Automated Channel Selection in High-Density sEMG for Improved Force Estimation.

Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of 30% for force estimation while reducing the dimensionality by 57% for a subset of three channels.

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices.

Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant's level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner's capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders' expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.

In-Distribution and Out-of-Distribution Self-Supervised ECG Representation Learning for Arrhythmia Detection.

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area. We then perform a comprehensive set of experiments using different augmentations and parameters to evaluate the effectiveness of various SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning, where we observe the best performance achieved by SwAV. Furthermore, our analysis shows that SSL methods achieve highly competitive results to those achieved by supervised state-of-the-art methods. To further assess the performance of these methods on both In-Distribution (ID) and Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and testing experiments. Our comprehensive experiments show almost identical results when comparing ID and OOD schemes, indicating that SSL techniques can learn highly effective representations that generalize well across different OOD datasets. This finding can have major implications for ECG-based arrhythmia detection. Lastly, to further analyze our results, we perform detailed per-disease studies on the performance of the SSL methods on the three datasets.

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data). Using a deep multimodal convolutional neural network, consisting of two CNN models, one with high-density (HD) EMG and one with motion data recorded by an Inertial Measurement Unit (IMU), our proposed TL technique significantly improved force modeling compared to leave-one-subject-out (LOSO) and even intra-subject scenarios. The TL approach increased the average R squared values of the force modeling task by 60.81%, 190.53%, and 199.79% compared to the LOSO case, and by 13.4%, 36.88%, and 45.51% compared to the intra-subject case for isotonic, isokinetic and dynamic conditions, respectively. These results show that it is possible to adapt to a new user with minimal data while improving performance significantly compared to the intra-subject scenario. We also show that TL can be used to generalize on a new experimental condition for a new user.

Analysis of MTHFR and MTRR Gene Polymorphisms in Iranian Ventricular Septal Defect Subjects.

Ventricular septal defect (VSD) is one of the most common types of congenital heart defects (CHD). There are vivid multifactorial causes for VSD in which both genetic and environmental risk factors are consequential in the development of CHD. Methionine synthase reductase (MTRR) and methylenetetrahydrofolate reductase (MTHFR) are two of the key regulatory enzymes involved in the metabolic pathway of homocysteine. Genes involved in homocysteine/folate metabolism may play an important role in CHDs. In this study; we determined the association of A66G and C524T polymorphisms of the MTRR gene and C677T polymorphism of the MTHFR gene in Iranian VSD subjects. A total of 123 children with VSDs and 125 healthy children were included in this study. Genomic DNA was extracted from the buccal cells of all the subjects. The restriction fragment length polymorphism polymerase chain reaction (PCR-RFLP) method was carried out to amplify the A66G and C524T polymorphism of MTRR and C677T polymorphism of MTHFR genes digested with Hinf1, Xho1 and Nde1 enzymes, respectively. The genotype frequencies of CC, CT and TT of MTRR gene among the studied cases were 43.1%, 40.7% and 16.3%, respectively, compared to 52.8%, 43.2% and 4.0%, respectively among the controls. For the MTRR A66G gene polymorphism, the genotypes frequencies of AA, AG and GG among the cases were 33.3%, 43.9% and 22.8%, respectively, while the frequencies were 49.6%, 42.4% and 8.0%, respectively, among control subjects. The frequencies for CC and CT genotypes of the MTHFR gene were 51.2% and 48.8%, respectively, in VSD patients compared to 56.8% and 43.2% respectively, in control subjects. Apart from MTHFR C677T polymorphism, significant differences were noticed (p < 0.05) in C524T and A66G polymorphisms of the MTRR gene between cases and control subjects.

Analysis of Gln223Agr polymorphism of Leptin Receptor Gene in type II diabetic mellitus subjects among Malaysians.

