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DELINEASI SINGLE-LEAD SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN DEEP LEARNING DENGAN BAYESIAN HYPERPARAMETER TUNING OPTIMIZATION

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Penilaian

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Electrocardiogram (ECG) is a medical procedure used to assess cardiac function, including its electrical activity. With the increasing prevalence of heart disease, which recorded an 18.71% rise in 2020 compared to 2010, the role of ECG interpretation has become critically important. Cardiac conditions can be analyzed through the morphology of ECG signals, consisting of the P wave, QRS complex, and T wave. This research employs a Deep Learning (DL) approach combining Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BiLSTM) to delineate single-lead ECG signals. Bayesian optimization was utilized to fine-tune hyperparameters and enhance model performance. The dataset used in this research is the Lobachevsky University Database (LUDB). The study achieved an accuracy of 99.28%, specificity of 99.49%, recall of 91.99%, precision of 92.66%, and an F1-score of 92.3%. The application of DL and Bayesian optimization demonstrated high efficacy in delineating single-lead ECG signals, particularly for normal beats. For future work, this research could be expanded to explore more diverse datasets and Deep Learning architectures.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000128	T164383	T1643832024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1643832024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvi, VI-1 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

EM

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