Automatic removal of high-amplitude artefacts from single-channel electroencephalograms

Teixeira, António; Tomé, Ana Maria; Lang, Elmar; Gruber, Peter J.; Silva, António Martins da

doi:10.1016/j.cmpb.2006.06.003

Cited by 58 publications

(33 citation statements)

References 16 publications

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“…Projective subspace models, applied to time series data sets, can be found in literature under various names depending on the domain of application: Singular Spectrum Analysis (SSA) (for instance in climate time series analysis) [6,5,18] and Singular Value Decomposition (SVD) (for instance in speech enhancement) [4,8,10]. The aim of SSA is to achieve a decomposition of the original time series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and noise.…”

Section: Introductionmentioning

confidence: 99%

SSA of biomedical signals: A linear invariant systems approach

Figueiredo

Georgieva

Lang

et al. 2010

Statistics and Its Interface

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Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters. The number of filters is determined by the embedding dimension. We propose to explicitly define the frequency response of each filter responsible for the selection of informative components. We also introduce a subspace distance measure for clustering subspace models. We illustrate the methodology by analyzing Electroencephalograms (EEG).

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Section: Introductionmentioning

confidence: 99%

SSA of biomedical signals: A linear invariant systems approach

Figueiredo

Georgieva

Lang

et al. 2010

Statistics and Its Interface

Self Cite

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show abstract

“…In fact, m needs to be ''big enough'' to capture the information content necessary, especially when the time series data is heavily correlated [12]. Thus, provided the data were sampled using a reasonable rate (according to the Nyquist criterion), and by setting the lag s-time to one, it is possible to choose the practical minimum size for m only by considering the lowest frequency of the periodic components we are looking for as [5,12,27]…”

Section: Preparing a Multidimensional Datasetmentioning

confidence: 99%

Extracting sources from noisy abdominal phonograms: a single-channel blind source separation method

Jiménez-González

James

2009

Med Biol Eng Comput

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In this work we highlight a methodology that extracts sources from noisy single-channel abdominal phonograms. First, an appropriate matrix of delays is constructed. Next, multiple independent components are calculated using the FastICA algorithm. Then these components are projected back to the measurement space and classified for recovering the sources of interest. Single-channel phonograms obtained from three different subjects were analysed. Results show successful extraction of foetal heart sounds (FHS), maternal respiration/pulse wave, and line noise. It is important to point out the high performance of the method for extracting the former two as separate sources; especially due to the fact that pulse wave and FHS may overlap as maternal and foetal QRSs do in the abdominal ECG. The most outstanding factor is that this is achieved using a single-channel method. So, this approach extracts physiological sources from noisy abdominal phonograms, and we believe it will be useful for surveillance, not only for foetal well-being but also for maternal condition.

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“…5a, it can be seen that there is a significant and unexpected drop in the RSC for the period between 70 and 85 s (preictal state). This sharp and unexpected drop is caused by short-time and high-amplitude artefacts [12,29], as marked in Fig. 5b.…”

Section: Rsc Analysis Of Real Newborn Eegmentioning

confidence: 99%

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

2007

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This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity (RSC), which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed RSC measure is used in the analysis of newborn electroencephalogram (EEG). To do this, firstly, a time-frequency decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

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Automatic removal of high-amplitude artefacts from single-channel electroencephalograms

Cited by 58 publications

References 16 publications

SSA of biomedical signals: A linear invariant systems approach

SSA of biomedical signals: A linear invariant systems approach

Extracting sources from noisy abdominal phonograms: a single-channel blind source separation method

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

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