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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Abstract

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

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