Journal ArticleDOI
Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis
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TLDR
Fractal analysis is explored to parameterize the temporal and spectral variability of normal and abnormal VAG signals to help in the detection and monitoring of knee-joint pathology.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2013-01-01. It has received 73 citations till now. The article focuses on the topics: Fractal analysis & Fractal dimension.read more
Citations
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Journal ArticleDOI
Finding the Best Classification Threshold in Imbalanced Classification
TL;DR: The drawbacks of using ROC as the sole measure of imbalance in data classification problems are analyzed and a novel framework for finding the best classification threshold is proposed.
Journal ArticleDOI
Feature selection and classification methodology for the detection of knee-joint disorders
TL;DR: This work investigates VAG signals by proposing a wavelet based decomposition and indicates that the classification accuracy was more prominent with features selected from FS algorithms.
Journal ArticleDOI
Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion
TL;DR: A novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance of knee joint vibration signals and extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and theNumber of significant signal turns detected with the fixed threshold in the time domain.
Journal ArticleDOI
An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology
Ganesh R. Naik,S. Easter Selvan,Sridhar P. Arjunan,Amit Acharyya,Dinesh Kumar,Arvind Ramanujam,Hung T. Nguyen +6 more
TL;DR: The outcome of this study is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.
Journal ArticleDOI
Classification of Knee Joint Vibration Signals Using Bivariate Feature Distribution Estimation and Maximal Posterior Probability Decision Criterion
TL;DR: The kernel-based probability density estimation method was used to model the distributions of the VAG signals recorded from healthy subjects and patients with knee joint disorders and demonstrated the merits of the bivariate feature distribution estimation and the superiority of the maximal posterior probability decision criterion for analysis of knee joint V AG signals.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
The Fractal Geometry of Nature
TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.
Journal ArticleDOI
Basic principles of ROC analysis
TL;DR: ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making and the concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error.
Related Papers (5)
Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions.
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Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions
Rangaraj M. Rangayyan,Yunfeng Wu +1 more
Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows
Rangaraj M. Rangayyan,Yunfeng Wu +1 more