scholarly journals A New Recongnition System Based on Gabor Wavelet Transform for Shockable Electrocardiograms

2021 ◽  
pp. 40-51
Author(s):  
Takayuki Okai ◽  
Shonosuke Akimoto ◽  
Hidetoshi Oya ◽  
Kazushi Nakano ◽  
Hiroshi Miyauchi ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Wavelet Transform ◽  
Recognition Performance ◽  
Sudden Cardiac Arrest ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Huge Number ◽  
Gabor Wavelet Transform ◽  
Feature Parameters ◽  
Spectrum Feature

This paper presents a new recognition system for shockable arrhythmias for patients suffering from sudden cardiac arrest. In order to develop the recognition system, lots of electrocardiogram (ECGs) have been analyzed by using gabor wavelet transform (GWT). Although, there is a huge number of spectrum feature parameters, recognition performance for all combinations for spectrum feature parameters are evaluated, and on the basis of the evaluation results, useful and effective spectrum features for ECGs are extracted. As a result, the proposed recognition system based on the selected effective spectrum feature parameters can achieved good performance comparing with the existing results.

scholarly journals Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sulayman Ahmed ◽  
Mondher Frikha ◽  
Taha Darwassh Hanawy Hussein ◽  
Javad Rahebi
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Swarm Optimization ◽  
Gabor Wavelet Transform ◽  
Face Recognition System

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

MEAN AND STANDARD DEVIATION AS FEATURES FOR PALMPRINT RECOGNITION BASED ON GABOR FILTERS

2011 ◽  
Vol 25 (04) ◽  
pp. 491-512 ◽  
Author(s):  
MEIRU MU ◽  
QIUQI RUAN
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Standard Deviation ◽  
Gabor Filter ◽  
Recognition Performance ◽  
Gabor Filters ◽  
Gabor Wavelet ◽  
Palmprint Recognition ◽  
Linear Discriminant ◽  
Gabor Wavelet Transform

The two-dimensional (2D) Gabor function has been recognized as a very useful tool in feature extraction of image, due to its optimal localization properties in both spatial and frequency domain. This paper presents a novel palmprint feature extraction method based on the statistics of decomposition coefficients of the Gabor wavelet transform. It is experimentally found that the magnitude coefficients of the Gabor wavelet transform within each subband uniformly to approximate the Lognormal distribution. Based on this fact, we create the palmprint representation using two simple statistics (mean and standard deviation) as feature components after applying the logarithmic transformation of Gabor filtered magnitude coefficients for each subband with different orientations and scales. The optimum setting of the number of Gabor filters and orientation of each Gabor filter is experimentally determined. For palmprint recognition, the popularly used Fisher Linear Discriminant (FLD) analysis is further applied on the constructed feature vectors to extract discriminative features and reduce dimensionality. All experiments are both executed over the CCD-based HongKong PolyU Palmprint Database of 7752 images and the scanner-based BJTU_PalmprintDB (V1.0) of 3460 images. The results demonstrate the effectiveness of the proposed palmprint representation in achieving the improved recognition performance.

EXTRACTION AND MATCHING OF SYMBOLIC CONTOUR GRAPHS

2003 ◽  
Vol 17 (07) ◽  
pp. 1279-1302 ◽  
Author(s):  
TINO LOURENS ◽  
ROLF P. WÜRTZ
Keyword(s):  
Wavelet Transform ◽  
Object Recognition ◽  
Experimental Evidence ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Model Matching ◽  
Gabor Wavelet Transform ◽  
Line Following

We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system invariant under translation, rotation and scaling. We provide experimental evidence and theoretical arguments that the matching complexity is below O(#V3), and show that the system is competitive with other graph-based matching systems.

Down Syndrome Diagnosis Based on Gabor Wavelet Transform

2011 ◽  
Vol 36 (5) ◽  
pp. 3205-3213 ◽  
Author(s):  
Şafak Saraydemir ◽  
Necmi Taşpınar ◽  
Osman Eroğul ◽  
Hülya Kayserili ◽  
Nuriye Dinçkan
Keyword(s):  
Down Syndrome ◽  
Wavelet Transform ◽  
Gabor Wavelet ◽  
Gabor Wavelet Transform ◽  
Syndrome Diagnosis

ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM

2019 ◽  
Vol 19 (01) ◽  
pp. 1940008 ◽  
Author(s):  
ÖZAL YILDIRIM
Keyword(s):  
Wavelet Transform ◽  
Recognition Performance ◽  
Relevant Information ◽  
Recognition System ◽  
Distinctive Features ◽  
Ecg Signals ◽  
Feature Vectors ◽  
Fast Recognition ◽  
Beat Detection ◽  
Learning Machine

Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.

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