scholarly journals Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Mingfeng Jiang ◽  
Shanshan Jiang ◽  
Lingyan Zhu ◽  
Yaming Wang ◽  
Wenqing Huang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Body Surface ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Regression Problem ◽  
Generalization Performance ◽  
Surface Potentials ◽  
Transmembrane Potentials

The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals Determination of the excitation origin in the ventricles from the ECG using support vector regression

2017 ◽  
Vol 3 (2) ◽  
pp. 257-260
Author(s):  
Nicolas Pilia ◽  
Christian Ritter ◽  
Danila Potyagaylo ◽  
Walther H. W. Schulze ◽  
Olaf Dössel ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Functional Relation ◽  
Support Vector ◽  
Transmembrane Potentials ◽  
Surface Ecg ◽  
Machine Learning Approach ◽  
Ill Posed ◽  
Ectopic Beats ◽  
Forward Calculation ◽  
Future Work

AbstractA common treatment of focal ventricular tachycardia is the catheter ablation of triggering sites. They have to be found manually by the physician during an intervention in a catheter lab. Thus, a method for determining the position of the focus automatically is desired. The inverse problem of electrocardiography addresses this problem by reconstructing the source of the ectopic beats using the surface ECG. This problem is ill-posed and therefore needs specific methods for solving it. We propose a machine learning approach for localisation of the ectopic foci in the heart to assist cardiologists with their therapy planning.We simulated 600 120-lead ECGs with different known excitation origins in the heart using a cellular automaton followed by a forward calculation. Features from the ECGs were used as input for a support vector regression (SVR). We assumed a functional relation between features from the ECG and the excitation origin. To benchmark SVR, we also used the well-known Tikhonov 0th order regularisation to reconstruct the transmembrane potentials in the heart and detect the location of the ectopic foci. Parameters for SVR and regularisation were chosen using a grid search minimising the error between estimated and true excitation origin. Compared to the Tikhonov regularisation method, SVR achieved a smaller deviation between estimated and real excitation origin evaluated with 6-fold cross validation. Future work could investigate on the behaviour on data from simulations with other torso and electrophysiological models, the influence of other methods for feature extraction and finally the evaluation with clinical data.

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

2016 ◽  
Vol 25 (8) ◽  
pp. 1248-1258 ◽  
Author(s):  
Fayçal Megri ◽  
Ahmed Cherif Megri ◽  
Riadh Djabri
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Support Vector Regression ◽  
Thermal Sensation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Statistical Regression ◽  
Swarm Optimization ◽  
Comfort Indices

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

scholarly journals Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

2016 ◽  
Vol 10 (7) ◽  
pp. 29 ◽  
Author(s):  
Jaber Soltani ◽  
Moosa Kalanaki ◽  
Mohammad Soltani
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Failure Rates ◽  
Input Output ◽  
Generalization Capability ◽  
Comparative Tests

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

scholarly journals A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Optimization Methods ◽  
Parameters Optimization ◽  
Least Square ◽  
Movement Direction ◽  
Support Vector ◽  
Machine Model ◽  
Mode Decomposition

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

Research in Analysis of Asphalt Pavement Performance Evaluation Based on PSO-SVM

2011 ◽  
Vol 97-98 ◽  
pp. 203-207 ◽  
Author(s):  
Ke Zhen Yan ◽  
Zou Zhang
Keyword(s):  
Performance Evaluation ◽  
Asphalt Pavement ◽  
Pso Algorithm ◽  
Condition Index ◽  
Pavement Performance ◽  
Small Samples ◽  
Support Vector ◽  
Regression Problem ◽  
Evaluation Indexes ◽  
Pavement Condition Index

An emerging machine learning technique, the support vector machine (SVM), based on statistical learning theory is very good at analyzing small samples and non-linear regression problem. The particle swarm optimize (PSO) can avoid the man-made blindness and enhance the efficiency and capability in forecasting. In this paper, SVM is applied to establish a model for asphalt pavement performance evaluation, optimized by PSO algorithm. In road engineering, PCI, SSI, SRI and IRI were selected as the asphalt pavement performance evaluation indexes, but it is difficult to get pavement condition index. This paper describes the relationships among the four indicators, and SSI, SRI and IRI were used for establishing the prediction model to forecast PCI based on PSO-SVM. The results show that the method is simple and effective for evaluation of asphalt pavement performance.

Support Vector Regression on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 494-495 ◽  
pp. 964-967
Author(s):  
Xiao Li Yang ◽  
Yan Fang Li ◽  
Xing Wang Zhang ◽  
Shi Qiang Hu
Keyword(s):  
Support Vector Regression ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Support Vector Regression Model ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Spectrometry Technique

We studied rapid moisture determination in lignitic coal samples using near-infrared (NIR) spectrometry technique. This research applied support vector regression (SVR) and discrete wavelet transform (DWT) to analyze NIR spectra. Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build support vector regression model. Through parameters optimization, the results show that DWT-SVR can obtain satisfactory performance for moisture determination in lignitic coal samples.

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