Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier

2012 ◽  
Vol 108 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Luay Fraiwan ◽  
Khaldon Lweesy ◽  
Natheer Khasawneh ◽  
Heinrich Wenz ◽  
Hartmut Dickhaus
Keyword(s):  
Random Forest ◽  
Frequency Analysis ◽  
Sleep Stage ◽  
Random Forest Classifier ◽  
Identification System ◽  
Time Frequency Analysis ◽  
Time Frequency

Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates

2009 ◽  
Vol 35 (4) ◽  
pp. 693-702 ◽  
Author(s):  
Luay Fraiwan ◽  
Khaldon Lweesy ◽  
Natheer Khasawneh ◽  
Mohammad Fraiwan ◽  
Heinrich Wenz ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Sleep Stage ◽  
Preterm Neonates ◽  
Time Frequency Analysis ◽  
Time Frequency

scholarly journals V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4286
Author(s):  
Camelia Skiribou ◽  
Fouzia Elbahhar
Keyword(s):  
Random Forest ◽  
Noise Level ◽  
Spectrum Sharing ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Random Forest Classifier ◽  
Identification System ◽  
Signal Identification ◽  
Communication Signals ◽  
Time Frequency

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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