Leptin is known as the adipose peptide hormone. It plays an important role in the regulation of body fat and inhibits food intake by its action. Moreover, it is believed that leptin level deductions might be the cause of obesity and may play an important role in the development of Type 2 Diabetes Mellitus (T2DM), as well as in cardiovascular diseases (CVD). The Leptin Receptor (LEPR) gene and its polymorphisms have not been extensively studied in relation to the T2DM and its complications in various populations. In this study, we have determined the association of Gln223Agr loci of LEPR gene in three ethnic groups of Malaysia, namely: Malays, Chinese and Indians. A total of 284 T2DM subjects and 281 healthy individuals were recruited based on International Diabetes Federation (IDF) criteria. Genomic DNA was extracted from the buccal specimens of the subjects. The commercial polymerase chain reaction (PCR) method was carried out by proper restriction enzyme MSP I to both amplify and digest the Gln223Agr polymorphism. The p-value among the three studied races was 0.057, 0.011 and 0.095, respectively. The values such as age, WHR, FPG, HbA1C, LDL, HDL, Chol and Family History were significantly different among the subjects with Gln223Agr polymorphism of LEPR (p < 0.05).

Flow-Based Spatio-Temporal Structured Prediction of Motion Dynamics.

Conditional Normalizing Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and large interdimensional correlations, making them appealing for structured output learning. Their effectiveness in modelling multivariates spatio-temporal structured data has yet to be completely investigated. We propose MotionFlow as a novel normalizing flows approach that autoregressively conditions the output distributions on the spatio-temporal input features. It combines deterministic and stochastic representations with CNFs to create a probabilistic neural generative approach that can model the variability seen in high-dimensional structured spatio-temporal data. We specifically propose to use conditional priors to factorize the latent space for the time dependent modeling. We also exploit the use of masked convolutions as autoregressive conditionals in CNFs. As a result, our method is able to define arbitrarily expressive output probability distributions under temporal dynamics in multivariate prediction tasks. We apply our method to different tasks, including trajectory prediction, motion prediction, time series forecasting, and binary segmentation, and demonstrate that our model is able to leverage normalizing flows to learn complicated time dependent conditional distributions.

Deep Gait Recognition: A Survey.

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.

Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation.

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art. We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.

Convolutional Neural Network Approach for Elbow Torque Estimation during Quasi-dynamic and Dynamic Contractions.

Accurate torque estimation during dynamic conditions is challenging, yet an important problem for many applications such as robotics, prosthesis control, and clinical diagnostics. Our objective is to accurately estimate the torque generated at the elbow during flexion and extension, under quasi-dynamic and dynamic conditions. High-density surface electromyogram (HD-EMG) signals, acquired from the long head and short head of biceps brachii, brachioradialis, and triceps brachii of five participants are used to estimate the torque generated at the elbow, using a convolutional neural network (CNN). We hypothesise that incorporating the mechanical information recorded by the biodex machine, i.e., position and velocity, can improve the model performance. To investigate the effects of the added data modalities on the model accuracy, models are constructed that combine EMG and position, as well as EMG with both position and velocity. R2 values are improved by 2.35%, 37.50%, and 16.67%, when position and EMG are used as inputs to the CNN models, for isotonic, isokinetic, and dynamic cases, respectively compared to using only EMG. The model performances improves further by 2.29%, 12.12%, and 20.50% for isotonic, isokinetic, and dynamic conditions, when velocity is added with the EMG and position data.

CapsField: Light Field-Based Face and Expression Recognition in the Wild Using Capsule Routing.

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained facial image analysis, this technology has never been used for face and expression recognition in the wild. In this context, this paper proposes a new deep face and expression recognition solution, called CapsField, based on a convolutional neural network and an additional capsule network that utilizes dynamic routing to learn hierarchical relations between capsules. CapsField extracts the spatial features from facial images and learns the angular part-whole relations for a selected set of 2D sub-aperture images rendered from each LF image. To analyze the performance of the proposed solution in the wild, the first in the wild LF face dataset, along with a new complementary constrained face dataset captured from the same subjects recorded earlier have been captured and are made available. A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests. An extensive performance assessment study using the new datasets has been conducted for the proposed and relevant prior solutions, showing that the CapsField proposed solution achieves superior performance for both face and expression recognition tasks when compared to the state-of-the-art.

