Deteksi Gelombang T Sinyal ECG Menggunakan Hybrid Deep Learning Dan Algoritma Heuristik

Satriyawan, Setifan Fajar (2024) Deteksi Gelombang T Sinyal ECG Menggunakan Hybrid Deep Learning Dan Algoritma Heuristik. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Gelombang T merupakan salah satu gelombang yang didapat dari pembacaan elektrokardiogram. Letak gelombang T serta ukurannya yang lebih kecil daripada gelombang lainnya membuat sulit untuk mengetahui letak pastinya. Bentuk gelombang T yang tidak normal merupakan tanda bahwa pasien menderita gangguan pada jantung. Dilakukan deteksi dengan bantuan gelombang QRS untuk menentukan lokasi dimana terdapat gelombang T. Teknik normalisasi digunakan untuk meningkatkan hasil pembuatan model training Multilayer Perceptron (MLP) dan mendapatkan hasil prediksi titik puncak gelombang T. Algoritma heuristik ditambahkan agar hasil akhir deteksi titik puncak gelombang T menjadi lebih presisi dan tingkat akurasinya meningkat.
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The T wave is one of the waves obtained from electrocardiogram readings. The location of the T wave and its smaller size than other waves makes it difficult to know its exact location. An abnormal T wave shape is a sign that the patient is suffering from heart problems. Detection is carried out with the help of QRS waves to determine the location where there are T waves. Normalization techniques are used to improve the results of making the training Multilayer Perceptron (MLP) model and get prediction results for the peak point of the T wave. A heuristic algorithm is added so that the final result is detection of the peak point T waves become more precise and the level of accuracy increases.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Elektrokardiogram, Gelombang T, MLP, Electrocardiogram, T Wave, MLP
Subjects:	T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.84 Heuristic algorithms.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Setifan Fajar Satriyawan
Date Deposited:	12 Jul 2024 01:48
Last Modified:	12 Jul 2024 01:48
URI:	http://repository.its.ac.id/id/eprint/106910

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