A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain
Tài liệu tham khảo
Rajendra Acharya, 2012, Automated diagnosis of epileptic eeg using entropies, Biomed. Signal Process Contr., 7, 401, 10.1016/j.bspc.2011.07.007
Alcin, 2016, Multi-category EEG signal classification developing time–frequency texture features based Fisher Vector encoding method, Neurocomputing, 218, 251, 10.1016/j.neucom.2016.08.050
Ali, 2013, Spectrum sensing with spatial signatures in the presence of noise uncertainty and shadowing, EURASIP J. Wirel. Commun. Netw., 2013, 10.1186/1687-1499-2013-150
Ali, 2017, Blind source separation schemes for mono-sensor and multi-sensor systems with application to signal detection, Circ. Syst. Signal Process., 1
Altunay, 2010, Epileptic eeg detection using the linear prediction error energy, Expert Syst. Appl., 37, 5661, 10.1016/j.eswa.2010.02.045
Bajaj, 2017, Time-frequency image based features for classification of epileptic seizures from eeg signals, Biomedical Physics & Engineering Express, 3, 10.1088/2057-1976/aa5199
Barkat, 2004, Algorithms for blind components separation and extraction from the time-frequency distribution of their mixture, EURASIP J. Appl. Signal Process., 2004, 2025
Bhattacharyya, 2017, Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic eeg signals, Appl. Sci., 7, 385, 10.3390/app7040385
Boashash, 2015, Principles of time-frequency feature extraction for change detection in non-stationary signals: applications to newborn eeg abnormality detection, Pattern Recogn., 48, 616, 10.1016/j.patcog.2014.08.016
Boashash, 2015, Time-frequency features for pattern recognition using high-resolution tfds: a tutorial review, Digit. Signal Process., 40, 1, 10.1016/j.dsp.2014.12.015
Boubchir, 2014, Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of eeg data, 32
Boubchir, 2014, 32
Cohen, 1989, Time-frequency distributions-a review, Proc. IEEE, 77, 941, 10.1109/5.30749
Hlawatsch, 1992, Linear and quadratic time-frequency signal representations, IEEE Signal Processing Magazine, 9, 21, 10.1109/79.127284
Hodges, 1996, A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography, Electroencephalogr. Clin. Neurophysiol. Electromyogr. Mot. Contr., 101, 511
Hopfengärtner, 2007, An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp eeg recordings, Clin. Neurophysiol., 118, 2332, 10.1016/j.clinph.2007.07.017
Hunyadi, 2017, Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy, J. Neurosci. Meth., 287, 13, 10.1016/j.jneumeth.2017.05.024
Joshi, 2014, Classification of ictal and seizure-free eeg signals using fractional linear prediction, Biomed. Signal Process Contr., 9, 1, 10.1016/j.bspc.2013.08.006
Kalbkhani, 2017, Stockwell transform for epileptic seizure detection from eeg signals, Biomed. Signal Process Contr., 38, 108, 10.1016/j.bspc.2017.05.008
Khan, 2016, Classification of eeg signals using adaptive time-frequency distributions, Metrol. Meas. Syst., 23, 251, 10.1515/mms-2016-0021
Kemak Kiymik, 2005, Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application, Comput. Biol. Med., 35, 603, 10.1016/j.compbiomed.2004.05.001
Kocadagli, 2017, Classification of eeg signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations, Expert Syst. Appl., 88, 419, 10.1016/j.eswa.2017.07.020
Kumar, 2015, Classification of seizure and seizure-free eeg signals using local binary patterns, Biomed. Signal Process Contr., 15, 33, 10.1016/j.bspc.2014.08.014
Li, 2013, Feature extraction and recognition of ictal eeg using emd and svm, Comput. Biol. Med., 43, 807, 10.1016/j.compbiomed.2013.04.002
Lina, 2017, Automatic epileptic seizure detection in eeg signals using multi-domain feature extraction and nonlinear analysis, Entropy, 19, 1
Majumdar, 2012, Differential operator in seizure detection, Comput. Biol. Med., 42, 70, 10.1016/j.compbiomed.2011.10.010
Mallat, 1993, Matching pursuits with time-frequency dictionaries, IEEE Trans. Signal Process., 41, 3397, 10.1109/78.258082
Mohammadi, 2016, A highly adaptive directional time-frequency distribution, Signal, Image and Video Processing, 10, 1369, 10.1007/s11760-016-0901-x
Mohammadi, 2018, Locally optimized adaptive directional time–frequency distributions, Circ. Syst. Signal Process., 1
Navakatikyan, 2006, Seizure detection algorithm for neonates based on wave-sequence analysis, Clin. Neurophysiol., 117, 1190, 10.1016/j.clinph.2006.02.016
Pachori, 2011, Analysis of normal and epileptic seizure eeg signals using empirical mode decomposition, Comput. Meth. Progr. Biomed., 104, 373, 10.1016/j.cmpb.2011.03.009
Rankine, 2007, IF estimation for multicomponent signals using image processing techniques in the time-frequency domain, Signal Process., 87, 1234, 10.1016/j.sigpro.2006.10.013
Sengur, 2016, Time-frequency texture descriptors of eeg signals for efficient detection of epileptic seizure, Brain informatics, 3, 101, 10.1007/s40708-015-0029-8
Sharma, 2017, A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension, Pattern Recogn. Lett., 94, 172, 10.1016/j.patrec.2017.03.023
Stevenson, 2012, A nonparametric feature for neonatal eeg seizure detection based on a representation of pseudo-periodicity, Med. Eng. Phys., 34, 436, 10.1016/j.medengphy.2011.08.001
Varsavsky, 2011, 1
Yuan, 2017, Epileptic seizure detection based on imbalanced classification and wavelet packet transform, Seizure-European Journal of Epilepsy, 50, 99, 10.1016/j.seizure.2017.05.018
Zahra, 2017, Seizure detection from eeg signals using multivariate empirical mode decomposition, Comput. Biol. Med., 88, 132, 10.1016/j.compbiomed.2017.07.010