Skripsi

DEEP FILTER DAN BI-LSTM UNTUK PENINGKATAN KINERJA DELINEASI SINYAL ELECTROCARDIOGRAM

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Penilaian

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The delineation of ECG signals is often hindered by noise, such as baseline wandering and electrode motion. This study presents a robust model for ECG signal denoising and delineation into four classes: baseline, P wave, QRS complex, and T wave, using data from multiple sources. The denoising model, based on a Multibranch LANLD architecture, was trained with noisy signals from NSTDB and clean labels from QTDB, while LUDB was used for delineation training. Fine-tuning was done by replacing the CNN output layer with a Bi-LSTM and Dense layer. The model achieved denoising up to 23 dB and delineation F1-scores of 88.2% for baseline, 84.5% for P wave, 89.7% for QRS complex, and 80.6% for T wave, with an overall accuracy of 86.4%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407006602	T159889	T1598892024	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1598892024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 165 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

616.075 407

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Electrocardiogram

Specific Detail Info

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Statement of Responsibility

TUTI

Other version/related

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