Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

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General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720911009 ◽

2020 ◽

Vol 16 (3) ◽

pp. 155014772091100 ◽

Cited By ~ 1

Author(s):

Ahmad al-Qerem ◽

Faten Kharbat ◽

Shadi Nashwan ◽

Staish Ashraf ◽

khairi blaou

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Differential Evolution ◽

Discrete Wavelet Transform ◽

Research Work ◽

Discrete Wavelet ◽

Proposed Model ◽

Feature Extraction And Selection ◽

Selection Step ◽

Wavelet Family

Wavelet family and differential evolution are proposed for categorization of epilepsy cases based on electroencephalogram (EEG) signals. Discrete wavelet transform is widely used in feature extraction step because it efficiently works in this field, as confirmed by the results of previous studies. The feature selection step is used to minimize dimensionality by excluding irrelevant features. This step is conducted using differential evolution. This article presents an efficient model for EEG classification by considering feature extraction and selection. Seven different types of common wavelets were tested in our research work. These are Discrete Meyer (dmey), Reverse biorthogonal (rbio), Biorthogonal (bior), Daubechies (db), Symlets (sym), Coiflets (coif), and Haar (Haar). Several kinds of discrete wavelet transform are used to produce a wide variety of features. Afterwards, we use differential evolution to choose appropriate features that will achieve the best performance of signal classification. For classification step, we have used Bonn databases to build the classifiers and test their performance. The results prove the effectiveness of the proposed model.

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Detection of Attention Deficit Hyperactivity Disorder from EEG Signal using Discrete Wavelet Transform

2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA) ◽

10.1109/iccubea47591.2019.9128977 ◽

2019 ◽

Author(s):

R. Catherine Joy ◽

S. Thomas George ◽

A. Albert Rajan ◽

M. S. P. Subathra

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Attention Deficit ◽

Discrete Wavelet ◽

Eeg Signal ◽

Hyperactivity Disorder

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Comparison of different feature extraction methods for the analysis of uterine magnetomyography signals to predict term labor

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.29.18449 ◽

2018 ◽

Vol 7 (3.29) ◽

pp. 1

Author(s):

T Ananda Babu ◽

Dr P. Rajesh Kumar

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Wavelet Packet ◽

Extraction Methods ◽

Wavelet Packet Transform ◽

Svm Classifier ◽

Discrete Wavelet ◽

Term Labor

The prediction of term labor by analyzing the uterine magnetomyographic signals attempted in this research. The existing works did not focus on the classification of the signals. Publicly available MIT-BIH database records were divided into term-labor and term-nonlabor groups. This research presents two methods for feature extraction, discrete wavelet transform and wavelet packet transform. Energy, standard deviation, variance, entropy and waveform length of transform coefficients used in the first method. The normalized logarithmic energy of wavelet coefficients from each packet of the total wavelet packet tree used as the feature space for the second method. The labor assessment done through the classification of the features by using five different classifiers for different mother wavelet families. Discrete wavelet transform features extracted using coif5 wavelet with random subspace classification gives the accuracy, precision and FPrates of 93.9286%, 94.2014% and 5.7986% respectively. Using sym8 wavelet for wavelet packet transform features classified with SVM classifier performed well with 95.8763% accuracy, 95.9719% precision and 4.0281% FPrate. The results obtained from the research will be helpful in term labor assessment and understanding the parturition process.

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