Building an Integrated Model Using Decision Trees to Improve the Quality of ECG Signals Recognition

Authors

  • Dinh Do Van Dinh Do Van, Sao Do University, Hai Duong-03500, Viet Nam

Keywords:

Neural network, MLP, TSK, SVM, Integrated System, Decision Tree, Hermite Basis Functions, Electrocardiogram (ECG) Signals

Abstract

Recognizing and improving the quality of recognition of electrocardiographic signals has many published scientific works, each with different methods. To improve the quality of ECG signal recognition, the article proposes a solution to improve the quality (accuracy) of ECG signal recognition (Electro Cardio Graphy), based on the use of binary decision trees to combine many single recognition models, which are classic neural networks MLP (Multi Layer Perceptron), neuro-fuzzy TSK network (Takaga-Sugeno-Kang), SVM (Support Vector Machines) and RF (Random  Forest). The article uses Hermite basis functions (Hermite Basis Functions) to develop QRS complex and two time characteristics which are the distance between two consecutive peaks R (R-R), the average value of the last 10 R-R distances. The algorithms have been tested and tested on the classic data sets of the international classic database MIT-BIH (Massachusetts Institute of Technology, Boston's Beth Israel Hospital) and MGH database from the Web site http://physionet.org.  

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References

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Published

16.07.2023

How to Cite

Van, D. D. . (2023). Building an Integrated Model Using Decision Trees to Improve the Quality of ECG Signals Recognition. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 636–642. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3266

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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