Loading

Electrocardiogram Classification for Arrhythmia using Convolutional Neural Network 2D and Adabound Optimizer
Monika R.Diniari1, Sani M. Isa2

1Monika R.Diniari, Computer Science Department BINUS Graduate Program-Master in Computer Science, Bina Nusantara University, Jakarta, Indonesia.
2Sani M. Isa, Computer Science Department BINUS Graduate Program-Master in Computer Science, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1277-1284 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4591018520/2020©BEIESP | DOI: 10.35940/ijrte.E4591.018520

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cardiovascular disease is the number one deadly disease in the world. Arrhythmia is one of the types of cardiovascular disease which is hard to detect but by using the routine electrocardiogram (ECG) recording. Due to the variety and the noise of ECG, it is very time consuming to detect it only by experts using bare eyes.Learning from the previous research in order to help the experts, this research develop 11 layers Convolutional Neural Network 2D (CNN 2D) using MITBIH Arrhythmia Dataset. The dataset is firstly preprocessed by using wavelet transform method, then being segmented by R-peak method. The challenge is how to conquer the imbalance and small amount of data but still get the optimal accuracy. This research can be helpful in helping the doctors figure out the type of arrhythmia of the patient. Therefore, this research did the comparison of various optimizers attach in CNN 2D namely, Adabound, Adadelta, Adagrad, Amsbound, Adam and Stochastic Gradient Descent (SGD). The result is Adabound get the highest performance with 91% accuracy and faster 1s training duration than Adam which is approximately 18s per epoch.
Keywords: Electrocardiogram Classification, Arrhythmia, Adabound, Convolutional Neural Network.
Scope of the Article: Classification.