scholarly journals Arrhythmia Classification of ECG Signals Using Hybrid Features

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Syed Muhammad Anwar ◽  
Maheen Gul ◽  
Muhammad Majid ◽  
Majdi Alnowami
Keyword(s):  
Heart Beat ◽  
Morphological Features ◽  
Frequency Resolution ◽  
Discrete Wavelet ◽  
Supraventricular Arrhythmia ◽  
Dynamic Feature ◽  
Dynamic Features ◽  
Hybrid Features ◽  
Rr Interval

Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.

scholarly journals A Detector for Premature Atrial and Ventricular Complexes

2021 ◽  
Vol 12 ◽  
Author(s):  
Guadalupe García-Isla ◽  
Luca Mainardi ◽  
Valentina D. A. Corino
Keyword(s):  
Binary Classification ◽  
Morphological Features ◽  
Discrete Wavelet ◽  
Supraventricular Arrhythmia ◽  
Low Sensitivity ◽  
Sensitivity Specificity ◽  
The Relationship ◽  
Ventricular Beat

The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers.

DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION

2017 ◽  
Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Viacheslav Krylenko ◽  
Viacheslav Krylenko
Keyword(s):  
Coastal Zone ◽  
Coastal Area ◽  
Economic Activity ◽  
Basic Information ◽  
Dynamic Features ◽  
Static State ◽  
Evolutionary Features ◽  
Coastal Features ◽  
The Stability

There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.

scholarly journals Heartbeat Classification Using Normalized RR Intervals and Morphological Features

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Chun-Min Yang
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Linear Prediction ◽  
Morphological Features ◽  
Rr Intervals ◽  
Heartbeat Classification ◽  
Linear Discriminant ◽  
Rr Interval ◽  
Signal Preprocessing ◽  
Ectopic Beats

This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

scholarly journals Classification of data stream based on dynamic feature extraction and neural network

2010 ◽  
Vol 30 (6) ◽  
pp. 1539-1542
Author(s):  
Cheng-liang WANG ◽  
Xu PANG ◽  
Zhi-jian LU ◽  
Chang-yin LUO
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Data Stream ◽  
Dynamic Feature

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

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