KLASIFIKASI KELAS PENYAKIT JANTUNG BERDASARKAN SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI

FANSYURI, AHMAD and Nurmaini, Siti (2021) KLASIFIKASI KELAS PENYAKIT JANTUNG BERDASARKAN SINYAL ELEKTROKARDIOGRAM MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK 1-DIMENSI. Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease can be identified based on the pattern of electrical current activity in the heart using an electrocardiogram. The data generated through the electrocardiogram will be in the form of a signal that represents the human heartbeat or is called an electrocardiograph. This study aims to classify classes of heart disease based on the signal morphology of each class using the convolutional neural network method. Based on previous studies, the 1-dimensional CNN method has advantages because it has feature extraction or feature learning in the learning process. In this study, the ECG signal classification process will be divided into five scenarios, with the first four scenarios dividing the signal segmentation into 1000 nodes, 2000 nodes, 3000 nodes, and 4000 nodes. Then in scenario five using 2000 nodes segmentation with the addition of three new datasets, each of which is the china physiological signal challenge 2018, MIT-BIH Normal Sinus Rhythm Database, BIDMC Congestive Heart Failure Database. From the 5th experimental scenario, the best performance results in training data fold 9 with accuracy, sensitivity, specificity, precision, F1score, and error which are 100.00%, 99.98%, 100.00%, 100.00% and 00.00%, respectively.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Electrocardiogram (ECG), Classification, PTBDB, Deep Learning, Convolutional Neural Network
Subjects: Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
R Medicine > R Medicine (General) > R855-855.5 Medical technology
R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Mr Ahmad Fansyuri
Date Deposited: 19 Aug 2021 01:24
Last Modified: 19 Aug 2021 01:27
URI: http://repository.unsri.ac.id/id/eprint/52182

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