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched at enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models accurately detect the chronic stress exposure group (AUROC = 0.982 ± 0.002), the individual psychological stress score (R2 = 0.943 ± 0.009) and FSI at 34 weeks of gestation (R2 = 0.946 ± 0.013), as well as the maternal hair cortisol at birth reflecting chronic stress exposure (0.931 ± 0.006). The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The final DL model can be deployed in low-cost regular ECG biosensors as a simple, ubiquitous early stress detection and monitoring tool during pregnancy. This discovery should enable early behavioral interventions.

The future of simulation-based medical education: Adaptive simulation utilizing a deep multitask neural network.

BACKGROUND: In resuscitation medicine, effectively managing cognitive load in high-stakes environments has important implications for education and expertise development. There exists the potential to tailor educational experiences to an individual's cognitive processes via real-time physiologic measurement of cognitive load in simulation environments. OBJECTIVE: The goal of this research was to test a novel simulation platform that utilized artificial intelligence to deliver a medical simulation that was adaptable to a participant's measured cognitive load. METHODS: The research was conducted in 2019. Two board-certified emergency physicians and two medical students participated in a 10-minute pilot trial of a novel simulation platform. The system utilized artificial intelligence algorithms to measure cognitive load in real time via electrocardiography and galvanic skin response. In turn, modulation of simulation difficulty, determined by a participant's cognitive load, was facilitated through symptom severity changes of an augmented reality (AR) patient. A postsimulation survey assessed the participants' experience. RESULTS: Participants completed a simulation that successfully measured cognitive load in real time through physiological signals. The simulation difficulty was adapted to the participant's cognitive load, which was reflected in changes in the AR patient's symptoms. Participants found the novel adaptive simulation platform to be valuable in supporting their learning. CONCLUSION: Our research team created a simulation platform that adapts to a participant's cognitive load in real-time. The ability to customize a medical simulation to a participant's cognitive state has potential implications for the development of expertise in resuscitation medicine.

PCA-Based Channel Selection in High-Density EMG for Improving Force Estimation.

In this paper, a method for selecting channels to improve estimated force using fast orthogonal search (FOS) has been proposed. Surface electromyogram (sEMG) signals acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii, and brachioradialis during isometric contractions are used to estimate force induced at the wrist using the FOS algorithm. The method utilizes principle component analysis (PCA) in the frequency domain to select the channels with the highest contribution to the first principal component (PC). Our analysis demonstrates that our proposed method is capable of reducing the dimensionality of the data (the number of channels was reduced from 21 to 9) while improving the accuracy of the estimated force.

An Investigation of Dimensionality Reduction Techniques for EMG-based Force Estimation.

In this paper, extracted features in time and frequency domain, from high-density surface electromyogram (HD-sEMG) signals acquired from the long head and short head of biceps brachii, and brachioradialis during isometric elbow flexion are used to estimate force induced at the wrist using an artificial neural network (ANN). Different hidden layer sizes were considered to investigate its effect on the model accuracy. Also, we applied two dimensionality reduction techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), on the feature set and investigated their effects on force estimation accuracy.

Erratum to "Expression Profiling of Genes Related to Endothelial Cells Biology in Patients with Type 2 Diabetes and Patients with Prediabetes".

[This corrects the article DOI: 10.1155/2016/1845638.].

Flower extract of Allium atroviolaceum triggered apoptosis, activated caspase-3 and down-regulated antiapoptotic Bcl-2 gene in HeLa cancer cell line.

