DELINEASI GELOMBANG P DAN RR-INTERVAL PADA SINYAL ELEKTROKARDIOGRAM SINGLE-LEAD MENGGUNAKAN METODE RECURRENT NEURAL NETWORKS

PUTRI, ELSA MAHARANI and Nurmaini, Siti and Sukemi, Sukemi (2023) DELINEASI GELOMBANG P DAN RR-INTERVAL PADA SINYAL ELEKTROKARDIOGRAM SINGLE-LEAD MENGGUNAKAN METODE RECURRENT NEURAL NETWORKS. Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to perform the delineation of P-waves and RR-intervals in single-lead electrocardiogram (ECG) signals using Recurrent Neural Networks (RNN). The ECG signals consist of P, Q, R, S, T, and U waves. The delineation process is conducted to identify waves in the EKG signal, dividing the data into eight classes, namely Pwave, Poff-Qon, Qon-Rpeak, Rpeak-Qoff, Qoff-Ton, Twave, Toff-Pon, and Zeropad. The utilization of deep learning methods in delineation aims to reduce interpretation errors. In this study, a computer-based delineation system employs a combination of CNN-BiLSTM deep learning methods. Delineation is carried out for eight wave classes, with a total of 312 designed models, each trained and tested using QTDB data. Each model is constructed with variations in hidden layer parameters, batch size, learning rate, and epoch to achieve optimal results. The delineation process of medical image signals in EKG with the CNN-BiLSTM architecture shows the best results in trials using a CNN with 7 layers and 1 layer of BiLSTM. The fourth model in this architecture exhibits a sensitivity of 93.59%, precision of 94.94%, specificity of 99.52%, accuracy of 99.15%, error rate of 0.85%, and an F1-Score of 94.20%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	GELOMBANG P, RR-INTERVAL ,SINYAL ELEKTROKARDIOGRAM
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Elsa Maharani Putri
Date Deposited:	17 Jan 2024 07:39
Last Modified:	17 Jan 2024 07:39
URI:	http://repository.unsri.ac.id/id/eprint/138216

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