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From wavelets to adaptive approximations: time-frequency parametrization of EEG.

Durka PJ - Biomed Eng Online (2003)

Bottom Line: This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG).It covers in details two major steps: introduction of wavelets and adaptive approximations.This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Medical Physics, Institute of Experimental Physics, Warsaw University, Warszawa, Poland. piotr@durka.info

ABSTRACT
This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

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Related in: MedlinePlus

Sleep spindles selected from the MP parametrization of the same recordings as in Figure 16 – plots organized accordingly – chosen as structures with frequency 12–14 Hz, amplitude above 15 μV and time duration 0.5–2.5 seconds. Each dot in the plots marks one spindle in the frequency (horizontal, Hz) versus amplitude (vertical, μV) coordinates. Numbers on the right of each plot give increase of total power relative to the night after placebo (absent in first column), power carried by selected structures, number of structures per minute (in parenthesis total time of qualified stages in min.), average amplitude and its variance, and average (weighted by amplitude) frequency with variance
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Figure 17: Sleep spindles selected from the MP parametrization of the same recordings as in Figure 16 – plots organized accordingly – chosen as structures with frequency 12–14 Hz, amplitude above 15 μV and time duration 0.5–2.5 seconds. Each dot in the plots marks one spindle in the frequency (horizontal, Hz) versus amplitude (vertical, μV) coordinates. Numbers on the right of each plot give increase of total power relative to the night after placebo (absent in first column), power carried by selected structures, number of structures per minute (in parenthesis total time of qualified stages in min.), average amplitude and its variance, and average (weighted by amplitude) frequency with variance

Mentions: Figures 17 and 19 present each of the structures, classified as sleep spindles (Figure 17) or SWA (Figure 19) as a dot in the frequency-amplitude plane. These structures were selected using not only the frequency information, as in the "classical" approach, but also information on time duration and amplitude from Table 1. Total power carried by these structures, summed and normalized per time unit, is indicated on the right of each plot, together with the average number of occurrences per minute, average amplitude and frequency.


From wavelets to adaptive approximations: time-frequency parametrization of EEG.

Durka PJ - Biomed Eng Online (2003)

Sleep spindles selected from the MP parametrization of the same recordings as in Figure 16 – plots organized accordingly – chosen as structures with frequency 12–14 Hz, amplitude above 15 μV and time duration 0.5–2.5 seconds. Each dot in the plots marks one spindle in the frequency (horizontal, Hz) versus amplitude (vertical, μV) coordinates. Numbers on the right of each plot give increase of total power relative to the night after placebo (absent in first column), power carried by selected structures, number of structures per minute (in parenthesis total time of qualified stages in min.), average amplitude and its variance, and average (weighted by amplitude) frequency with variance
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC149437&req=5

Figure 17: Sleep spindles selected from the MP parametrization of the same recordings as in Figure 16 – plots organized accordingly – chosen as structures with frequency 12–14 Hz, amplitude above 15 μV and time duration 0.5–2.5 seconds. Each dot in the plots marks one spindle in the frequency (horizontal, Hz) versus amplitude (vertical, μV) coordinates. Numbers on the right of each plot give increase of total power relative to the night after placebo (absent in first column), power carried by selected structures, number of structures per minute (in parenthesis total time of qualified stages in min.), average amplitude and its variance, and average (weighted by amplitude) frequency with variance
Mentions: Figures 17 and 19 present each of the structures, classified as sleep spindles (Figure 17) or SWA (Figure 19) as a dot in the frequency-amplitude plane. These structures were selected using not only the frequency information, as in the "classical" approach, but also information on time duration and amplitude from Table 1. Total power carried by these structures, summed and normalized per time unit, is indicated on the right of each plot, together with the average number of occurrences per minute, average amplitude and frequency.

Bottom Line: This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG).It covers in details two major steps: introduction of wavelets and adaptive approximations.This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Medical Physics, Institute of Experimental Physics, Warsaw University, Warszawa, Poland. piotr@durka.info

ABSTRACT
This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

Show MeSH
Related in: MedlinePlus