1. 首都医科大学 生物医学工程学院，北京 100069；2. Division of Interdisciplinary Medicine and Biotechnology, Beth Israel
Deaconess Medical Center, Harvard University, Boston MA 02215, USA；3. 煤炭总医院 耳鼻喉科，北京 100028
Application of the EMD-Based CPC Technique in Pediatric Sleep Analysis
LIU Dongdong1, GUO Dan2, WU Huili3, SUN Rushan3, Chung-Kang Peng2
1. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; 2. Division of Interdisciplinary Medicine
and Biotechnology, Beth Israel Deaconess Medical Center, Harvard University, Boston MA 02215, USA; 3. Department of ENT,
Meitan General Hospital, Beijing 100028, China
Abstract：Objective To explore the diagnostic value of empirical mode decomposition-based cardio-pulmonary coupling (HHT-CPC)
technique in pediatric sleep-disordered breathing (SDB). Methods A total of 63 pediatric patients undergoing diagnostic overnight
Hypno PTT were retrospectively analyzed and classified into severity and non-severity groups according to the nasal flow-derived
respiratory disturbance index (RDI). Hypno PTT sleep parameters, OAS-18 scale scores and CPC indexes of each group were
compared, and the correlation and consistency between HHT-CPC and Hypno PTT were observed in the diagnosis of clinical SDB.
ROC curve was applied to evaluate SDB diagnostic efficiency of HHT-CPC. Results Patients in severity group presented increased
low frequency coupling (LFC) and elevated LFC, but decreased high frequency coupling (HFC) compared to that of non-severity
group. These HHT-CPC indices were strongly correlated with Hypno PTT parameters. Especially, the HHT-CPC based respiratory
disturbance index (H-RDI) was strongly correlated with Hypno PTT based RDI and 3% oxygen desaturation index, where r=0.844
and 0.770 respectively, both P<0.001. Furthermore, Bland-Altman result showed high consistency between two methods. For SDB
diagnosis, the area under ROC curve was 0.93, and the sensitivity and specificity were 0.85 and 0.90, respectively. Conclusion
HHT-CPC and Hypno PTT have good consistency in the diagnosis of SDB in children, and have high diagnostic value.
刘冬冬1，郭丹2，吴慧莉3，孙汝山3，Chung-Kang Peng2. 基于经验模式分解的心肺耦合技术在儿童睡眠分析中的应用[J]. 中国医疗设备, 2019, 34(8): 32-36.
LIU Dongdong1, GUO Dan2, WU Huili3, SUN Rushan3, Chung-Kang Peng2. Application of the EMD-Based CPC Technique in Pediatric Sleep Analysis. China Medical Devices, 2019, 34(8): 32-36.
