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Indrawati, Aida Noor, Nuryani, Nuryani, Nugroho, Anto Satriyo, Utomo, Trio Pambudi. (1401). Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms. سامانه مدیریت نشریات علمی, 12(6), 627-636. doi: 10.31661/jbpe.v0i0.2010-1216
Aida Noor Indrawati; Nuryani Nuryani; Anto Satriyo Nugroho; Trio Pambudi Utomo. "Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms". سامانه مدیریت نشریات علمی, 12, 6, 1401, 627-636. doi: 10.31661/jbpe.v0i0.2010-1216
Indrawati, Aida Noor, Nuryani, Nuryani, Nugroho, Anto Satriyo, Utomo, Trio Pambudi. (1401). 'Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms', سامانه مدیریت نشریات علمی, 12(6), pp. 627-636. doi: 10.31661/jbpe.v0i0.2010-1216
Indrawati, Aida Noor, Nuryani, Nuryani, Nugroho, Anto Satriyo, Utomo, Trio Pambudi. Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms. سامانه مدیریت نشریات علمی, 1401; 12(6): 627-636. doi: 10.31661/jbpe.v0i0.2010-1216

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، اسفند 2022، صفحه 627-636    اصل مقاله (1.01 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2010-1216
نویسندگان
Aida Noor Indrawati1؛ Nuryani Nuryani* 2؛ Anto Satriyo Nugroho3؛ Trio Pambudi Utomo3
1MSc, Medical Instrumentation, Physics, University Sebelas Maret, Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
2PhD, Medical Instrumentation, Physics, University of Sebelas Maret. Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
3PhD, Center for Information and Communication Technology Agency for Assessment and Application of Technology, Indonesia
چکیده
Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient’s body is fitted with many sensors. 
Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms.
Material and Methods: In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). 
Results: The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied.  
Conclusion: ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.
کلیدواژه‌ها
Electrocardiography؛ Sleep Apnea؛ Obstructive؛ Machine Learning
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