Biometrics of ECG Signal through Temporal Organization with Support Vector Machine

2021 ◽  
Author(s):  
Robert LeMoyne ◽  
Timothy Mastroianni
Keyword(s):  
Support Vector Machine ◽  
Temporal Organization ◽  
Support Vector ◽  
Ecg Signal

Support Vector Machine Based Sub-Classification of Arrhythmia Using ECG Signal

2020 ◽  
Author(s):  
Mohit Singh Dhaka ◽  
Poras Khetarpal ◽  
Neeraj Kumar
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal

Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

2020 ◽  
Author(s):  
Thamba Meshach W ◽  
Hemajothi S ◽  
Mary Anita E A
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Technologies ◽  
Support Vector ◽  
Affect Recognition ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Hybrid Features ◽  
Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

scholarly journals ELECTROCARDIOGRAM ARRHYTHMIA CLASSIFICATION SYSTEM USING SUPPORT VECTOR MACHINE BASED FUZZY LOGIC

2016 ◽  
Vol 9 (1) ◽  
pp. 35 ◽  
Author(s):  
Sugiyanto Sugiyanto ◽  
Tutuk Indriyani ◽  
Muhammad Heru Firmansyah
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Logic ◽  
Support Vector ◽  
Ecg Signal ◽  
Average Value ◽  
Sensitivity Level ◽  
Accuracy Level ◽  
Baseline Wander ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Arrhythmia is a cardiovascular disease that can be diagnosed by doctors using an electrocardiogram (ECG). The information contained on the ECG is used by doctors to analyze the electrical activity of the heart and determine the type of arrhythmia suffered by the patient. In this study, ECG arrhythmia classification process was performed using Support Vector Machine based fuzzy logic. In the proposed method, fuzzy membership functions are used to cope with data that are not classifiable in the method of Support Vector Machine (SVM) one-against-one. An early stage of the data processing is the baseline wander removal process on the original ECG signal using Transformation Wavelet Discrete (TWD). Afterwards then the ECG signal is cleaned from the baseline wander segmented into units beat. The next stage is to look for six features of the beat. Every single beat is classified using SVM method based fuzzy logic. Results from this study show that ECG arrhythmia classification using proposed method (SVM based fuzzy logic) gives better results than original SVM method. ECG arrhythmia classification using SVM method based fuzzy logic forms an average value of accuracy level, sensitivity level, and specificity level of 93.5%, 93.5%, and 98.7% respectively. ECG arrhythmia classification using only SVM method forms an average value accuracy level, sensitivity level, and specificity level of 91.83%, 91.83%, and 98.36% respectively.

scholarly journals Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

2018 ◽  
Vol 970 ◽  
pp. 012012 ◽  
Author(s):  
Arjon Turnip ◽  
M. Ilham Rizqywan ◽  
Dwi E. Kusumandari ◽  
Mardi Turnip ◽  
Poltak Sihombing
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Arrhythmia Detection

Feature extraction of ECG signal using a support vector machine

2013 ◽  
Vol 46 (4) ◽  
pp. e15
Author(s):  
I. Saini ◽  
V. Kumar ◽  
A. Khosla
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector ◽  
Ecg Signal

COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR FEATURE-BASED CLASSIFICATION USING HEART RATE VARIABILITY

2020 ◽  
Vol 32 (02) ◽  
pp. 2050009
Author(s):  
Kirti Tripath ◽  
Harsh Sohal ◽  
Shruti Jain
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
Support Vector ◽  
Ecg Signal ◽  
K Nearest Neighbor ◽  
Computer Aided ◽  
Feature Based

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.

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