstrument staat. Er is beperkte wetenschappelijk evidentie dat praktijktesten in bepaalde omstandigheden en onder bepaalde voorwaarden effectief, haalbaar en ethisch verantwoord zijn. Wij zijn daarom voorzichtige voorstanders van het gebruik van praktijktesten als beleidsinstrument tegen discriminatie.
Geometric Wavelength for Monitoring Dynamic Changes in Repolarization Caused by Time-Varying Parameters

The autonomic nervous system modulates atrial activity, notably through acetylcholine (ACh)release. This time-dependent action may alter the dynamics of atrial arrhythmia, and in particular its wavelength. Wavelength is a critical factor that determines the vulnerability to reentry. It represents the spatial extent of refractory periods and may be estimated as the product of action potential duration and conduction velocity. When electrophysiological properties vary in space and/or time, and when conduction is anisotropic, that estimate may be inaccurate. We assessed an instantaneous geometric measure of wavelength applicable to monitoring the effect of externally-driven variations in ACh concentration at a shorter time scale than action potential duration. The method was applied to quantify the hysteresis in wavelength caused by release and degradation of ACh.
Effect of Tissue Elasticity in Cardiac Radiofrequency Catheter Ablation Models

Radiofrequency catheter ablation (RFCA) is an effective treatment for different types of cardiac arrhythmias. However, major complications can occur, including thrombus formation and steam pops. We present a full 3D mathematical model for the radiofrequency ablation process that uses an open-irrigated catheter and accounts for the tissue deformation, an aspect overlooked by the existing literature. An axisymmetric Boussinesq solution for spherical punch is used to model the deformation of the tissue due to the pressure of the catheter tip at the tissue-catheter contact point. We compare the effect of the tissue deformation in the RFCA model against the use of a standard sharp insertion of the catheter in the tissue that other state-of-the-art RFCA computational models use.
ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival. However, CPR must be interrupted for a reliable rhythm analysis because chest compressions (CCs) induce artifacts in the ECG. This paper introduces a double-stage shock advice algorithm (SAA) for a reliable rhythm analysis during manual CCs. The method used two configurations of the recursive least-squares (RLS) filter to remove CC artifacts from the ECG. For each filtered ECG segment over 200 shock/no-shock decision features were computed and fed into a random forest (RF) classifier to select the most discriminative 25 features. The proposed SAA is an ensemble of two RF classifiers which were trained using the 25 features derived from different filter configurations. Then, the average value of class posterior probabilities was used to make a final shock/no-shock decision. The dataset was comprised of 506 shockable and 1697 non-shockable rhythms which were labelled by expert rhythm resuscitation reviewers in artifact-free intervals. Shock/no-shock diagnoses obtained through the proposed double-stage SAA were compared with the rhythm annotations to obtain the Sensitivity (Se), Specificity (Sp) and balanced accuracy (BAC) of the method. The results were 93.5%, 96.5% and 95.0%, respectively.