Comput. Biol. Med. - Filtering multifocal VEP signals using Prony's method.

Tópicos

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Resumo

CKGROUND: This paper describes use of Prony's method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony's method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles.METHOD: By selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony's method.RESULTS: Filtering using Prony's method improved the signals' original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver-operating-characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony's method than in the original signals or in those filtered using the FFT.CONCLUSION: filtering using Prony's method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.

Resumo Limpo

ckground paper describ use proni method filter appli multifoc visualevokedpotenti mfvep signal proni method can view extens fourier analysi allow signal decompos linear combin function differ amplitud damp factor frequenc phase anglesmethod select proni method paramet frequenc filter develop improv signaltonois ratio snr three differ criteria appli data record control subject produc three separ dataset unfilt raw data data filter use tradit method fast fourier transform fft data filter use proni methodresult filter use proni method improv signal origin snr fft filter improv snr extent signal can separ nois analys use receiveroperatingcharacterist roc curv area curv auc greater signal filter use proni method origin signal filter use fftconclus filter use proni method improv qualiti mfvep signal preprocess compar origin signal filter use fft

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