scholarly journals An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

2019 ◽  
Vol 10 (1) ◽  
pp. 47-54
Author(s):  
Abdullah Jafari Chashmi ◽  
Mehdi Chehel Amirani
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Diseases ◽  
Selection Procedure ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Method ◽  
The Neural Network ◽  
Ecg Arrhythmia

Abstract Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
A. M. Umbrajkaar ◽  
A. Krishnamoorthy ◽  
R. B. Dhumale
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Condition Monitoring ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Variable Load ◽  
Combined Approach ◽  
Shaft Misalignment ◽  
Load Conditions

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

scholarly journals Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

2020 ◽  
Author(s):  
V. Vijayasarveswari ◽  
A.M. Andrew ◽  
M. Jusoh ◽  
T. Sabapathy ◽  
R.A.A. Raof ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
The Other ◽  
Ultra Wideband ◽  
Support Vector ◽  
Data Normalization ◽  
Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

CARDIAC ADVANCED ARRYTHMIA PREDICTION SYSTEMS- CAAPS

2021 ◽  
pp. 54-55
Author(s):  
Pradeep Kumar Radhakrishnan ◽  
Gayathri Ananyajyothi Ambat ◽  
Saihrudya Samhita ◽  
Murugan U S ◽  
Tarig Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Ventricular Arrhythmias ◽  
Deep Neural Network ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Term Memory

There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input. Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term memory (LSTM) can be outperformed. AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term memory,RNN recurrent neural network

Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kavitha Senthil ◽  
Vidyaathulasiramam
Keyword(s):  
Neural Network ◽  
Ovarian Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Cancer Diagnosis ◽  
Support Vector ◽  
Hybrid Neural Network ◽  
Cancer Prediction ◽  
The Neural Network

Abstract Objectives This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.

Classification of Medical Text Data Using Convolutional Neural Network-Support Vector Machine Method

2020 ◽  
Vol 10 (7) ◽  
pp. 1746-1753
Author(s):  
Lan Liu ◽  
Xiankun Sun ◽  
Chengfan Li ◽  
Yongmei Lei
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Data ◽  
Machine Method ◽  
Medical Text ◽  
Conventional Methods

Conventional methods of medical text data classification, neglect of context among different words and semantic information, has a poor text description, classification effect and generalization capability and robustness. To tackle the inefficiencies and low precision in the classification of medical text data, in this paper, we presented a new classification method with improved convolutional neural network (CNN) and support vector machine (SVM), i.e., CNN-SVM method. In the method, some convolution kernel filters that contribute greatly to the CNN model are first selected by the average response energy (ARE) value, and then used to simplify and reconstruct the CNN model. Next, the SVM classifier was optimized by firefly algorithm (FA) and context information to overcome the disadvantages of over-saturation and over-training in SVM classification. Finally, the presented CNN-SVM method is tested by the simulation experiment and the true classification of medical text data. The experimental results show that the presented CNN-SVM method in this paper can significantly reduce the complexity and amount of computation compared to the conventional methods, and further promote the computational efficiency and classification accuracy of medical text data.

Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis

2006 ◽  
Vol 532-533 ◽  
pp. 496-499
Author(s):  
Wangs Shen Hao ◽  
Xun Sheng Zhu ◽  
Jian Cai Zhao ◽  
Biao Jun Tian
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Support Vector Machine Algorithm ◽  
The Neural Network ◽  
Vector Machines ◽  
Diagnosis Accuracy

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved.

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