Sistem Deteksi Fibrilasi Atrium menggunakan Fitur RR Elektokardiogram dengan Jaringan Syaraf Tiruan

Eka Anzihory, N Nuryani, D Darmanto

Abstract


Penelitian untuk deteksi gangguan jantung fibrilasi atrium (AF) pada elektrokardiogram (EKG) dengan metode Jaringan Syaraf Tiruan (JST) menggunakan fitur statistik RR telah berhasil dilaksanakan. Pada penelitian ini digunakan tiga metode JST yaitu Learning Vector Quantization (LVQ), Radial Basis Functon (RBF), dan Multilayer Perception-Backporpagation (MLP-BP) untuk menentukan JST yang terbaik dalam mendeteksi AF. Hasil terbaik ditunjukkan pada JST RBF dengan masukan 7 macam fitur dari statistik deskriptif RR pada panjang segmen EKG 15 denyut dengan kinerja berupa sensitivitas, spesifisitas, serta akurasi  sebesar 99,97%, 99,84% dan 99,89%.

Abstract


Research for Atrial Fibrillation detection at electrocardiogram (ECG) using Artificial Neural Network (ANN) with RR statistic features has been successfully implemented. This study was conducted by varying RBF NN, MLP-BP NN and LVQ NN to determine the best of ANN in detecting AF. The best results were found when seven features from RR statistic features at length 15 beats of ECG segment by using RBF NN. The best performance were 99,97%, 99,84% and 99,89% in terms of sensitivity, specificity and accuracy, respectively.


Keywords


atrial fibrillation, RR statistic features, artificial neural network

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References


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DOI: http://dx.doi.org/10.12962/j24604682.v12i2.1330

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