KLASIFIKASI KELAS BINER PADA SINYAL EKG UNTUK KASUS ATRIAL FIBRILLATION MENGGUNAKAN RECURRENT NEURAL NETWORK
Daftar Isi:
- Early detection of cardiac disease can extend life through proper treatment. One of the most dangerous cardiac diseases is atrial fibrillation. Atrial fibrillation can be detected using an electrocardiogram (ECG), which is a signal recording of the electrical activity of the heart. This research aims to classify normal heart and atrial fibrillation of the ECG signal. Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)-based is proposed due to can process sequential data such as ECG signal. This study used Physionet.org/Computing in Cardiology (CinC) Challenge 2017 database, which has a large imbalanced data ratio. To overcome the problems of imbalanced data, Synthetic Minority Oversampling Technique (SMOTE) is proposed. SMOTE technique shows the results performance accuracy, sensitivity, specificity, precision, and F1 score is 94.83%. 94.95%. 94.95%. 94.78%. and 94.82%. respectively