Application of the EMD-Based CPC Technique in Sleep Analysis
LIU Dong-dong1, ZHANG Ling2, YANG Xiao-wen3, ZHANG Bo4, WU Wen-fang1
1.School of Biomedical Engineering,
Capital Medical University, Beijing
100069, China; 2.MDD Treatment
Center, Beijing Anding Hospital,
Capital Medical University, Beijing
100088, China; 3.Department of Sleep
Respiration Diagnosis and Treatment,
Xianyang Central Hospital, Xianyang
Shanxi 712000, China; 4.Gold NOAH
(Beijing) Tech Co., Ltd, Beijing 100011,
China
Abstract：Objective To explore application of the EMD (Empirical Mode Decomposition)-based CPC (Cardio-Pulmonary Coupling) technique in sleep analysis. Methods Through analysis of 30 cases of thoracic ECG signals recorded by PSG (Poly-Somno-Graphy), the instantaneous frequency and instantaneous phase were obtained with deployment of EMD so as to construct a CPC map. Then, CAP (Cyclic Alternating Pattern) was utilized to divide sleep into three stages: CAP Stage, Non-CAP Stage and Wake/REM (Rapid Eyes Movement) Stage. The waving degree of the maximum CPC peak was measured by ZCR (Zero Crossing Rate), which reflected the severity of OSAHS (Obstructive Sleep Apnea-Hypopnea Syndrome). Results The frequency band of OSAHS patients’ map was distributed centralizedly in the low-frequency areas with small waving changes of the maximum peak at each time. Comparisons were made between manual staging and automatic staging, which revealed that EMD-based CPC could differentiate accurately between the different sleep statuses. Significant differences existed between the waving principles of the maximum peak in OSAHS Patient Group and Healthy Volunteer Group. ZCR values were significantly different between Slight/Middle OSAHS Patient Group and Healthy Volunteer Group (P<0.001), and between Slight/Middle OSAHS Patient Group and Severe OSAHS Patient Group (P<0.001). Therefore, the maximum coupling peak value and apnea-hypopnea Index could be used as indexes to identify the different severity of OSAHS patients. Moreover, strong negative correlation was seen between the two indexes (r=-0.77, P=5.8×10-18). Conclusion Combination of EMD and the CPC technique had proven its easy-to-operate features in data acquisition so as to provide reliable micro-structure and disorder information of sleep, which had huge development potentials in the fields of wearable health management and clinically-aided diagnosis.
刘冬冬，张玲，杨晓文，张博，武文芳. 基于经验模式分解的心肺耦合技术在睡眠分析中的应用[J]. 中国医疗设备, 2015, 30(6): 28-32.
LIU Dong-dong, ZHANG Ling, YANG Xiao-wen, ZHANG Bo, WU Wen-fang. Application of the EMD-Based CPC Technique in Sleep Analysis. China Medical Devices, 2015, 30(6): 28-32.
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