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DELINEASI SINYAL ELEKTROKARDIOGRAM UNTUK DETEKSI T WAVE ALTERNANS MENGGUNAKAN DENOISING AUTO ENCODER DAN CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI DENGAN BIDIRECTIONAL LONG SHORT TERM MEMORY

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Penilaian

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Electrocardiogram (ECG) is a biological signal that results from the electrical activity of the heart that is tapped through electrodes that are attached to the body. T Wave Alternans (TWA) is associated with several diseases and their accurate detection can contribute to the early diagnosis of complications that occur in the heart. The ECG signal is a very weak bioelectric signal. The method that will be used in this research is a deep learning method using a denoising autoencoder to reduce noise from the ECG signal. The methods that will be used are Denosing Autoencoder (DAE) and Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In this research, the datasets used are QT Database (QTDB) and T Wave Alternans Database (TWADB). QTDB will be used in the training process for model testing while TWADB as a testing process uses several DAE parameters. The model that produces the best model is DAE with SNR 36.94 with CNN model with 4 layers CNN hidden layer and 1 layer BiLSTM. This model is tested by parameter batch size 8, learning rate 0.0001, and 300 epochs. This model produces the best evaluation results with recall, precision, specificity, accuracy and FQ values of 98.55%, 98.26%, 99.89%, 99.81%, and 98.40%. The results of this delineation detected 20 of 30 data on patients who experienced TWA.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003850	T79179	T791792022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T791792022

Publisher

Inderalaya : Jurusan Sstem Komputer, Fakultas Ilmu Komputer., 2022

Collation

xiii, 65 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

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)

Jaringan Komunikasi Komputer
Jurusan Sstem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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