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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rizhky, Ayu Nissa Berlianri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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QRS Detection on Heart Rate Variability Readings using Two Moving Average Methods Rizhky, Ayu Nissa Berlianri; Wisana, I Dewa Gede Hari; Pudji, Andjar; Des, Sima
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 1 (2023): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i1.165

Abstract

Heart Rate Variability or the average deviation between heartbeats in humans is influenced by the autonomic nervous system control of heart function. Monitoring HRV is necessary to diagnose the underlying pathophysiology of hypertension, optimize treatment modalities for hypertensive patients with signs of autonomic dysfunction, and predict cardiovascular events in the heart. This study focused on providing an overview of QRS complex detection for heart rate variability or HRV reading using the Two Moving Average method in detecting heart in humans. In addition, current research also determine QRS complex detection for heart rate variability reading by adding a window size feature, then create a QRS Complex detection tool for HRV reading using the Two Moving Average method by adding a window size feature. Furthermore, another aim of this study is to know the FFT signal results in order to see the frequency of each ECG signal generated by the patient. In this study, the use of the Two Moving Average method or moving average makes it easier to find the R peak-to-peak signal, so the heartbeats reading is easier as well. In this study, QRS complex signal detection was performed using lead II pickups using the Two Moving Average method, which was used as a filter or attenuator of unsought signals such as P and T signals in ECG signals. In this case, this method is recommended for detecting patients with high P and T signal values. This was achieved by evaluating and studying each change in window size, an algorithm that uses an equation with two different window widths to generate signal features and detection thresholds, allowing it to adapt to various changes in QRS and noise levels.
Co-Authors Des, Sima I Dewa Gede Hari Wisana Pudji, Andjar