Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection

Pattern Recognition ◽

10.1016/j.patcog.2014.08.016 ◽

2015 ◽

Vol 48 (3) ◽

pp. 616-627 ◽

Author(s):

Boualem Boashash ◽

Ghasem Azemi ◽

Nabeel Ali Khan

Keyword(s):

Feature Extraction ◽

Change Detection ◽

Abnormality Detection ◽

Time Frequency ◽

Stationary Signals ◽

Newborn Eeg ◽

Frequency Feature

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Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

EURASIP Journal on Advances in Signal Processing ◽

10.1155/s1110865704406167 ◽

2004 ◽

Vol 2004 (16) ◽

Author(s):

Hamid Hassanpour ◽

Mostefa Mesbah ◽

Boualem Boashash

Keyword(s):

Feature Extraction ◽

Time Frequency ◽

Newborn Eeg ◽

Frequency Feature

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Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction

Computer Speech & Language ◽

10.1016/j.csl.2021.101216 ◽

2021 ◽

pp. 101216

Author(s):

Biswajit Karan ◽

Sitanshu Sekhar Sahu ◽

Juan Rafael Orozco-Arroyave ◽

Kartik Mahto

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Feature Extraction ◽

Matrix Factorization ◽

Disease Prediction ◽

Time Frequency ◽

Voice Signal ◽

Frequency Feature ◽

Non Negative Matrix Factorization

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Time-Frequency Feature Extraction from Residues of Voiced Phonemes

IEEE Latin America Transactions ◽

10.1109/tla.2004.1468648 ◽

2004 ◽

Vol 2 (3) ◽

pp. 168-173

Author(s):

E.V. de Payer

Keyword(s):

Feature Extraction ◽

Time Frequency ◽

Frequency Feature

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An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.198-199.803 ◽

2012 ◽

Vol 198-199 ◽

pp. 803-807

Author(s):

Feng Li Wang ◽

Shu Lin Duan ◽

Hong Tao Gao

Keyword(s):

Feature Extraction ◽

Frequency Analysis ◽

Local Energy ◽

Simulation Research ◽

Time Frequency Analysis ◽

Process Systems ◽

Frequency Space ◽

Time Frequency ◽

Stationary Signals ◽

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

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Optimizing Time-Frequency Feature Extraction and Channel Selection through Gradient Backpropagation to Improve Action Decoding based on Subthalamic Local Field Potentials

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9175885 ◽

2020 ◽

Author(s):

Thomas Martineau ◽

Shenghong He ◽

Ravi Vaidyanathan ◽

Peter Brown ◽

Huiling Tan

Keyword(s):

Feature Extraction ◽

Local Field ◽

Local Field Potentials ◽

Channel Selection ◽

Field Potentials ◽

Time Frequency ◽

Frequency Feature

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Time-frequency feature extraction from frequency-modulated sounds in a population of auditory cortical neurons

Acoustical Science and Technology ◽

10.1250/ast.29.36 ◽

2008 ◽

Vol 29 (1) ◽

pp. 36-40 ◽

Author(s):

Shunji Sugimoto ◽

Yuta Suzuki ◽

Hiroyuki Tanaka ◽

Michinori Kubota ◽

Junsei Horikawa

Keyword(s):

Feature Extraction ◽

Cortical Neurons ◽

Time Frequency ◽

Frequency Feature

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Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study

Knowledge-Based Systems ◽

10.1016/j.knosys.2016.05.027 ◽

2016 ◽

Vol 106 ◽

pp. 38-50 ◽

Author(s):

Boualem Boashash ◽

Samir Ouelha

Keyword(s):

Machine Learning ◽

Abnormality Detection ◽

Time Frequency ◽

Signal Abnormality ◽

Newborn Eeg ◽

Frequency Features

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Time–Frequency Feature Extraction of Acoustic Emission Signals in Aluminum Alloy MIG Welding Process Based on SST and PCA

IEEE Access ◽

10.1109/access.2019.2935117 ◽

2019 ◽

Vol 7 ◽

pp. 113988-113998 ◽

Author(s):

Kuanfang He ◽

Xuejun Li

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Aluminum Alloy ◽

Welding Process ◽

Mig Welding ◽

Time Frequency ◽

Frequency Feature

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A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds

Journal of Central South University ◽

10.1007/s11771-019-4116-5 ◽

2019 ◽

Vol 26 (6) ◽

pp. 1607-1618 ◽

Author(s):

Zhong-wei Zhang ◽

Huai-hai Chen ◽

Shun-ming Li ◽

Jin-rui Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Intelligent Fault Diagnosis ◽

Softmax Regression ◽

Time Frequency ◽

Sparse Filtering ◽

Frequency Feature ◽

Filtering Approach

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Change Detection by Feature Extraction and Processing from Time-Frequency Images

2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) ◽

10.1109/ecai.2018.8678973 ◽

2018 ◽

Author(s):

Dorel Aiordachioaie ◽

Theodor D. Popescu

Keyword(s):

Feature Extraction ◽

Change Detection ◽

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