Pendeteksi Penyakit Aritmia Nerbasis Prediksi Fitur QRS Pada Sinyal ECG dengan Metode Feed Forward Backpropagation Menggunakan Sistem Tertanam

Masruroh, Izzatul (2020) Pendeteksi Penyakit Aritmia Nerbasis Prediksi Fitur QRS Pada Sinyal ECG dengan Metode Feed Forward Backpropagation Menggunakan Sistem Tertanam. Undergraduate thesis, Institut Teknologin Sepuluh Nopember.

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Abstract

Aritmia adalah suatu tanda atau gejala dari gangguan detak jantung atau irama jantung yang bisa mengindikasikan pada penyakit kardiovaskular (penyakit yang berkaitan dengan organ jantung dan pembuluh darah). Berdasarkan data dari American Heart Association (AHA) penyakit kardiovaskular terdata sebagai penyebab utama kematian, terhitung sebanyak hampir 836.546 kematian di US dan menjadi penyebab kematian secara global yang dilaporkan sudah lebih dari 17,9 juta kematian setiap tahun pada tahun 2015, angka tersebut diperkirakan akan tumbuh menjadi lebih dari 23,6 juta pada tahun 2030. Maka dari itu, deteksi aritmia sangat diperlukan dengan menggunakan teknologi pada bidang machine learning yang akhir-akhir ini sangat berkembang dengan pesat, sehingga penyakit aritmia bisa diketahui se-dini mungkin dan dapat segera menerima terapi / pengobatan tertentu untuk mengurangi resiko terkena penyakit kardiovaskular. ============================================================= Arrhythmia is a sign or symptom of a disturbance in heart rate or heart rhythm that can indicate cardiovascular disease (a disease related to the heart organ and blood vessels). Based on data from American Heart Association (AHA) recorded cardiovascular disease as the leading cause of death, accounting for nearly 836,546 deaths in the US and being the cause of death globally, which reported more than 17.9 million deaths each year in 2015, the figure it is expected to grow to more than 23.6 million by 2030. Therefore, arrhythmia detection is needed by using technology in the field of machine learning which is very rapidly growing lately, so that arrhythmia can be known as early as possible and can immediately receive certain therapies = treatments to reduce the risk of cardiovascular disease.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Aritmia, Electrocardiography, Machine Learning Backpropagation, Sistem Tertanam. Arrhytmia, Electrocardiography, Machine Learning Backpropagation, Embedded System
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5102.9 Signal processing.
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7871.674 Detectors. Sensors
Divisions: Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering
Depositing User: Izzatul Masruroh
Date Deposited: 25 Aug 2020 04:30
Last Modified: 25 Aug 2020 04:30
URI: https://repository.its.ac.id/id/eprint/79500

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