Multi-Abnormal ECG Signal Classification using Dispersion Entropy and Statistic Feature

ERVIN MASITA DEWI, SUCI AULIA, SUGONDO HADIYOSO

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ABSTRAK

Elektrokardiogram (EKG) adalah salah satu perangkat medis yang paling banyak digunakan untuk mendiagnosis masalah jantung. Sinyal abnorma EKG mempunyai variasi dan beberapa mirip antara yang satu dengan lainnya. Oleh karena itu, pada penelitian ini diusulkan metode klasifikasi kelainan jantung berdasarkan EKG menggunakan fitur statistik orde satu dan Dispersion Entropy (DisEn) untuk tahap ekstraksi ciri. Sedangkan untuk tahap klasifikas sinyal EKG multi-abnormal, kami membandingkan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). Pada penelitian ini diklasifikasikan tujuh kelas EKG, yaitu Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), dan Premature Ventricular Contraction (PVC). Dari simulasi ini, sistem dapat mendeteksi sinyal normal dan abnormal dengan akurasi 85,1% menggunakan K-NN. Sementara itu, pada simulasi klasifikasi tujuh kelas sinyal EKG menghasilkan akurasi hingga 75.1%.

Kata kunci: EKG, klasifikasi, Dispersion Entropy, statistik

 

ABSTRACT

Electrocardiogram (ECG) is one of the most widely used medical devices to diagnose heart disease. Abnormal ECG signals have variations and some are similar to another. Therefore, in this study, proposed a method for classifying cardiac abnormalities based on ECG using first-order statistical features and Dispersion Entropy (DisEn) for feature extraction. Meanwhile, for the multiabnormal ECG signal classification stage, we compared the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) methods. In this study, seven ECG classes were classified, namely Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), and Premature Ventricular Contraction (PVC). From this simulation, the system can detect normal and abnormal signals with an accuracy of 85.1% using K-NN. Meanwhile, the classification simulation of seven classes of ECG signals produces an accuracy of up to 75.1%.

Keywords: ECG, classification, Dispersion Entropy, statistics


Kata Kunci


ECG; classification; Dispersion Entropy; statistics

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i3.677

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