KLASIFIKASI ARITMIA PADA SINYAL FETAL ECG BERBASIS DEEP LEARNING

VIANTI, EZRA RISDA and Nurmaini, Siti (2024) KLASIFIKASI ARITMIA PADA SINYAL FETAL ECG BERBASIS DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Fetal Electrocardiogram (ECG) is a type of signal that reflects the electrophysiological activity of the fetal heart. The non-invasive abdominal electrode method is employed to obtain fetal EKG signals. Classification was conducted between normal and arrhythmia categories using two main approaches, which are autoencoder-Deep Neural Network (DNN) and Convolutional Neural Network (CNN). The autoencoder was used for feature extraction before classification with DNN. Evaluation results indicated that the DNN with autoencoder achieved a best accuracy of 76%, precision of 83%, recall of 83%, and F1 score of 79%. In contrast, the CNN approach demonstrated higher performance with an accuracy of 97%, precision of 100%, recall of 99%, and F1 score of 97%. Although the CNN approach yielded better metric results, the training loss and accuracy graphs were more optimal in the autoencoder-DNN procedure. This study indicates that the CNN method is superior for classifying fetal ECG signals, yet the autoencoder-DNN approach also shows potential with better optimization. The procedures used in this study were conducted to fulfill deep learning methods in artificial intelligence for the Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB).

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Fetal EKG, Autoencoder, DNN, CNN, NIFEADB, Deep Learning
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Ezra Risda Vianti
Date Deposited:	03 Sep 2024 02:13
Last Modified:	03 Sep 2024 02:13
URI:	http://repository.unsri.ac.id/id/eprint/156564

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