Javad Hashemi

EMG–force modeling using parallel cascade identification

Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation. In this study, parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface EMG recordings from upper-arm muscles to the induced force at the wrist. PCI mapping involves generating a parallel connection of a series of linear dynamic and nonlinear static blocks. The PCI model parameters were initialized to obtain the best force prediction. A comparison between PCI and a previously published Hill-based orthogonalization scheme, that captures physiological behaviour of the muscles, has shown 44% improvement in force prediction by PCI (averaged over all subjects in relative-mean-square sense). The improved performance is attributed to the structural capability of PCI to capture nonlinear dynamic effects in the generated force.

Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling

To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process. SEMG data recorded during constant force, constant velocity, and varying force, varying velocity flexion and extension trials are calibrated. The calibration values are obtained at specific elbow joint angles and interpolated to cover a continuous range of joint angles. The calibrated data are used in PCI models to estimate the force induced at the wrist. The experimental results show the effectiveness of the calibration scheme, combined with PCI modeling. For the constant force, constant velocity trials, minimum %RMSE of 8.3% is achieved for concentric contractions, 10.3% for eccentric contractions and 33.3% for fully dynamic contractions. Force estimation accuracy is superior in concentric contractions in comparison to eccentric contractions , which may be indicative of more nonlinearity in the eccentric SEMG-force relationship.

Biometric Identification through Hand Geometry

A new approach for person identification based on hand geometry is presented. After preprocessing hand features are extracted from a photograph taken while user has placed his/her hand (either left or right) on the platform of a document scanner with no limits or fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), radial basis function neural networks (RBF), multilayer perceptron (MLP), k-nearest neighbor (k-NN), Bayes method and Mahalanobis/Hamming distance have been used in classification section. Experimental results show a rate of success above 90%

Activation detection of intracardiac electrogram during atrial fibrillation based on the variance equality test

Performance of the algorithms which process intracardiac electrograms (IEGMs) highly depends on the accuracy of estimating the times that electrical waves pass the area under the electrodes. Estimating these activation times (ATs) from IEGMs during atrial fibrillation (AF) is extremely challenging as electrical activities of atria are very complex, non-stationary, and irregular. In this paper, we propose a new activation detector which is based on the test of the equality of variance of two sets of data. At any time t, we consider two sets of IEGM data: 1) data in a bounded interval around t, 2) data in bounded intervals around the first interval. We show that the activation zone can be extracted by comparing the variance of these two sets, i.e., we introduce a new preprocessing approach and show that it can effectively highlight activation zones of IEGMs. Our simulation results on bipolar atrial IEGMs gathered during AF confirm the efficiency of the proposed preprocessing method.

Surface EMG force modeling with joint angle based calibration.

In this paper, a calibration method to compensate for changes in SEMG amplitude with joint angle is introduced. Calibration factors were derived from constant amplitude surface electromyogram (SEMG) recordings from the biceps brachii (during elbow flexion) and the triceps brachii (during elbow extension) across seven elbow joint angles. SEMG data were then recorded from the elbow flexors (biceps brachii and brachioradialis) and extensors (triceps brachii) during isometric, constant force flexion and extension contractions at the same joint angles. The resulting force at the wrist was measured. The fast orthogonal search method was used to find a mapping between the system inputs - estimated SEMG amplitudes and joint angle - and the system output - measured force, for both calibrated and non-calibrated SEMG data. Models developed with calibrated data yielded a statistically significant improvement in force estimation compared to models developed with non-calibrated data, suggesting that the calibration method can compensate for changes in the SEMG-force relationship with changing joint angle. It was also found that the number of non-linear, joint angle-dependent terms used in the SEMG-force model was reduced with calibration. Additionally, initial inter-session analysis performed for four subjects suggests that calibration values can be used for subsequent recording sessions, and different output force levels.

Joint angle-based EMG amplitude calibration

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

Localization of the ectopic spiral electrical source using intracardiac electrograms during atrial fibrillation

Atrial fibrillation (AF) is a major global health issue as it is the most prevalent supraventricular arrhythmia. Multiple ectopic electrical sources in the atria are believed to sustain AF. Catheter-based ablation of these sources is considered an effective AF treatment. Based on the Hough transform (HT), we propose a general framework that processes the atrial intracardiac electrograms (IEGMs) to localize the tip of an ectopic source with a spiral wavefront shape. Using the locations of the catheters electrodes and the activation times of the IEGMs, we provide a method that can estimate the location of the tip of a spiral wavefront to be eliminated by ablation. By providing various examples, it is shown that the proposed method can accurately localize the tip of the spiral rotor.

Dynamic modeling of EMG-force relationship using parallel cascade identification

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

Regional Dominant Frequency: A New Tool for Wave Break Identification During Atrial Fibrillation

Cardiac mapping systems are based on the time/frequency feature analyses of intracardiac electrograms recorded from individual bipolar/unipolar electrodes. Signals from each electrode are processed independently. Such approaches fail to investigate the interrelationship between simultaneously recorded channels of any given mapping catheter during atrial fibrillation (AF). We introduce a novel signal processing technique that reflects regional dominant frequency (RDF) components. We show that RDF can be used to identify and characterize variation and disorganization in wavefront propagation- wave breaks. The intracardiac electrograms from the left atrium of 15 patients were exported to MATLAB and custom software employed to estimate RDF and wave break rate (WBR). We observed a heterogeneous distribution of both RDF and WBR; the two measures were weakly correlated (0.3; p < 0.001). We identified locations of AF or atrial tachycardia (ATach) termination and later compared offline with RDF and WBR maps. We inspected our novel metrics for associations with AF termination sites. Areas associated with AF termination demonstrated high RDF and low WBR (↑RDF,↓WBR). These sites were present in 14 of 15 patients (mean 2.6 ± 1.2 sites per patient; range, 1–4 sites), 43% situated within the pulmonary veins. In nine patients where AF terminated to sinus rhythm (6) or ATach (3), post-hoc analysis demonstrated all ↑RDF,↓WBR sites were ablated and correlated with AF termination sites. The proposed RDF signal processing tools can be used to identify and quantify wave break, and the combined use of these two novel metrics can aid characterization of AF. Further prospective studies are warranted.

Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation

OBJECTIVE In this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation. METHODS First, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distinguishing between active and inactive intervals of IEGMs. Then, we show that the natural logarithm of features corresponding to active and inactive intervals exhibits a mixture of two Gaussian distributions in three dimensional feature space. An Expectation Maximization algorithm for Gaussian mixtures is then applied for automatic clustering of the features into two categories. RESULTS The absolute error in onset and offset estimation of active intervals is 6.1ms and 10.7ms, respectively, guaranteeing a high resolution. The true positive rate for the proposed method is also 98.1%, proving the high reliability. CONCLUSION The proposed method can extract the active intervals of IEGMs during AF with a high accuracy and resolution close to manually annotated results. SIGNIFICANCE In contrast with some of the conventional methods, no windowing technique is required in our approach resulting in significantly higher resolution in estimating the onset and offset of active intervals. Furthermore, since the signal characteristics at inflection points are analyzed instead of signal samples, the computational time is significantly low, ensuring the real-time application of our algorithm. The proposed method is also robust to noise and baseline variations thanks to the Laplacian of Gaussian filter employed for extraction of inflection points.