Text

DETEKSI QRS COMPLEX PADA SINYAL FETAL ELECTROCARDIOGRAM MENGGUNAKAN DEEP LEARNING

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002711	T114208	T1142082023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1142082023

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer., 2023

Collation

xii, 41 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

006.307

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment