Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis

2018 ◽  
Vol 155 ◽  
pp. 11-17 ◽  
Author(s):  
Maie Bachmann ◽  
Laura Päeske ◽  
Kaia Kalev ◽  
Katrin Aarma ◽  
Andres Lehtmets ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Single Channel ◽  
Linear And Nonlinear ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

scholarly journals Protein Aggregation/Folding: The Role of Deterministic Singularities of Sequence Hydrophobicity as Determined by Nonlinear Signal Analysis of Acylphosphatase and Aβ(1–40)

2003 ◽  
Vol 85 (6) ◽  
pp. 3544-3557 ◽  
Author(s):  
Joseph P. Zbilut ◽  
Alfredo Colosimo ◽  
Filippo Conti ◽  
Mauro Colafranceschi ◽  
Cesare Manetti ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Signal Analysis ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

Nonlinear Signal Analysis Methods in the Elucidation of Protein Sequence−Structure Relationships

2002 ◽  
Vol 102 (5) ◽  
pp. 1471-1492 ◽  
Author(s):  
Alessandro Giuliani ◽  
Romualdo Benigni ◽  
Joseph P. Zbilut ◽  
Charles L. Webber, ◽  
Paolo Sirabella ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Signal Analysis ◽  
Sequence Structure ◽  
Analysis Methods ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

ChemInform Abstract: Nonlinear Signal Analysis Methods in the Elucidation of Protein Sequence-Structure Relationships

ChemInform ◽  
2010 ◽  
Vol 33 (28) ◽  
pp. no-no ◽  
Author(s):  
Alessandro Giuliani ◽  
Romualdo Benigni ◽  
Joseph P. Zbilut ◽  
Charles L. Webber Jr. ◽  
Paolo Sirabella ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Signal Analysis ◽  
Sequence Structure ◽  
Analysis Methods ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

A new similarity index for nonlinear signal analysis based on local extrema patterns

2018 ◽  
Vol 382 (5) ◽  
pp. 288-299 ◽  
Author(s):  
Hamid Niknazar ◽  
Ali Motie Nasrabadi ◽  
Mohammad Bagher Shamsollahi
Keyword(s):  
Signal Analysis ◽  
Similarity Index ◽  
Local Extrema ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis

Fault Diagnosis of Offshore Platforms Using the Local Mean Decomposition Method

2011 ◽  
Vol 365 ◽  
pp. 94-97
Author(s):  
Jin Shan Lin
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Offshore Platforms ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Local Mean ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis ◽  
Emd Method

Traditional techniques are not suitable for exploring non-stationary and nonlinear signals. Although empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis, yet it still has some shortcomings. Local mean decomposition (LMD), a novel signal processing method, seemingly overcomes many deficiencies of the EMD method and can take place of the EMD method for analyzing non-stationary and nonlinear signals. In this paper, the LMD method is employed to examine the signal captured from the decks of the WZ12-1 platform and succeeds in displaying the reasons causing the excessive vibration of the WZ12-1 platform. The results suggest that the LMD method seems to be a feasible method for fault diagnosis of offshore platforms.

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