scholarly journals Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

2014 ◽  
Vol 24 (3) ◽  
pp. 647-655 ◽  
Author(s):  
Hoai Linh Tran ◽  
Van Nam Pham ◽  
Hoang Nam Vuong
Keyword(s):  
Decision Tree ◽  
Support Vector ◽  
Ecg Signal ◽  
Signal Recognition ◽  
Massachusetts Institute Of Technology ◽  
Binary Decision ◽  
Ecg Signals ◽  
Binary Decision Tree ◽  
Institute Of Technology ◽  
Takagi Sugeno

Abstract The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution

Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

2019 ◽  
Vol 34 (03) ◽  
pp. 2050003
Author(s):  
Selda Güney ◽  
Ayten Atasoy
Keyword(s):  
Decision Tree ◽  
Electronic Nose ◽  
Low Cost ◽  
Storage Conditions ◽  
Tree Structure ◽  
Training Dataset ◽  
Support Vector ◽  
Binary Decision ◽  
Linear Discriminant ◽  
Binary Decision Tree

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

2014 ◽  
Vol 14 (05) ◽  
pp. 1450066 ◽  
Author(s):  
MANAB KUMAR DAS ◽  
SAMIT ARI
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Svm Classifier ◽  
Massachusetts Institute Of Technology ◽  
Ecg Signals ◽  
Beat Classification ◽  
Temporal Features ◽  
S Transform ◽  
Institute Of Technology ◽  
Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

scholarly journals Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ersen Yılmaz ◽  
Çağlar Kılıkçıer
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Support Vector ◽  
Characteristic Analysis ◽  
Swarm Optimization ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Receiver Operation Characteristic

We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.

DECISION TREE SUPPORT VECTOR MACHINE

2007 ◽  
Vol 16 (01) ◽  
pp. 1-15 ◽  
Author(s):  
LI ZHANG ◽  
WEI-DA ZHOU ◽  
TIAN-TIAN SU ◽  
LI-CHENG JIAO
Keyword(s):  
Decision Tree ◽  
Clustering Algorithm ◽  
Binary Classification ◽  
Classification Problem ◽  
Support Vector ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Kernel Clustering ◽  
Binary Classification Problem ◽  
Multi Class Classification

A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM.

scholarly journals An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Zhou Zhang ◽  
Zeyu Li ◽  
Zhangyong Li
Keyword(s):  
Real Time ◽  
Ecg Signal ◽  
Massachusetts Institute Of Technology ◽  
Detection Rates ◽  
Order Difference ◽  
Ecg Signals ◽  
Wave Detection ◽  
Exercise Ecg ◽  
Real Time Detection ◽  
Institute Of Technology

R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters. In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease. This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals. After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal. Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment. The R-wave detection rates were 99.93% and 99.98%, respectively. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.

scholarly journals Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenhao Xie ◽  
Yanhong She ◽  
Qiao Guo
Keyword(s):  
Decision Tree ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Single Class ◽  
Indirect Methods ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Multiple Classification ◽  
Svm Algorithm

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.

scholarly journals Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1568
Author(s):  
Junmo Kim ◽  
Geunbo Yang ◽  
Juhyeong Kim ◽  
Seungmin Lee ◽  
Ko Keun Kim ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Physical State ◽  
Acceptance Rate ◽  
Support Vector ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
Ecg Signals ◽  
Using Data ◽  
False Acceptance

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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