scholarly journals Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 148
Author(s):  
Nuryani Nuryani
Keyword(s):  
Atrial Fibrillation ◽  
Radial Basis Function ◽  
Basis Function ◽  
Paroxysmal Atrial Fibrillation ◽  
Experimental Result ◽  
Support Vector ◽  
Rr Intervals ◽  
Vector Method ◽  
Radial Basis ◽  
New Strategy

Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

scholarly journals Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 763 ◽  
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru ◽  
Sun-Hee Kim
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Classification Accuracy ◽  
Approximate Entropy ◽  
Cardiovascular Disorder ◽  
Signal Classification ◽  
Support Vector ◽  
Cardiovascular Disorders ◽  
Cvd Risk ◽  
Radial Basis

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

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