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Abstract: Poster Presentations |

THE APPLICATION OF WAVELET ANALYSIS ON THE EVALUATION OF SLEEP EEG FROM OBSTRUCTIVE SLEEP APNEA HYPOVENTILATION FREE TO VIEW

Guangfa Wang, MD*; Xingwang Li, MD; Jue Zhang; Jing Fang, PhD; Jing Ma, MD; Cheng Zhang, MD
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Dept of Pulmonary Medicine,Peking University First Hospital, Beijing, Peoples Rep of China


Chest


Chest. 2009;136(4_MeetingAbstracts):67S. doi:10.1378/chest.136.4_MeetingAbstracts.67S-a
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Abstract

PURPOSE:  Wavelet transformation was used to analyze the sleep EEG signal of the obstructive sleep apnea hypoventilation syndrome(OSAHS) patients for the purpose of finding reliable parameter reflecting the disease severity.

METHODS:  142 patients suspected with OSAHS and accepted polysomonography were enrolled in the study. The average energy scale graph was got by the convert process to EEG signal with Morlet wavelet function, and two indexes of the variation of EEG energy, div1 and div2, were acquired by the calculation of the energy per time unit. Two characteristic parameters,p1 and p50, were got from the energy distribution of div1. So were and P10, P20 from div2 . The correlation between these parameters and OSAHS severity was analysis by Pearson correlation analysis.

RESULTS:  We have 32 patients with snoring only, 34 with mild, 37 with moderate and 39 with severe diseases. In OSAHS patients, the ranges of energy per unit time, mean values and standard deviations increased with the severity of disease. The encephalon electricity energy variation parameters p1, p50, P10 and P20, was significantly different in the severe groups from that of the other groups (p < 0.05); was significant higher in severe groups than that in mild or moderate groups(p < 0.05). With multiple regression analysis, the PSG indexes which had the best correlation with parameter , , P10( ) and P20( ) were total apnea time/total sleep time(TAT/TST), apnea index, time of SpO2 < 90%/TST, apnea time(normalization partial regression coefficient were −0.37, −0.72, 0.32, −0.60, p < 0.05).Conclusion: Morlet wavelet analysis could be used for the evaluation of sleep EEG abnormality in OSAHS patients.

CONCLUSION:  Morlet wavelet analysis could be used for the evaluation of sleep EEG abnormality in OSAHS patients. The four parameters we have defined,p1, p50, P10 and P20, could predict the severity of OSAHS.

CLINICAL IMPLICATIONS:  There is no report about Morlet wavelet analysis on the sleep EEG from OSAHS patients. From the study, we recognize that Morlet wagvelet could be used as an indicator of the disease severity.

DISCLOSURE:  Guangfa Wang, No Financial Disclosure Information; No Product/Research Disclosure Information

Tuesday, November 3, 2009

12:45 PM - 2:00 PM


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