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| Content Provider | IET Digital Library |
|---|---|
| Author | Senay, Seda |
| Abstract | Time–frequency (TF) representations are very important tools to understand and explain circumstances, where the frequency content of non-stationary signals varies in time. A variety of biosignals such as speech, electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) show some form of non-stationarity. Considering Priestley's evolutionary (time-dependent) spectral theory for analysis of non-stationary signals, the authors defined a TF representation called evolutionary Slepian transform (EST). The evolutionary spectral theory generalises the definition of spectra while avoiding some of the shortcomings of bilinear TF methods. The performance of the EST in the representation of biosignals for the blind source separation (BSS) problem to extract information from a mixture of sources is studied. For example, in the case of EEG recordings, as electrodes are placed along the scalp, what is actually observed from EEG data at each electrode is a mixture of all the active sources. Separation of these sources from a mixture of observations is crucial for the analysis of recordings. In this study, they show that the EST can be used efficiently in the TF-based BSS problem of biosignals. |
| Starting Page | 242 |
| Ending Page | 246 |
| Page Count | 5 |
| Volume Number | 5 |
| e-ISSN | 20533713 |
| Issue Number | Issue 6, Dec (2018) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/htl/5/6 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5029 |
| Journal | Healthcare Technology Letters |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2018-11-15 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-No Derivs License (http://creativecommons.org/licenses/by-nd/3.0/) |
| Subject Keyword | Active Sources Bilinear TF Method Biosignals Blind Source Separation Blind Source Separation Problem Electroencephalography EST Evolutionary Spectral Theory Frequency Content Gaussian Processes Important Tool Iterative Method Mathematical Analysis Medical Signal Processing Nonstationarity Nonstationary Signal Varies Priestley's Evolutionary Signal Processing And Detection Signal Processing Theory Signal Reconstruction Spectral Analysis Statistics TF BSS TF Representation TF-based BSS Problem Time-dependent Time-frequency Analysis Time–frequency Representations |
| Content Type | Text |
| Resource Type | Article |
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