Jestrović Iva, Coyle James L, Sejdić Ervin
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
Signal Processing. 2017 Feb;131:483-491. doi: 10.1016/j.sigpro.2016.09.008. Epub 2016 Sep 16.
The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms.
加窗傅里叶变换(短时傅里叶变换)和S变换是广泛用于从非平稳信号中提取频率信息的信号处理工具。此前,加窗傅里叶变换已被应用于图信号,并已证明对于从图中提取顶点频率信息非常有用。然而,高计算复杂度使得这些算法不切实际。我们试图开发一种快速加窗图傅里叶变换和一种快速图S变换,它们需要显著更短的计算时间。所提出的方案已经用合成测试图信号和从吞咽期间进行的脑电图记录中获得的真实图信号进行了测试。结果表明,与标准加窗图傅里叶变换和快速图S变换相比,所提出的方案提供了显著更低的计算时间。此外,结果表明噪声对快速加窗图傅里叶变换算法或图S变换的结果没有影响。最后,我们表明可以从用所提出的算法获得的顶点频率表示中重建图。