KLASIFIKASI SINYAL EKG MENGGUNAKAN DENOISING AUTOENCODER DAN DEEP NEURAL NETWORK
Daftar Isi:
- Arrhythmia is a indication or a symtop of heart beat disruption or heart rhythm Early detection of arrhythmia could help patient in handling the abnormalities quickly. Arrhythmia can be detected using electrocardiogram (ECG), which is heart activity electrical signal recording. This research aims to classify normal heart, premature ventricular contraction, atrial premature beat, right bundle branch block beat and non-conducted p-wave on the ECG signal. Deep Neural Network is proposed due to the ability of processing non-linear data like ECG signal. The data used in this research is obtained from Physionet.org website with imbalanced class ratio and noise-contained. To overcome the noise-contained data, Denoising Autoencoder is proposed to denoise the signal and Autoencoder used to extract the feature of the denoised ECG signal. Both the technique above shows the results performance accuracy, sensitivity, specificity, precision and F1 Score is 99.06%, 93.56%, 99.35%, 89.42% and 91.11% repectively.