Open Access
Wavelets, adapted waveforms and de-noising.
Ronald R. Coifman,Mladen Victor Wickerhauser +1 more
- 01 Jan 1996
- Vol. 45, pp 57-78
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TL;DR: Applications of new libraries of waveforms well-adapted to various numerical analysis and signal processing tasks are described, including a feature extraction and data compression algorithm for speech signals, and an adapted waveform analysis algorithm for removing fish noises from hydrophone recordings.
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Abstract: This is a short summary of a talk given at the Frontier Science in EEG Symposium, Continuous Waveform Analysis, held on 9 October 1993 in New Orleans. We describe some new libraries of waveforms well-adapted to various numerical analysis and signal processing tasks. The main point is that by expanding a signal in a library of waveforms which are well-localized in both time and frequency, one can achieve both understanding of structure and efficiency in computation. We briefly cover the properties of the new "wavelet packet" and "localized trigonometric" libraries. The main focus will be applications of such libraries to the analysis of complicated transient signals: a feature extraction and data compression algorithm for speech signals which uses best-adapted time and frequency decompositions, and an adapted waveform analysis algorithm for removing fish noises from hydrophone recordings. These signals share many of the same properties as EEG traces, but with distinct features that are easier to characterize and detect.
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Citations
Early seizure detection.
Kristin K. Jerger,Theoden I. Netoff,Theoden I. Netoff,Joseph T. Francis,Joseph T. Francis,Timothy Sauer,Louis M. Pecora,Steven L. Weinstein,Steven J. Schiff +8 more
TL;DR: All the methods described here were successful in detecting changes leading to a seizure between one and two minutes before the first changes noted by the neurologist, although analysis of phase correlation proved the most robust.
A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs
TL;DR: The development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of aWavelet, in a wavelets/filters transform for improving EEG wavelet analysis, intended for brain-computer interfaces.
Correlation network analysis for data integration and biomarker selection.
Aram Adourian,Ezra G. Jennings,Raji Balasubramanian,Wade M. Hines,Doris Damian,Thomas N. Plasterer,Clary B. Clish,Paul Stroobant,Robert N. McBurney,Elwin Verheij,Ivana Bobeldijk,Jan van der Greef,Johan Lindberg,Kerstin Kenne,Ulf Andersson,Heike Hellmold,Kerstin Nilsson,Hugh Salter,Ina Schuppe-Koistinen +18 more
TL;DR: This work presents a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers for drug-induced hepatic toxicity effects in a rodent model.
Single trial analysis of event related potentials: non-linear de-noising with wavelets
TL;DR: In this paper, a method for single trial analysis of event related potentials (ERPs) that combines techniques from non-linear time series analysis with the wavelet transform is presented.
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Nonlinear denoising of transient signals with application to event-related potentials
TL;DR: A new wavelet-based method for the denoising of event-related potentials (ERPs), employing techniques recently developed for the paradigm of deterministic chaotic systems, which enables the study of individual ERPs against strong ongoing brain electrical activity.
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