Skripsi

OTOMATISASI DELINEASI SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN METODE LONG SHORT-TERM MEMORY BERBASIS EKSTRAKSI FITUR CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI

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Penilaian

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Electrocardiogram (ECG) is electrical records that contains information about human heart. In the medical field, humans heart condition can be diagnosed by analyzing the changes in hearts beat or rhythm that contain p wave, QRSComplex and T wave. Delineation can be very hard for doctor to do because of human errors. Because of that, automation of ECG delineation by using deep learning is preferred. The deep learnings methodology used in this study is Recurrent Neural Network(RNN) with Long Short-Term Memory(LSTM) combined with Convolutional Neural Network(CNN) as feature extraction. LSTM is an effective method for classifying time series data. LSTM can also overcomes vanishing gradient’s problems that occur in RNN. In this study, delineation is applied to 4 and 7 types of waves. There are 14 models generated with the best learning rate, number of hidden layers and batch size. Every time step in LSTM have 370 nodes for every types of waves. From the 14 experimental models, the best model is obtained by using CNN as feature extraction before using Bi-LSTM in both 4 and 7 types of waves. CNN and Bi-LSTM’s model have the highest evaluation values in 7 types of waves scenarios with performance value of sensitivity, precision, specificity, accuracy and F1-Score respectively 98.82%, 98,86%, 99.9%, 99.83%, and 98.84%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002715	T40404	T404042021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T404042021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xx, 138 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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