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Deteksi sleep apnea menggunakan metode decision tree dengan fitur statistik RR interval

Subairi, Subairi, Permatasari, Delila Cahya, Dirgantara, Wahyu, Gumilang, Yandhika Surya Akbar, Zahroya, Isvine and Haitsam (2022) Deteksi sleep apnea menggunakan metode decision tree dengan fitur statistik RR interval. Jurnal EECCIS, 16 (3). pp. 1-5. ISSN 2460-8122

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Obstructive sleep apnea (OSA) or sleep apnea is a rare sleep disorder that requires the use of electrical activity signals, commonly referred to as electrograms (ECG), to be detected. An ECG signal consists of a waveform, duration, waveform rhythm, and signal orientation, which cardiologists can use to evaluate a patient's heart condition. The aim of this study was to detect sleep apnea using an existing ECG dataset. It is hoped that the sleep apnea detection system will be able to detect disorders in patients at an early stage and help doctors more accurately and quickly diagnose patients so that they can provide further treatment. This study proposes how to detect sleep apnea with software by using the statistical parameters of the RR interval signal in an ECG signal dataset and then classifying it using the Decision Tree
method. The sleep apnea detection process that the
researcher proposes using the RR interval and the decision
tree process has an accuration rate of 99.49

Item Type: Article
Additional Information: Nama : Subairi
Uncontrolled Keywords: Decision tree, ECG, RR interval, sleep apnea, classification.
Divisions: Fakultas Teknik > S1 Teknik Elektro
Depositing User: Surya Dannie
Date Deposited: 12 Apr 2023 05:38
Last Modified: 12 Apr 2023 05:38

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