利用经验模态分解从多参数监测信号中检测睡眠呼吸暂停

K. V. Madhav, E. Krishna, K. Reddy
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引用次数: 5

摘要

用于诊断阻塞性睡眠呼吸暂停(OSA),使用多导睡眠图(PSG)。使用PSG是检测睡眠呼吸暂停的金标准。本研究的主要目的是检测睡眠呼吸暂停从更常见的生理信号,如心电图(ECG)和光电容积脉搏图(PPG)信号在任何简单的床边多参数监测器。从ECG和PPG信号中提取的呼吸活动用于检测呼吸暂停发作。这个过程在不能记录PSG的情况下或作为可能的OSA患者的初步筛选试验是有用的。在本研究中,使用经验模式分解(EMD)方法获得的ecg衍生呼吸(EDR)和PPG衍生呼吸(PDR)信号用于检测OSA发作。使用MIMIC数据库中的信号进行实验。实验结果表明,该方法能够有效地从ECG和PPG信号中提取呼吸信息,用于阻塞性睡眠呼吸暂停综合征(OSAS)的检测。在时域和频域计算的相似度参数证实了这一点。高灵敏度和积极的预测水平表明了高度的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of sleep apnea from multiparameter monitor signals using empirical mode decomposition
For diagnosing obstructive sleep apnea (OSA), polysomnography (PSG) is used. Use of PSG is gold standard for detection of sleep apnea. This research is basically aimed at detection of sleep apnea from more commonly available physiological signals such as electrocardiogram (ECG) and photoplethysmographic (PPG) signals in any simple bedside multiparameter monitors. Respiratory activity extracted from ECG and PPG signals is used for the detection of apnea episodes. This process is useful in situations when recording of PSG is not possible or as a preliminary screening test of possible OSA in patients. In the present work ECG-derived respiration (EDR) and PPG derived respiration (PDR) signals, obtained using empirical mode decomposition (EMD) method, and are used to detect OSA episodes. Signals from MIMIC database were used for experimentation. The test results have revealed that the proposed method has efficiently extracted respiratory information from ECG and PPG signals for detection of obstructive sleep apnea syndrome (OSAS). The similarity parameters computed in both time and frequency domains have confirmed the same. High sensitivity and positive predictivity levels have revealed high degree of correctness.
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