Sleep Apnea Detection by Real-time Heart Rate Variability Analysis

Abstract

Sleep Apnea Detection by Real Heart Rate Variability Analysis; The obstructive sleep apnea (OSA) is a chronic, lifelong medical condition that effects on the sleep, health, and quality of life. Breakdown of the patients on the basis of the acute situations can be useful in providing appropriate therapy. In this study we present a classifier to predict the obstructive sleep apnea (OSA) based on heart rate variability of patients. We used the recorded ECG signals from PhysioNet Database. At first, in the preprocessing, stage the noise from the electrocardiogram (ECG) signal removed and R spikes were detected to generate heart rate variability (HRV). The HRV signal is measured by calculating the time between R spikes on an ECG period [2]. Then, the HRV signal segmented to multiple windows with same durations. The choice of the optimum window duration was determined based on the correct detection of the apnea event period. The next stage was related to linear and non -linear feature extraction. We used paired sample t-test that is a statistical technique to compare two periods (apnea and non-apnea). A significant result was indicated with p- value less than 0.05. These features were used as the inputs of two different classifier include Multi-Layer Perceptron (MLP) and Radial Basic Function (RBF) to find the best method to distinguish patients with higher apnea risk. The results showed that the MLP classifier is more capable to separate the apnea periods from control. The MSE and R index for detecting the control, OSA and CSA event were 0.051-0.94, 0.057-0.93 and 0.053-0.94, respectively. In conclusion, based on proposed algorithm the HRV signal and feature presented in this paper had the lowest mean square error for the detection the apnea event of the non-apnea.

Full text

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* Corresponding author: ½YÆe Ê°a ¹Â¸ |uYÁ Ê»ÔY {YM ÃZ´¿Y{ ,Ê°a Ê|ÀÆ» Á dY|Æ] Ã|°¿Y{ ʼ¸ cPÌÅ Â,ZË{ZfY
Email: karimi.m@iautmu.ac.ir
2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
Technology, Tehran, Iran, 30 November - 1 December 2017
978-1-5386-3609-1/17/$31.00 ©2017 IEEE 227

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 . Blood oxygen saturation level (SPO2)
 . Electrocardiogram (ECG)
 . Standard Deviation of NN Intervals (SDNN)
2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
Technology, Tehran, Iran, 30 November - 1 December 2017
978-1-5386-3609-1/17/$31.00 ©2017 IEEE 228

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differ by more than 50 ms (pNN50)
 . Burg
 . Wigner-Ville distribution
 . Short-time Fourier transform (STFT)
 . Wavelet transform
 . Autoregressive (AR)
 . Power spectral density function (PSD)
 . Very low frequency (VLF)
 . Low Frequency (LF)
 . High Frequency (HF)
 . Low Frequency to High Frequency (LF/HF)
 . Detrended Fluctuation Analysis (DFA)
 . Correlation Coefficient
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 . Statistical Package for the Social Sciences
(SPSS)
 . paired sample t-test
 . Artificial Neural Network (ANN)
 . Multi-layer perceptron (MLP)
 . Radial basis function (RBF)
2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
Technology, Tehran, Iran, 30 November - 1 December 2017
978-1-5386-3609-1/17/$31.00 ©2017 IEEE 229

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2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
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2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
Technology, Tehran, Iran, 30 November - 1 December 2017
978-1-5386-3609-1/17/$31.00 ©2017 IEEE 231

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5. mY»
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2017 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of
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978-1-5386-3609-1/17/$31.00 ©2017 IEEE 232