Cervical cancer accounts for the second most frequent cancer and also third leading cause of cancer mortality (15%) among women worldwide. The major problems of chemotherapeutic treatment in cervical cancer are non-specific cytotoxicity and drug resistance. Plant-derived products, known as natural therapies, have been used for thousands of years in cancer treatment with a very low number of side effects. Allium atroviolaceum is a species in the genus Allium and Liliaceae family, which could prove to have beneficial effects on cancer treatment, although there is a lack of corresponding attention. The methanolic extract from the A.atroviolaceum flower displayed marked anticancer activity on HeLa human cervix carcinoma cells with much lower cytotoxic effects on normal cells (3T3). The A.atroviolaceum extract induced apoptosis, confirmed by cell cycle arrest at the sub-G0 (apoptosis) phase, characteristic morphological changes, evident DNA fragmentation, observed by fluorescent microscope, and early and late apoptosis detection by Annexin V. Furthermore, down-regulation of Bcl-2 and activation of caspase-9 and -3 strongly indicated that the mitochondrial pathway was involved in the apoptosis signal pathway. Moreover, combination of A.atroviolaceum extract with doxorubicin revealed a significant reduction of IC50 and led to a synergistic effect. In summary, A.atroviolaceum displayed a significant anti-tumour effect through apoptosis induction in HeLa cells, suggesting that the A.atroviolaceum flower might have therapeutic potential against cervix carcinoma.

In vitro Antiproliferative and Apoptosis Inducing Effect of Allium atroviolaceum Bulb Extract on Breast, Cervical, and Liver Cancer Cells.

Natural products are considered potent sources for novel drug discovery and development. The multiple therapeutic effects of natural compounds in traditional medicine motivate us to evaluate the cytotoxic activity of bulb of Allium atroviolaceum in MCF7 and MDA-MB-231, HeLa and HepG2 cell lines. The bulb methanol extract of A. atroviolaceum was found to be an active cell proliferation inhibitor at the time and dose dependent manner. Determination of DNA content by flow cytometry demonstrated S and G2/M phase arrest of MCF-7 cell, correlated to Cdk1 downregulation, S phase arrest in MDA-MB-231 which is p53 and Cdk1-dependent, sub-G0 cell cycle arrest in HeLa aligned with Cdk1 downregulation, G0/G1, S, G2/M phase arrest in HepG2 which is p53-dependent. Apoptosis as the mechanism of cell death was confirmed by morphology study, caspases activity assay, as well as apoptosis related gene expression, Bcl-2. Caspase-8, -9, and -3 activity with downregulation of Bcl-2 illustrated occurrence of both intrinsic and extrinsic pathways in MCF7, while caspase-3 and -8 activity revealed extrinsic pathway of apoptosis, although Bcl-2 downregulated. In HeLa cells, the activity of caspase-9 and -3 and downregulation of Bcl-2 shows intrinsic pathway or mitochondrial pathway, whereas HepG2 shows caspase independent apoptosis. Further, the combination of the extract with tamoxifen against MCF7 and MDA-MB-231 and combination with doxorubicin against HeLa and HeG2 demonstrated synergistic effect in most concentrations, suggests that the bulb of A. atroviolaceum may be useful for the treatment of cancer lonely or in combination with other drugs.

Genetic Analysis of the Atrial Natriuretic Peptide Gene Polymorphisms among Essential Hypertensive Patients in Malaysia.

Background. Atrial natriuretic peptide (ANP) considerably influences blood pressure regulation through water and sodium homoeostasis. Several of the studies have utilized anonymous genetic polymorphic markers and made inconsequent claims about the ANP relevant disorders. Thus, we screened Insertion/Deletion (ID) and G191A polymorphisms of ANP to discover sequence variations with potential functional significance and to specify the linkage disequilibrium pattern between polymorphisms. The relationships of detected polymorphisms with EH with or without Type 2 Diabetes Mellitus (T2DM) status were tested subsequently. Method. ANP gene polymorphisms (I/D and A191G) were specified utilizing mutagenically separated Polymerase Chain Reaction (PCR) in 320 subjects including 163 EH case subjects and 157 controls. Result. This case-control study discovered a significant association between I/D polymorphisms of ANP gene in EH patient without T2DM. However, the study determined no association between G191A polymorphisms of ANP in EH with or without T2DM. In addition, sociodemographic factors in the case and healthy subjects exhibited strong differences (P < 0.05). Conclusion. As a risk factor, ANP gene polymorphisms may affect hypertension. Despite the small sample size in this study, it is the first research assessing the ANP gene polymorphisms in both EH and T2DM patients among Malaysian population.