DELINEASI SINYAL ELEKTROKARDIOGRAM MULTI-LEAD MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

PRAYOGA, RAFI AUDI and Nurmaini, Siti (2021) DELINEASI SINYAL ELEKTROKARDIOGRAM MULTI-LEAD MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

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, QRS-Complex and T wave. The deep learnings methodology used in this study is Convolutional Neural Network(CNN) combined with Long Short-Term Memory(LSTM). 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 types of waves There are 18 models generated with the best learning rate, number of hidden layers and batch size. From the 18 experimental models, the best model is obtained by using CNN as feature extraction before using Bi-LSTM in 4 types of waves. CNN and Bi-LSTM’s model have the highest evaluation values in 4 types of waves scenarios with performance value of sensitivity, precision, specificity, accuracy and F1-Score respectively 95.56%, 96.1%, 99.14%, 98.72% and 95.83%

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Elektrokardiogram, Delineasi, Convolutional Neural Network, Long Short-Term Memory,
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Rafi Audi Prayoga
Date Deposited:	29 Nov 2021 07:16
Last Modified:	29 Nov 2021 07:16
URI:	http://repository.unsri.ac.id/id/eprint/58154

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