NURHERLIZA, FEBBY and Nurmaini, Siti (2021) PERANCANGAN SISTEM UNTUK MENDETEKSI NILAI QT-CORRECTED MENGGUNAKAN METODE LONG SHORT -TERM MEMORY. Undergraduate thesis, Sriwijaya University.
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Abstract
The bioelectric activity of the heart produces an electrical signal called an Electrocardiogram signal. This recording of electrical signals helps doctors diagnose abnormalities in the patient's heart. One form of congenital abnormality in the human heartbeat that can cause syncope, sudden cardiac arrest and sudden death is Long-QT syndrome, which is characterized by an extension of the interval between Q and T waves on an electrocardiogram signal. Classification of ECG beats in large amounts of data and sequences has its challenges, so Deep Learning that have the advantage in processing data automatically and are able to learn their own computational features and methods are highly recommended in this research. From the 3 experimental models, the best models were Bi-LSTM Model 3, with results of accuracy, sensitivity, specificity, precision, and F1-Score of 99.52%, 96.23%, 99.72%, 96.53%, 96.37% respectively. Then this model was tested back to the other datasets NSRDB with 99.76% accuracy results, 98.30 % sensitivity, 99.87% specificity, 98.37% precision, and 98.34% F1-Score.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | Electrocardiogram, Long-QT syndrome, Classification, Deep Learning, 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: | Users 9968 not found. |
Date Deposited: | 19 Jan 2021 03:56 |
Last Modified: | 19 Jan 2021 03:56 |
URI: | http://repository.unsri.ac.id/id/eprint/40399 |
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