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Classification of sleep apnea using cross wavelet transform

机译：交叉小波变换对睡眠呼吸暂停的分类

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In this paper, a novel approach for classifying sleep apneas using cross wavelet transform has been proposed. This is the first time that cross wavelet transform has ever been applied to sleep apnea type classification. The developed method takes the airflow and thoracic effort signals, as an in-put, which are then transformed to time-frequency and phase plane in order to extract the information of correlation between the two signals during different apnea condition. As the cross-wavelet returns large number of coefficients, which may be difficult to handle in some automated detection system, therefore dimension reduction was necessary. In the work, kernel principal component analysis (KPCA) based dimension reduction technique has been applied, and four Eigen values from each of the cross-wavelet amplitude and phase coefficients found to be effective for detection of apnea into three categories i.e., obstructive, central and mixed. The proposed system has been tested on the recordings obtained from 23 subjects. The average classification rate obtained using simple threshold technique was 85% ± 0.78%, and the values for each class were 85.2% (obstructive), 86.4% (central) and 83.6% (mixed). The results show that cross-wavelet is useful in order to distinguish the apneas, as it looks into the phase and amplitude coherence between the two signals.
机译：在本文中，提出了一种使用交叉小波变换对睡眠呼吸暂停进行分类的新方法。这是交叉小波变换首次应用于睡眠呼吸暂停类型分类。所开发的方法以气流和胸廓力信号为输入，然后将其转换为时频和相位平面，以提取不同呼吸暂停条件下两个信号之间的相关信息。由于交叉小波返回大量系数，这在某些自动检测系统中可能很难处理，因此有必要减小尺寸。在这项工作中，已经应用了基于核主成分分析（KPCA）的降维技术，并且发现来自每个交叉小波振幅和相位系数的四个特征值可有效地检测呼吸暂停，分为阻塞性，中央性三类。和混合。所提议的系统已经从23名受试者的录音中进行了测试。使用简单阈值技术获得的平均分类率为85％±0.78％，每个类别的值分别为85.2％（阻塞性），86.4％（中央）和83.6％（混合）。结果表明，交叉小波可用于区分呼吸暂停，因为它着眼于两个信号之间的相位和幅度相干性。

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