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Time-frequency BSS of biosignals
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Institution of Engineering and Technology
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6275134/ https://www.ncbi.nlm.nih.gov/pubmed/30568802 http://dx.doi.org/10.1049/htl.2018.5029 |
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author | Senay, Seda |
author_facet | Senay, Seda |
author_sort | Senay, Seda |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6275134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-62751342018-12-19 Time-frequency BSS of biosignals Senay, Seda Healthc Technol Lett Article 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. The Institution of Engineering and Technology 2018-11-15 /pmc/articles/PMC6275134/ /pubmed/30568802 http://dx.doi.org/10.1049/htl.2018.5029 Text en http://creativecommons.org/licenses/by-nd/3.0/ This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/) |
spellingShingle | Article Senay, Seda Time-frequency BSS of biosignals |
title | Time-frequency BSS of biosignals |
title_full | Time-frequency BSS of biosignals |
title_fullStr | Time-frequency BSS of biosignals |
title_full_unstemmed | Time-frequency BSS of biosignals |
title_short | Time-frequency BSS of biosignals |
title_sort | time-frequency bss of biosignals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6275134/ https://www.ncbi.nlm.nih.gov/pubmed/30568802 http://dx.doi.org/10.1049/htl.2018.5029 |
work_keys_str_mv | AT senayseda timefrequencybssofbiosignals |