Identifikasi Sinyal Congestive Heart Failure dengan Metode Convolutional Neural Network 1D

MUHAMMAD ADNAN PRAMUDITO, YUNENDAH NUR FU’ADAH, RITA MAGDALENA, ACHMAD RIZAL, FAUZI FRAHMA TALININGSIH

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ABSTRAK

Penyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.

Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)

ABSTRACT

Heart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.

Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)

 

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Referensi


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DOI: https://doi.org/10.26760/mindjournal.v7i1.11-20

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