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.

scholarly journals Face–Iris Multimodal Biometric Identification System

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Basma Ammour ◽  
Larbi Boubchir ◽  
Toufik Bouden ◽  
Messaoud Ramdani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Gabor Filter ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Evaluation Process ◽  
Relevant Information ◽  
Recognition System ◽  
Identification System ◽  
New Feature

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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 Weighted Module Linear Regression Classifications for Partially-Occluded Face Recognition

2021 ◽  
Author(s):  
Wei-Jong Yang ◽  
Cheng-Yu Lo ◽  
Pau-Choo Chung ◽  
Jar Ferr Yang
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Recognition Performance ◽  
Recognition System ◽  
Face Image ◽  
Fast Recognition ◽  
The Face ◽  
Robust Face Recognition ◽  
Partially Occluded ◽  
Linear Regression Classification

Face images with partially-occluded areas create huge deteriorated problems for face recognition systems. Linear regression classification (LRC) is a simple and powerful approach for face recognition, of course, it cannot perform well under occlusion situations as well. By segmenting the face image into small subfaces, called modules, the LRC system could achieve some improvements by selecting the best non-occluded module for face classification. However, the recognition performance will be deteriorated due to the usage of the module, a small portion of the face image. We could further enhance the performance if we can properly identify the occluded modules and utilize all the non-occluded modules as many as possible. In this chapter, we first analyze the texture histogram (TH) of the module and then use the HT difference to measure its occlusion tendency. Thus, based on TH difference, we suggest a general concept of the weighted module face recognition to solve the occlusion problem. Thus, the weighted module linear regression classification method, called WMLRC-TH, is proposed for partially-occluded fact recognition. To evaluate the performances, the proposed WMLRC-TH method, which is tested on AR and FRGC2.0 face databases with several synthesized occlusions, is compared to the well-known face recognition methods and other robust face recognition methods. Experimental results show that the proposed method achieves the best performance for recognize occluded faces. Due to its simplicity in both training and testing phases, a face recognition system based on the WMLRC-TH method is realized on Android phones for fast recognition of occluded faces.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

Sulphonamide: Distribution, Toxicology, Environmental Characteristics, and Analysis - A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Kirubanandam Grace Pavithra ◽  
Vasudevan Jaikumar ◽  
Ponnusamy Senthil Kumar ◽  
PanneerSelvam SundarRajan
Keyword(s):  
Aqueous Solution ◽  
High Performance Liquid Chromatography ◽  
Capillary Electrophoresis ◽  
Liquid Chromatography ◽  
High Performance ◽  
Uv Spectroscopy ◽  
Relevant Information ◽  
Fluorometric Determination ◽  
Distinctive Features ◽  
The World

Background: Many antibiotics were widely used as medication based on their distinctive features. Among them, sulphonamides were commonly used, however their recalcitrant nature makes them difficult to dispose. Hence, their interaction with environment and analytic technique requires considerable attention globally. Objective: Therefore, this review aimed to provide detailed discussion about environmental as well as human health behaviour and analytic techniques corresponding to sulphonamides. Methods: Various results and discussion were extracted from technical journals and books published by different researchers from all over the world. The cited bibliographic references were intentionally investigated in order to extract relevant information related to proposed work. Results: In this review, the determination techniques such as UV-spectroscopy, Enthalpimetry, Immunosensor, Chromatography, Chemiluminescence, Photoinduced fluorometric determination, Capillary electrophoresis for sulphonamide determination were discussed in detail. Among them, High performance liquid chromatography (HPLC) and UV-spectroscopy was effective and extensively used for screening sulphonamide. Conclusion: Knowing the quantification and behaviour of sulphonamide in aqueous solution is mandatory to opt the suitable wastewater treatment required. Hence, choosing appropriate high precision and feasible screening techniques is necessary, which can be attained with this review.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

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