Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

A class of highly concentrated time-frequency distributions based on the ambiguity domain representation and complex-lag moment

Abstract: A class of time-frequency distributions with complex-lag argument is proposed. It is based on the ambiguity domain representations of real and complex-lag moment, combined to provide a cross-terms free representation for multicomponent signals. Furthermore, the distributions from the proposed class provide a more effective instantaneous frequency estimation for signals with fast varying phase function than the existing approach. The theory is illustrated by the examples.

A Novel Method to Detect Multiple Arrhythmias Based on Time-Frequency Analysis and Convolutional Neural Networks

Abstract: Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring.

Linear vs. bilinear time-frequency methods for interference mitigation in direct sequence spread spectrum communication systems

Abstract: Time frequency signal representations have been examined for interference mitigation in spread spectrum (SS) communication systems. We implement a direct sequence spread spectrum (DS-SS) system with additive jamming and utilize two distinct time-frequency (t-f) methods to mitigate the interference, namely the shift-covariant class of time frequency distributions (TFD) and the Gabor (1946) transform. The TFD is a bilinear transform and is shown to improve the receiver characteristics in a highly nonstationary jamming environment. The Gabor transform is a linear method that has real-time computational advantages yet has a limited class of jammers against which it performs well. A common feature in these two excision methods is that the t-f points are analyzed with the same time resolutions and frequency resolutions. The performance of both methods under several jamming scenarios is discussed and computational considerations are addressed.