Lifting Wavelet Based Signal Detection
Lifting Wavelet Based Signal Detection
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基于提升小波的信号检测
DOI：
10.1109/icaipr.2016.7585219
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Tomonori Tabusa
中科院分区：
文献类型：
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作者：
Koichi Kuzume;Tomonori Tabusa
信号的局部包络包含重要信息，例如图像中的边缘和心电图（ECG）中的QRS复合波。为了检测信号中的这种局部扰动，小波变换作为一种强有力的信号处理工具，已经成为信号处理应用的研究热点。信号突变时的小波极大值通常幅度较大。然而，只有小波极大值的幅度的信号的特征不能知道的细节。Mallat等人提出了用于多分辨率信号分析中观察信号跨尺度的Lipchitz正则性，但其计算成本相对昂贵。提出了一种基于提升二进小波变换的信号检测新方法。对Swelden公式中包含的提升小波参数进行了调整，使其适应待检测信号。调整这些参数的方法是在多分辨率分析中学习目标信号的特征。为了评估我们的方法，我们应用它们来检测包含在ECG中的QRS波群。实验结果表明，该方法能够准确地检测出目标信号。
Local regularities of a signal contain important information such as edges in an image and QRS complexes in an Electrocardiogram (ECG). In order to detect such local regularities in the signal, wavelet transform has been focused on as a powerful tool for signal processing applications. Wavelet maxima at the time in which the signal abruptly changes are usually large in amplitude. However, with only the magnitude of the wavelet maxima the features of the signal cannot be known in detail. Mallat et al. proposed the Lipchitz regularity for observing signal cross scales in multiresolution signal analysis, but its computational cost was relatively expensive. This paper presents a novel method for signal detection using lifting dyadic wavelet transform, which has the time-invariant property. The lifting wavelet parameters contained in Swelden's formula were tuned, adapting them to the signals to be detected. The method for tuning these parameters was to learn the features of the target signals in the multiresolution analysis. To evaluate our methods we applied them to detect the QRS complexes contained in an ECG. The results showed that our methods were useful to detect target signals accurately.