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Made Putera Wiguna
Departemen Teknik Elektro, Universitas Brawijaya
Education

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KLASIFIKASI SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK SEBAGAI PENDETEKSI FIBRILASI ATRIUM Made Putera Wiguna; Ponco Siwindarto; Erni Yudaningtyas
Jurnal Mahasiswa TEUB Vol. 11 No. 1 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

The research currently being developed focuses on the classification of Electrocardiogram (ECG) signals on arrhythmic disorders of the heart rate of the atrial fibrillation type. This monitoring and classification aim to be an early treatment for types of atrial fibrillation arrhythmia disorders. The proposed classification can classify normal patient signals with those of patients with atrial fibrillation using the Artificial Neural Network method with long short-term memory architecture. Data preprocessing techniques on ECG signals before the classification process, namely segmentation, normalization, and feature extraction. The results show that the method used has a very good accuracy value of 94,41%, a sensitivity of 94,52%, and a specificity of 93,74%. Keywords: EKG, Arrhythmia, Atrial Fibrillation, Classification, Artificial Neural Network, Long Short-Term Memory
Co-Authors Erni Yudaningtyas Ponco Siwindarto