To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities dynamics EMG-force relationship are needed. In this work, angle-based calibration parallel cascade identification (PCI) combined for EMG-based force estimation in dynamic contractions, including concentric eccentric contractions biceps brachii triceps muscles. Angle-based has been shown to improve surface (SEMG) based...

This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning electrocardiogram (ECG) signals and identifies best parameters. The baseline our proposed framework consists two main parts: downstream task. In first stage, we train an encoder using a number to extract generalizable ECG signal representations. We then freeze finetune few linear layers with different amounts labelled data arrhythmia detection. experiment techniques...

We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored identification. To enhance model's adaptability to unseen subjects without extensive retraining, we introduce an attention-based adapter network fine-tuning, which reduces computational cost of adapting model new...

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting novel analysis data distributions on three popular ECG-based datasets: PTB-XL, Chapman, and Ribeiro. To best our knowledge, study is first to quantitatively explore characterize these in area. then perform comprehensive set experiments using different augmentations parameters evaluate various SSL methods,...

The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency as inputs feed-forward backpropagation neural networks (FBNN). proposed had better results with 98.25% accuracy compared previously studied methods such wavelet transform, entropy, logistic regression and Lyapunov exponent. (4 pages)

In this paper a new approach to accurately classify ECG arrhythmias through combination of the wavelet transform and artificial neural network is presented. Three kinds features in very computationally efficient manner are computed as follows: 1-Joint time-frequency (discrete coefficients). 2-Time domain (R-R intervals). 3-Statistical feature (form factor). Using these features, limitations other methods classifying multiple arrhythmia with high accuracy for all them at once overcome....

Performance of the algorithms which process intracardiac electrograms (IEGMs) highly depends on accuracy estimating times that electrical waves pass area under electrodes. Estimating these activation (ATs) from IEGMs during atrial fibrillation (AF) is extremely challenging as activities atria are very complex, non-stationary, and irregular. In this paper, we propose a new detector based test equality variance two sets data. At any time t, consider IEGM data: 1) data in bounded interval...

A new approach for person identification based on hand geometry is presented. After preprocessing features are extracted from a photograph taken while user has placed his/her (either left or right) the platform of document scanner with no limits fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), Radial basis function neural networks (RBF), Multi layer perceptron (MLP), k-Nearest Neighbor (k-NN), Bayes method and mahalanobis/Hamming distance have been...

Substrate based catheter ablation strategies are widely employed for treatment of scar-related ventricular tachycardia (VT). We analysed intracardiac electrograms (EGMs) from close-coupled paced extrastimuli extracted the EnSite Precision mapping system. sought to characterize EGM responses myocardium varying coupling intervals right apex (RVA) in both healthy individuals and patients presenting with VT ablation.Extrastimuli were delivered RVA after estimation effective refractory period....

A calibration method is proposed to compensate for the changes in surface electromyogram (SEMG) amplitude level of biceps brachii at different joint angles due movement muscle bulk under EMG electrodes a constant force level. To this end, an experiment was designed, and SEMG measurements were collected from 5 subjects. The fast orthogonal search (FOS) used find mapping between recorded wrist. Comparison evaluation values models trained with calibrated non-calibrated signals revealed...

Parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface electromyography recordings from upper-arm muscles the elbow-induced force at wrist. PCI mapping composed of parallel connection linear and nonlinear static blocks. Experimental comparison between previously published orthogonalization scheme has shown superior prediction by PCI. The improved performance attributed structural capability in capturing effects generated force.

Cardiac mapping systems are based on the time/frequency feature analyses of intracardiac electrograms recorded from individual bipolar/unipolar electrodes. Signals each electrode processed independently. Such approaches fail to investigate interrelationship between simultaneously channels any given catheter during atrial fibrillation (AF). We introduce a novel signal processing technique that reflects regional dominant frequency (RDF) components. show RDF can be used identify and...

Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the targets VT patients are done manually, and therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present fully automated detection algorithm for with which expands on previously defined features substantially decreases associated study times. Paced electrogram signals were...

Abstract Background Current noninvasive risk stratification methods offer limited prediction of arrhythmic events when selecting patients for ICD implantation. Our laboratory has recently developed a signal processing metric called Layered Symbolic Decomposition frequency (LSDf) that quantifies the percentage hidden QRS wave components in signal‐averaged ECG (SAECG) recordings. The purpose this pilot study was to determine whether LSDf can be predictive ventricular arrhythmia or death an...

This paper presents a novel approach for automatic ECG delineation with focus on QT interval estimation, using an unsupervised learning algorithm. A three-dimensional feature space is created which uses the characteristics of waveform at its inflection points. To this end, three features are introduced, including Truncated Energy, makes our method robust to baseline wandering and noise. Using fact that logarithm exhibits mixture four Gaussian distributions, each one P wave, QRS complex, T...

Atrial fibrillation (AF) is a major global health issue as it the most prevalent supraventricular arrhythmia. Multiple ectopic electrical sources in atria are believed to sustain AF. Catheter-based ablation of these considered an effective AF treatment. Based on Hough transform (HT), we propose general framework that processes atrial intracardiac electrograms (IEGMs) localize tip source with spiral wavefront shape. Using locations catheter's electrodes and activation times IEGMs, provide...

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu an abstract DEVELOPMENT OF AN INTERACTIVE WEB-BASED ENVIRONMENT FOR MEASUREMENT HARDNESS IN METALS J. Hashemi, E.E. Anderson, N. Chandrashekar Texas Tech University Department Mechanical Engineering An interactive web-based experiment was designed as a preparation tool for students the Materials Mechanics Laboratory course at University. In web- based experiment, were given introduction to...

Catheter ablation therapy has become a key intervention in treatment of recurrent Ventricular Tachycardia (VT).We propose an automated fractionation detection method for VT patients which can potentially increase the accuracy and success rate therapy.A train pacing with three different timings, close to ventricular effective refractory period (VERP), was introduced from right ventricle (RV) apex; surface intracardiac activations were recorded sites RV 10 (5 tests, 5 control).Data processed...

The performance of any intracardiac electrogram processing method is limited by the accuracy its activation detection approach. most common approaches in literature aim to find highest peak envelope disregarding start and end points. However, duration can be used extract useful information such as wave collisions. In this work, we propose a novel orthogonal based approach for fast accurate estimation activations (activation envelope) recordings during atrial fibrillation. Wavelet...