A Multiscale Energy-Based Time-Domain Approach for Interference Detection in Non-stationary Signals

2020 ◽  
pp. 36-47
Author(s):  
Vittoria Bruni ◽  
Lorenzo Della Cioppa ◽  
Domenico Vitulano
Keyword(s):  
Time Domain ◽  
Interference Detection ◽  
Stationary Signals

scholarly journals Influence of Wilbraham-Gibbs Phenomenon on Digital Stochastic Measurement of EEG Signal Over an Interval

2014 ◽  
Vol 14 (5) ◽  
pp. 270-278 ◽  
Author(s):  
P. Sovilj ◽  
M. Milovanović ◽  
D. Pejić ◽  
M. Urekar ◽  
Z. Mitrović
Keyword(s):  
Time Domain ◽  
Input Signal ◽  
Gibbs Phenomenon ◽  
Measurement Methods ◽  
Eeg Signal ◽  
Measurement Interval ◽  
Maximal Error ◽  
Small Series ◽  
Stationary Signals ◽  
The Time Domain

Abstract Measurement methods, based on the approach named Digital Stochastic Measurement, have been introduced, and several prototype and small-series commercial instruments have been developed based on these methods. These methods have been mostly investigated for various types of stationary signals, but also for non-stationary signals. This paper presents, analyzes and discusses digital stochastic measurement of electroencephalography (EEG) signal in the time domain, emphasizing the problem of influence of the Wilbraham-Gibbs phenomenon. The increase of measurement error, related to the Wilbraham-Gibbs phenomenon, is found. If the EEG signal is measured and measurement interval is 20 ms wide, the average maximal error relative to the range of input signal is 16.84 %. If the measurement interval is extended to 2s, the average maximal error relative to the range of input signal is significantly lowered - down to 1.37 %. Absolute errors are compared with the error limit recommended by Organisation Internationale de Métrologie Légale (OIML) and with the quantization steps of the advanced EEG instruments with 24-bit A/D conversion

Dynamical correlation of non-stationary signals in time domain—A comparative study

2010 ◽  
Vol 5 (3) ◽  
pp. 205-213 ◽  
Author(s):  
M. Muthuraman ◽  
A. Galka ◽  
G. Deuschl ◽  
U. Heute ◽  
J. Raethjen
Keyword(s):  
Comparative Study ◽  
Time Domain ◽  
Dynamical Correlation ◽  
Stationary Signals

The Application of the Wavelet Transform to Polysomnographic Signals

2003 ◽  
Vol 01 (03) ◽  
pp. 263-274 ◽  
Author(s):  
M. MacCallum ◽  
A. E. A. Almaini
Keyword(s):  
Time Domain ◽  
Quantitative Information ◽  
Sleep Apnoea ◽  
Visual Evaluation ◽  
Data Set ◽  
Specific Patient ◽  
Obstructive Sleep ◽  
Eeg Data ◽  
Stationary Signals ◽  
The Time Domain

Polysomnographic (sleep) signals are recorded from patients exhibiting symptoms of a suspected sleep disorder such as Obstructive Sleep Apnoea (OSA). These non-stationary signals are characterised by having both quantitative information in the frequency domain and rich, dynamic data in the time domain. The collected data is subsequently analysed by skilled visual evaluation to determine whether arousals are present, an approach which is both time-consuming and subjective. This paper presents a wavelet-based methodology which seeks to alleviate some of the problems of the above method by providing: (a) an automated mechanism by which the appropriate stage of sleep for disorder observation may be extracted from the composite electroencephalograph (EEG) data set and (b) an ensuing technique to assist in the diagnosis of full arousal by correlation of wavelet-extracted information from a number of specific patient data sources (e.g. pulse oximetry, electromyogram [EMG] etc.).

APPLICATION OF NON-STATIONARY TECHNIQUES IN THE ANALYSIS OF ELECTROCHEMICAL NOISE

2004 ◽  
Vol 04 (02) ◽  
pp. L267-L272 ◽  
Author(s):  
K. DAROWICKI ◽  
A. ZIELIŃSKI
Keyword(s):  
Discrete Time ◽  
Time Domain ◽  
Fourier Transformation ◽  
Electrochemical Noise ◽  
Time Measurement ◽  
Time Dependent ◽  
Noise Resistance ◽  
Stationary Signals ◽  
Short Time ◽  
Short Time Fourier Transformation

Approaches to the electrochemical noise (EN) analysis are generally based on the assumption of its stationarity. In spite of usability of such methodology, occurrences of non-stationary signals are rather frequent in the practice. The methods of overcoming this drawback are presented in the paper. Applications of short-time Fourier transformation are discussed. It utilizes the concept of time-localized analyzing functions generated by appropriately chosen sliding time-domain window. Such technique, applied in discrete time measurement regime yields time-dependent frequency EN decomposition. Additionally, the authors introduce the concept of time dependent spectral noise response and spectral noise resistance.

Decomposition and Analysis of Non-Stationary Dynamic Signals Using the Hilbert Transform

2008 ◽  
Author(s):  
Michael Feldman
Keyword(s):  
Hilbert Transform ◽  
Time Domain ◽  
Instantaneous Frequency ◽  
Decomposition Method ◽  
Initial Signal ◽  
Time Modeling ◽  
Stationary Signals ◽  
A New Technique ◽  
The Time Domain ◽  
The Hilbert Transform

This paper describes a new technique, called the Hilbert Vibration Decomposition method, dedicated to decomposition of non-stationary wideband dynamic signals. Using the Hilbert transform in the time domain, we extract a number of elementary oscillating components of the initial signal, who’s both the instantaneous frequency and envelope can vary in time. Modeling examples of decomposition of non-stationary signals are included.

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