Emboli detection using a wrapper-based feature selection algorithm with multiple classifiers
Tóm tắt
Từ khóa
Tài liệu tham khảo
Guépié, 2019, Sequential emboli detection from ultrasound outpatient data, IEEE Journal of Biomedical and Health Informatics, 23, 334, 10.1109/JBHI.2018.2808413
D’Andrea, 2016, Transcranial doppler ultrasonography: From methodology to major clinical applications, World Journal of Cardiology, 8, 383, 10.4330/wjc.v8.i7.383
Kaczynski, 2018, Reproducibility of transcranial doppler ultrasound in the middle cerebral artery, Cardiovascular Ultrasound, 16, 15, 10.1186/s12947-018-0133-z
King, 2009, Doppler embolic signals in cerebrovascular disease and prediction of stroke risk, Stroke, 40, 3711, 10.1161/STROKEAHA.109.563056
Ritz, 2014, Cause and mechanisms of intracranial atherosclerosis, Circulation, 130, 1407, 10.1161/CIRCULATIONAHA.114.011147
Annangi, 2017, Prevalence of pulmonary embolism among systemic lupus erythematosus discharges: A decade of analysis of the national hospital discharge survey, JCR: Journal of Clinical Rheumatology, 23, 200
Szekely, 1964, Systemic embolism and anticoagulant prophylaxis in rheumatic heart disease, British Medical Journal, 1, 1209, 10.1136/bmj.1.5392.1209
Ng, 2016, The prevalence and incidence of atrial fibrillation in patients with acute pulmonary embolism, PloS One, 11, 10.1371/journal.pone.0150448
Valton, 1998, Microembolic signals and risk of early recurrence in patients with stroke or transient ischemic attack, Stroke, 29, 2125, 10.1161/01.STR.29.10.2125
Best, 2016, Transcranial doppler ultrasound detection of microemboli as a predictor of cerebral events in patients with symptomatic and asymptomatic carotid disease: a systematic review and meta-analysis, European Journal of Vascular and Endovascular Surgery, 52, 565, 10.1016/j.ejvs.2016.05.019
Serbes, 2014, Denoising performance of modified dual-tree complex wavelet transform for processing quadrature embolic doppler signals, Medical & Biological Engineering & Computing, 52, 29, 10.1007/s11517-013-1114-x
Serbes, 2015, An emboli detection system based on dual tree complex wavelet transform and ensemble learning, Applied Soft Computing, 37, 87, 10.1016/j.asoc.2015.08.015
Serbes, 2014, An emboli detection system based on dual tree complex wavelet transform, 819
Guépié, 2017, Discrimination between emboli and artifacts for outpatient transcranial doppler ultrasound data, Medical & Biological Engineering & Computing, 55, 1787, 10.1007/s11517-017-1624-z
S. Purkayastha, F. Sorond, Transcranial doppler ultrasound: technique and application, in: Seminars in neurology, vol. 32, Thieme Medical Publishers, 2012, pp. 411–420.
McCartney, 2012
M. Geryes, S. Ménigot, W. Hassan, A. Mcheick, J. Charara, J.-M. Girault, Detection of doppler microembolic signals using high order statistics, Computational and Mathematical Methods in Medicine (2016).
Geryes, 2019, Enhanced weak doppler micro-embolic signal detection using energy fluctuations, Biomedical Signal Processing and Control, 47, 177, 10.1016/j.bspc.2018.08.020
Sombune, 2018, Automated cerebral emboli detection using adaptive threshold and adaptive neuro-fuzzy inference system, IEEE Access, 6, 55361, 10.1109/ACCESS.2018.2871136
Ferroudji, 2017, An automated microemboli detection and classification system using backscatter rf signals and differential evolution, Australasian Physical & Engineering Sciences in Medicine, 40, 85, 10.1007/s13246-016-0512-4
Aydin, 1999, The use of the wavelet transform to describe embolic signals, Ultrasound in Medicine & Biology, 25, 953, 10.1016/S0301-5629(99)00052-6
Mallat, 1989, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674, 10.1109/34.192463
Mallat, 1999
Girault, 2012, Cerebral microembolism synchronous detection with wavelet packets, Signal and Image Multiresolution Analysis, 245, 10.1002/9781118568767.ch4
Chen, 2008, Doppler embolic signal detection using the adaptive wavelet packet basis and neurofuzzy classification, Pattern Recognition Letters, 29, 1589, 10.1016/j.patrec.2008.03.015
Wang, 2010, A new wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine, Expert Systems with Applications, 37, 7040, 10.1016/j.eswa.2010.03.014
Wu, 2001, An efficient architecture for two-dimensional discrete wavelet transform, IEEE Transactions on Circuits and Systems for Video Technology, 11, 536, 10.1109/76.915359
Bayram, 2009, Frequency-domain design of overcomplete rational-dilation wavelet transforms, IEEE Transactions on Signal Processing, 57, 2957, 10.1109/TSP.2009.2020756
Serbes, 2009, A complex discrete wavelet transform for processing quadrature doppler ultrasound signals, 1
Serbes, 2016, Directional dual-tree complex wavelet packet transforms for processing quadrature signals, Medical & Biological Engineering & Computing, 54, 295, 10.1007/s11517-014-1224-0
Serbes, 2014, Directional dual-tree rational-dilation complex wavelet transform, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014, 1465
Serbes, 2015, A micro emboli vs non-emboli classification system based on the directional dual tree rational dilation wavelet transform, 1
Ringelstein, 1998, Consensus on microembolus detection by tcd, Stroke, 29, 725, 10.1161/01.STR.29.3.725
Chen, 2016, Accidental microembolic signals: prevalence and clinical relevance, Neurovascular Imaging, 2, 5, 10.1186/s40809-016-0017-2
Aydin, 2004, Embolic doppler ultrasound signal detection using discrete wavelet transform, IEEE Transactions on Information Technology in Biomedicine, 8, 182, 10.1109/TITB.2004.828882
Aydin, 1994, Quadrature-to-directional format conversion of doppler signals using digital methods, Physiological Measurement, 15, 181, 10.1088/0967-3334/15/2/007
Serbes, 2016, Analysis of embolic signals with directional dual tree rational dilation wavelet transform, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2016, 3821
J. Weston, C. Watkins, Multi-class support vector machines, Tech. rep., Citeseer (1998).
S.S. Khan, M.G. Madden, A survey of recent trends in one class classification, in: Irish Conference on Artificial Intelligence and Cognitive Science, Springer, 2009, pp. 188–197.
Wang, 2005, vol. 177
Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427
Hosmer, 2013, vol. 398
Rosenblatt, 1958, The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review, 65, 386, 10.1037/h0042519
Kursa, 2010, Feature selection with the boruta package, Journal of Statistical Software, 36, 1, 10.18637/jss.v036.i11
Sakar, 2019, A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform, Applied Soft Computing, 74, 255, 10.1016/j.asoc.2018.10.022