DETEKSI QRS COMPLEX PADA SINYAL FETAL ELECTROCARDIOGRAM MENGGUNAKAN DEEP LEARNING

ARDIANSYAH, MUHAMMAD and Nurmaini, Siti (2023) DETEKSI QRS COMPLEX PADA SINYAL FETAL ELECTROCARDIOGRAM MENGGUNAKAN DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to develop a detection model by combining Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) architectures to detect QRS Complex signal waves in fetal electrocardiogram signal datasets. In this study, CNN is used to extract features and process the signals, while the function of RNN is to detect the QRS Complex signals. The research focuses on detecting two classes: "QRS-Complex" and "Non-QRS." The implementation of RNN in this study utilizes the Bidirectional Long Short-term Memory (BiLSTM) architecture, which is an improvement over traditional RNN architectures. The research findings indicate that the best model is found in the second model, which achieves high accuracy. The detection performance of the second model resulted in 100% accuracy, validated using unseen data. In conclusion, the combination of Convolutional Neural Network and Bidirectional Long Short-Term Memory shows compatibility and can be used for accurate detection of QRS Complex signal waves in fetal EKG signal datasets.

Item Type:	Thesis (Undergraduate)
Additional Information:	A. Surtono and G. A. Pauzi, “Deteksi Miokard Infark Jantung pada Rekaman Elektrokardiogram Menggunakan Elevasi Segmen ST,” J. Teor. dan Apl. Fis., vol. 4, no. 1, pp. 119–124, 2016, [Online]. Available: http://jurnal.fmipa.unila.ac.id. E. L. Utari, “Analisa Deteksi Gelombang Qrs Untuk Menentukan Kelainan Fungsi Kerja Jantung,” Teknoin, vol. 22, no. 1, pp. 27–37, 2016, doi: 10.20885/teknoin.vol22.iss1.art4. G. de Lannoy, B. Frenay, M. Verleysen, and J. Delbeke, “Supervised ECG Delineation Using the Wavelet Transform and Hidden Markov Models,” IFMBE Proc., vol. 22, pp. 22–25, 2009, doi: 10.1007/978-3-540-89208-3_7. A. Manuscript, “d M us A deep learning approach for fetal QRS complex,” 2018. W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent Neural Network Regularization,” no. 2013, pp. 1–8, 2014, [Online]. Available: http://arxiv.org/abs/1409.2329. J. S. Lee, M. Seo, S. W. Kim, and M. Choi, “Fetal QRS detection based on convolutional neural networks in noninvasive fetal electrocardiogram,” 2018 4th Int. Conf. Front. Signal Process. ICFSP 2018, vol. 4, pp. 75–78, 2018, doi: 10.1109/ICFSP.2018.8552074. U. G. M. Press, G. M. U. Press, and E. Maharani, Elektrokardiografi Konsep Dasar dan Praktik Klinik. UGM PRESS, 2018. D. Putra, Pengolahan Citra Digital. Penerbit Andi. W. Setiawan, Deep Learning menggunakan Convolutional Neural Network: Teori dan Aplikasi. Media Nusa Creative (MNC Publishing), 2021. J. Wang, R. Li, R. Li, and B. Fu, “A knowledge-based deep learning method for ECG signal delineation,” Futur. Gener. Comput. Syst., vol. 109, pp. 56–66, Aug. 2020, doi: 10.1016/J.FUTURE.2020.02.068. L. Medsker and L. C. Jain, Recurrent Neural Networks: Design and Applications. CRC Press, 1999. TEKNIK ENSEMBLE LEARNING UNTUK PENINGKATAN PERFORMA AKURASI MODEL PREDIKSI (SELEKSI MAHASISWA PENERIMA BEASISWA). Pascal Books, 2022. S. Chivers et al., “Measurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study,” JRSM Cardiovasc. Dis., vol. 11, p. 204800402210962, 2022, doi: 10.1177/20480040221096209. A. Darmawahyuni et al., “Deep learning with a recurrent network structure in the sequence modeling of imbalanced data for ECG-rhythm classifier,” Algorithms, vol. 12, no. 6, pp. 1–12, 2019, doi: 10.3390/a12060118.
Uncontrolled Keywords:	admin.library@unsri.ac.id
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.6.A2-Z General works Simulation Cf. QA76.9.C65 Computer science Cf. TA343 Engineering mathematics
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Ardiansyah
Date Deposited:	11 Jul 2023 05:44
Last Modified:	11 Jul 2023 05:44
URI:	http://repository.unsri.ac.id/id/eprint/114208

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