KLASIFIKASI BEAT EKG SECARA DEEP LEARNING MENGGUNAKAN AUTOENCODER DAN DEEP NEURAL NETWORK

BHAYYU, VICKO and Nurmaini, Siti (2019) KLASIFIKASI BEAT EKG SECARA DEEP LEARNING MENGGUNAKAN AUTOENCODER DAN DEEP NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Electrocardiograph (ECG) is medical testing for examining heart condition in an electrical signal to provide clinical information about a patient’s heart. With ECG, Cardiologists can diagnose the patient's heart condition either by heartbeats or rhythm. ECG beat classification with a large amount of data has its challenges so that the Deep Learning method that has a high level of abstraction in learning features is highly favored. With the Autoencoder method as feature extraction to learn features and reduce feature dimensions and Deep Neural Network as an ECG beat classifier. The dimension of ECG beat features from raw data is 252 then perform extraction feature by Autoencoder which is then reduced to 32. The results of this extraction feature are then classified by Deep Neural Network with 10 classes. Conducted as many as 15 experimental models with the best model will be tested to another dataset. From the 15 experimental models, the best models are obtained, namely Deep AE - DNN 3 HL, with the results of accuracy, sensitivity, specificity, precision, and F1-Score respectively 99.59%, 91.02%, 99.8%, 93.06%, 91.79%. Then this model was tested back to other datasets SVDB and IncartDB with 99.5% accuracy results, 89.6% sensitivity, 98.39% specificity, 97.62% precision, and F1-Score 93.07% for IncartDB and 97.86% accuracy, sensitivity 87.28%, specificity 94%, precision 91.91%, F1-Score 89.46%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Elektrokardiografi, Klasifikasi, Autoencoder, Deep Neural Network
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Vicko Bhayyu
Date Deposited: 28 Aug 2019 04:06
Last Modified: 28 Aug 2019 04:06
URI: http://repository.unsri.ac.id/id/eprint/4965

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