A novel method of detection and classification of motor nerve late-wave activity

2011 ◽  
Author(s):  
D. Iyer ◽  
L. Zheng ◽  
M. L. Williams ◽  
B. H. Tracey ◽  
S. N. Gozani
Keyword(s):  
Motor Nerve ◽  
Wave Activity ◽  
Novel Method ◽  
Late Wave

scholarly journals A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4729 ◽  
Author(s):  
Taha Khan ◽  
Lina E. Lundgren ◽  
Eric Järpe ◽  
M. Charlotte Olsson ◽  
Pelle Viberg
Keyword(s):  
Random Forest ◽  
Blood Lactate ◽  
Change Point ◽  
Vastus Lateralis ◽  
Lactate Concentration ◽  
Treadmill Running ◽  
Vastus Lateralis Muscle ◽  
Semg Signal ◽  
Novel Method

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.

scholarly journals A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6491
Author(s):  
Le Zhang ◽  
Jeyan Thiyagalingam ◽  
Anke Xue ◽  
Shuwen Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Signal Amplitude ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Amplitude Change ◽  
Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

CLASSIFICATION OF SIMILAR 2-D OBJECTS BY WAVELET-SPARSE-MATRIX (WSM) METHOD

2001 ◽  
Vol 15 (02) ◽  
pp. 329-345 ◽  
Author(s):  
L. FENG ◽  
T. D. BUI ◽  
Y. Y. TANG
Keyword(s):  
Sparse Matrix ◽  
Zernike Moment ◽  
Fourier Descriptor ◽  
Moment Invariant ◽  
Spatial Features ◽  
Novel Method

This paper proposes a novel method called Wavelet-Sparse-Matrix (WSM) to extract the spatial features of 2-D objects for classifying objects that have subtle differences. The differences between these objects are present in the spatial orientations of the objects, or in the local positions of points on the contours of the objects. The separable wavelets are able to distinguish these differences and to separate them into three sparse subpatterns. Sparse matrix technique has the ability to rearrange nonzero elements in a sparse matrix by moving them as close together as possible. WSM method is a combination of these two techniques which can considerably improve the distinction of slightly dissimilar objects. Experiments are conducted, which include a series of discriminative simulations and comparisons with Fourier descriptor and Zernike moment invariant. These experiments verify the feasibility and effectiveness of the WSM method.

scholarly journals A novel method for achieving an optimal classification of the proteinogenic amino acids

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Andre Then ◽  
Karel Mácha ◽  
Bashar Ibrahim ◽  
Stefan Schuster
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Interaction Potential ◽  
Binary Separation ◽  
Multiple Alignments ◽  
Novel Method ◽  
Optimal Classification ◽  
Helix Propensity ◽  
Α Helix

Abstract The classification of proteinogenic amino acids is crucial for understanding their commonalities as well as their differences to provide a hint for why life settled on the usage of precisely those amino acids. It is also crucial for predicting electrostatic, hydrophobic, stacking and other interactions, for assessing conservation in multiple alignments and many other applications. While several methods have been proposed to find “the” optimal classification, they have several shortcomings, such as the lack of efficiency and interpretability or an unnecessarily high number of discriminating features. In this study, we propose a novel method involving a repeated binary separation via a minimum amount of five features (such as hydrophobicity or volume) expressed by numerical values for amino acid characteristics. The features are extracted from the AAindex database. By simple separation at the medians, we successfully derive the five properties volume, electron–ion-interaction potential, hydrophobicity, α-helix propensity, and π-helix propensity. We extend our analysis to separations other than by the median. We further score our combinations based on how natural the separations are.

scholarly journals An Unbiased Method for Clustering Bacterial Effectors Using Host Cellular Phenotypes

2013 ◽  
Vol 80 (3) ◽  
pp. 1185-1196 ◽  
Author(s):  
Andrea J. Dowling ◽  
David J. Hodgson
Keyword(s):  
Significant Advance ◽  
Content Type ◽  
Cluster Group ◽  
Homology Searching ◽  
Wide Range ◽  
Novel Method ◽  
Cellular Phenotypes ◽  
Bacterial Effectors

ABSTRACTWe present a novel method implementing unbiased high-content morphometric cell analysis to classify bacterial effector phenotypes. This clustering methodology represents a significant advance over more qualitative visual approaches and can also be used to classify, and therefore predict the likely function of, unknown effector genes from any microbial genome. As a proof of concept, we use this approach to investigate 23 genetic regions predicted to encode antimacrophage effectors located across the genome of the insect and human pathogenPhotorhabdus asymbiotica. Statistical cluster analysis using multiple cellular measures categorized treated macrophage phenotypes into three major groups relating to their putative functionality: (i) adhesins, (ii) cytolethal toxins, and (iii) cytomodulating toxins. Further investigation into their effects on phagocytosis revealed that several effectors also modulate this function and that the nature of this modulation (increased or decreased phagocytosis) is linked to the phenotype cluster group. Categorizing potential functionalities in this way allows rapid functional follow-up of key candidates for more-directed cell biological or biochemical investigation. Such an unbiased approach to the classification of candidate effectors will be useful for describing virulence-related regions in a wide range of genomes and will be useful in assigning putative functions to the growing number of microbial genes whose function remains unclear from homology searching.

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