论文标题
自动无创呼吸周期隔离
Automatic Non-Invasive Isolation of Respiratory Cycles
论文作者
论文摘要
在本文中，我们引入了一种新型算法，该算法旨在分离胸部呼吸感上的单个呼吸周期。该算法使用信号处理和统计方法定位呼吸，并可以在单个呼吸水平上分析睡眠数据。评估了7.3小时的手持数据，或总共8782个单独的呼吸评估算法，估计可以正确分离94％的呼吸周期，同时产生的假阳性仅产生可检测总数的5％。该算法在包含大量睡眠呼吸事件的数据上进行了专门评估。我们发现，在呼吸呼吸的情况下检测到呼吸时，该算法在准确性方面没有受到影响。还对大量参与者进行了评估算法，我们发现算法的准确性在参与者之间是一致的。该算法最终通过开源Python库公开。
In this paper, we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal. The algorithm locates breaths using signal processing and statistical methods and enables the analysis of sleep data on an individual breath level. The algorithm was evaluated on 7.3 hours of hand-annotated data, or 8782 individual breaths in total, and was estimated to correctly isolate 94% of respiratory cycles while producing false positives that amount to only 5% of the total number of detections. The algorithm was specifically evaluated on data containing a great number of sleep-disordered breathing events. We found that the algorithm did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across a large set of participants, and we found that the accuracy of the algorithm was consistent across participants. This algorithm is finally made public via an open-source Python library.
