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Dyadic lifting wavelet based signal detection

机译：基于二元提升小波的信号检测

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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.
机译：信号的本地正则规则量包含在心电图（ECG）中图像和QRS复合物中的重要信息，例如图像和QRS复合物。为了检测信号中的这种本地规律，小波变换一直专注于信号处理应用的强大工具。在信号突然变化的时间内的小波最大值通常在幅度中大。然而，只有小波最大值的大小，不能详细了解信号的特征。 Mallat等人。提出了用于观察多分辨率信号分析中的信号交叉尺度的Lipchitz规律性，但其计算成本相对昂贵。本文介绍了使用提升二元小波变换的信号检测方法，具有时间不变的属性。调整瑞士文公式中包含的提升小波参数，调整它们以检测的信号。调整这些参数的方法是学习多分辨率分析中目标信号的特征。为了评估我们的方法，我们将它们应用于检测心电图中包含的QRS复合物。结果表明，我们的方法可准确地检测目标信号。

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