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使用离散小波变换的栓塞性多普勒超声信号检测

Embolic Doppler ultrasound signal detection using discrete wavelet transform.

作者信息

Aydin Nizamettin, Marvasti Farokh, Markus Hugh S

机构信息

School of Engineering and Electronic, The University of Edinburgh, Edinburgh EH9 3JL, UK.

出版信息

IEEE Trans Inf Technol Biomed. 2004 Jun;8(2):182-90. doi: 10.1109/titb.2004.828882.

Abstract

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

摘要

无症状循环栓子可通过多普勒超声检测到。栓塞性多普勒超声信号是持续时间短的瞬态类似信号。小波变换通过优化时频分辨率,是分析和检测此类信号的理想方法。我们提出了一种基于离散小波变换(DWT)的检测系统,并研究了一些可能有助于描述栓塞信号(ES)的参数。我们使用了基于Daubechies八阶小波滤波器且具有八个尺度的快速DWT算法。为了评估ES的DWT的可行性,使用了两个独立的数据集,每个数据集都由包含一个ES(N = 100)、伪像(N = 100)或多普勒斑点(DS)(N = 100)的短片段组成。将DWT应用于数据后,评估了几个参数。用于两个数据集的阈值使用第一个数据集进行了优化。虽然由伪像产生的DWT系数主要出现在较高尺度(五、六、七和八),但较低尺度(一、二、三、四)的DWT系数主要由ES和DS主导。DWT能够在变换过程中固有地滤除大部分伪像。对于第一个数据集,100个ES中有98个被检测为ES。对于第二个数据集,使用相同阈值时,100个ES中有95个被检测为ES。该算法还使用包含202个正常ES的第三个数据集进行了测试;198个信号被检测为ES。

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