Study on the Two-dimensional Sample Entropy of Sleep Apnea Based on the Hilbert-Huang Time-frequency Diagram

Sleep apnea (SA) as a common breathing disorder, has been determined to affect human physiological activities and is related to many diseases. Heart rate variability (HRV) analysis as an analysis method of the cardiac autonomic nervous system, is widely used in the study of sleep apnea. The Hilbert Huang Transform (HHT) method is composed of empirical mode decomposition (EMD) and Hilbert spectrum analysis, and is mainly used in nonlinear and non-stationary signal analysis. The two-dimensional sample entropy (SampEn2D) method can effectively analyze the irregularity of the image and evaluate the complexity of the image. We applied SampEn2D to the Hilbert-Huang time-frequency diagram to analyze the complexity of the time-frequency diagram of normal people and patients with sleep apnea. In the study, 60 electrocardiogram recordings were used for analysis, and nonlinearity SampEn2D was calculated. The SampEn2D of sleep apnea patients with different disease severity has significant differences (p<0.05), and the screening accuracy, sensitivity, and specificity reach 90%, 87.5%, and 95%, respectively. The results show that the two-dimensional sample entropy based on the Hilbert-Huang time-frequency diagram can be used to analyze the severity of sleep apnea and SA screening.

[1]  Alan V. Sahakian,et al.  Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome , 2007, IEEE Transactions on Biomedical Engineering.

[2]  Renata Trimer,et al.  Respiratory muscle strength effect on linear and nonlinear heart rate variability parameters in COPD patients , 2016, International journal of chronic obstructive pulmonary disease.

[3]  Thomas Penzel,et al.  Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea , 2003, IEEE Transactions on Biomedical Engineering.

[4]  Guanzheng Liu,et al.  Regularity of heart rate fluctuations analysis in obstructive sleep apnea patients using information-based similarity , 2021, Biomed. Signal Process. Control..

[5]  K. Jeong,et al.  STANDARDIZED TESTS OF HEART RATE VARIABILITY FOR AUTONOMIC FUNCTION TESTS IN HEALTHY KOREANS , 2007, The International journal of neuroscience.

[6]  Xi Zhang,et al.  An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram , 2015, IEEE Transactions on Automation Science and Engineering.

[7]  S. S. Shen,et al.  Applications of Hilbert–Huang transform to non‐stationary financial time series analysis , 2003 .

[8]  Jaehak Yu,et al.  Correlation between the Severity of Obstructive Sleep Apnea and Heart Rate Variability Indices , 2008, Journal of Korean medical science.

[9]  W A Whitelaw,et al.  A comparison of apnea-hypopnea indices derived from different definitions of hypopnea. , 1999, American journal of respiratory and critical care medicine.

[10]  Jayne C Carberry,et al.  Obstructive sleep apnea: current perspectives , 2018, Nature and science of sleep.

[11]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[12]  Yifan Li,et al.  Application of the Variance Delay Fuzzy Approximate Entropy for Autonomic Nervous System Fluctuation Analysis in Obstructive Sleep Apnea Patients , 2020, Entropy.

[13]  L. E. V. Silva,et al.  Two-dimensional sample entropy: assessing image texture through irregularity , 2016 .

[14]  U. Acharya,et al.  Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters , 2011, Physiological measurement.

[15]  Qing Jiang,et al.  Sliding Trend Fuzzy Approximate Entropy as a Novel Descriptor of Heart Rate Variability in Obstructive Sleep Apnea , 2019, IEEE Journal of Biomedical and Health Informatics.

[16]  G. Moody,et al.  The apnea-ECG database , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[17]  Jing Fang,et al.  HHT based cardiopulmonary coupling analysis for sleep apnea detection. , 2012, Sleep medicine.

[18]  Susan Redline,et al.  Sleep Apnea and Cardiovascular Disease: Lessons From Recent Trials and Need for Team Science. , 2017, Circulation.

[19]  Soo Yeol Lee,et al.  Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation , 2011, Comput. Biol. Medicine.

[20]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.