Evaluation of Karhunen-Loeve expansion for feature selection in computer-assisted classification of bioprosthetic heart-valve status

1999 ◽  
Vol 37 (4) ◽  
pp. 504-510 ◽  
Author(s):  
M. Yazdanpanah ◽  
L. Allard ◽  
L-G. Durand ◽  
R. Guardo
Keyword(s):  
Feature Selection ◽  
Heart Valve ◽  
Computer Assisted ◽  
Bioprosthetic Heart Valve

scholarly journals Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

2010 ◽  
Vol 61 (2) ◽  
pp. 93-99 ◽  
Author(s):  
Ganesh Naik ◽  
Dinesh Kumar
Keyword(s):  
Feature Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Signal Classification ◽  
Computer Assisted ◽  
Single Shot ◽  
Myoelectric Signal ◽  
Novel Method ◽  
Selection For

Hybrid Feature Selection for Myoelectric Signal Classification Using MICA This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

scholarly journals MECHANISMS UNDERLYING BIOPROSTHETIC HEART VALVE DYSFUNCTIONS

2018 ◽  
Vol 7 (2) ◽  
pp. 10-24 ◽  
Author(s):  
L. S. Barbarash ◽  
N. V. Rogulina ◽  
N. V. Rutkovskaya ◽  
E. A. Ovcharenko
Keyword(s):  
Heart Valve ◽  
Heart Valves ◽  
Negative Impact ◽  
Natural Aging ◽  
Bioprosthetic Heart Valve ◽  
Negative Effects ◽  
Related Factors ◽  
Bioprosthetic Heart Valves ◽  
Pathogenetic Mechanisms

The article presents new insights into the mechanisms underlying bioprosthetic heart valve dysfunctions based on the medical literature analysis. We highlighted the main pathogenetic mechanisms causing dysfunctions of bioprosthetic heart valves among the well-known and recently studied ones. In addition to the process of natural “aging” of the valve tissue that develops during continuous cyclic mechanical loads and is accompanied by the formation of calcification foci (passive and active calcification process), the negative impact of prosthesis- and recipientrelated factors has been evaluated. The prosthesis-related factors contributing to the development of dysfunctions include technological and technical factors, which may produce negative effects on bioprosthetic heart valves during the preimplantation preparation and implantation itself. Main dysmetabolic, immune, hemostasis and hyperproliferative (hyperplastic) mechanisms have been reviewed from the standpoint of the recipient-related factors that may shorten the lifespan of bioprostheses. Therefore, we propose a classification of bioprosthetic heart valve dysfunctions based on the underlying pathogenetic mechanisms and specific morphological patterns.

Prevention of Bioprosthetic Heart Valve Calcification by Ethanol Preincubation

Circulation ◽  
1997 ◽  
Vol 95 (2) ◽  
pp. 479-488 ◽  
Author(s):  
Narendra Vyavahare ◽  
Danielle Hirsch ◽  
Eyal Lerner ◽  
Jonathan Z. Baskin ◽  
Frederick J. Schoen ◽  
...  
Keyword(s):  
Heart Valve ◽  
Bioprosthetic Heart Valve ◽  
Valve Calcification

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For

scholarly journals An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

2021 ◽  
Vol 11 (15) ◽  
pp. 6983
Author(s):  
Maritza Mera-Gaona ◽  
Diego M. López ◽  
Rubiel Vargas-Canas
Keyword(s):  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Eeg Signals ◽  
Specificity And Sensitivity ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Stability ◽  
Selection Algorithms ◽  
Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

Sign in / Sign up

Export Citation Format

Share Document