Multiresolution Analysis of EEG Signals

Ali, Z., Elamvazuthi, I., Alsulaiman, M., & Muhammad, G. (2016). Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals. Journal of Medical Systems, 40(1), 1–10.10.1007/s10916-015-0392-2Search in Google Scholar

Banerjee, S., Gupta, R., & Mitra, M. (2012). Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement, 45(3), 474–487.10.1016/j.measurement.2011.10.025Search in Google Scholar

Gandhi, T., Panigrahi, B. K., & Anand, S. (2011). A comparative study of wavelet families for EEG signal classification. Neurocomputing, 74(17), 3051–3057.10.1016/j.neucom.2011.04.029Search in Google Scholar

Gawędzki, W., Socha, M., & Sławik, P. (2015). Dekompozycja sygnałów EEG w dziedzinie czasu przy zastosowaniu transformacji Hilberta-Huanga HHT. Przegląd Elektrotechniczny, 91, 33–36.Search in Google Scholar

Gentile, A., & Messina, A. (2003). On the continuous wavelet transforms applied to discrete vibrational data for detecting open cracks in damaged beams. International Journal of Solids and Structures, 40(2), 295–315.10.1016/S0020-7683(02)00548-6Search in Google Scholar

Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., et al. (2000). Physiobank, physiotoolkit, and physionet. Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.10.1161/01.CIR.101.23.e215Search in Google Scholar

Guo, L., Rivero, D., & Pazos, A. (2010). Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. Journal of Neuroscience Methods, 193(1), 156–163.10.1016/j.jneumeth.2010.08.030Search in Google Scholar

Hu, Q., He, Z., Zhang, Z., & Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21(2), 688–705.10.1016/j.ymssp.2006.01.007Search in Google Scholar

Kitlas, A., Oczeretko, E., Laudański, P., & Laudański, T. (2010, August). Signal processing methods in the analysis of the uterine contractility. Paper presented at the 6th World Congress of Biomechanics (WCB 2010), Singapore.10.1007/978-3-642-14515-5_173Search in Google Scholar

Klonowski, W. (1997). Zastosowanie teorii układów nieliniowych do analizy sygnału EEG. X Krajowa Konferencja Naukowa: Biocybernetyka i Inżynieria Biomedyczna. Materiały naukowe, 2, 590–594.Search in Google Scholar

Kronland-Martinet, R., Morlet, J., & Grossmann, A. (1987). Analysis of sound patterns through wavelet transforms. International Journal of Pattern Recognition and Artificial Intelligence, 1(02), 273–302.10.1142/S0218001487000205Search in Google Scholar

Kumar, R., & Kumar, A. (2015). ECG signal compression algorithm based on joint-multiresolution analysis (J-MRA). Paper presented at the 2nd International Conference on Electronics and Communication Systems (ICECS).10.1109/ECS.2015.7124982Search in Google Scholar

Lee, S. H., & An, J. (2011). Quantitative EEG evaluation of multimedia intervention program for ADHD children. Paper presented at the Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies.10.1145/2093698.2093714Search in Google Scholar

Lou, X., & Loparo, K. A. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18(5), 1077–1095.10.1016/S0888-3270(03)00077-3Search in Google Scholar

Mallat, S. G. (1987). A Theory for Multiresolution Signal Decomposition: The Wavelet Representation (University of Pennsylvania, Department of Computer and Information Science Technical Report No. MS-CIS-87-22).Search in Google Scholar

Mallat, S. G. (1989). A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.10.1109/34.192463Search in Google Scholar

Mallat, S. G. (1999). A wavelet tour of signal processing. Academic press.Search in Google Scholar

Mirzaei, A., Ayatollahi, A., Gifani, P., & Salehi, L. (2010). EEG analysis based on wavelet-spectral entropy for epileptic seizures detection. Paper presented at the 3rd International Conference on Biomedical Engineering and Informatics.10.1109/BMEI.2010.5639894Search in Google Scholar

Muthuswamy, J., & Thakor, N. V. (1998). Spectral analysis methods for neurological signals. Journal of Neuroscience Methods, 83(1), 1–14.10.1016/S0165-0270(98)00065-XSearch in Google Scholar

Pal, S., & Mitra, M. (2010). Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement, 43(2), 255–261.10.1016/j.measurement.2009.10.004Search in Google Scholar

Pinsky, M. A. (2002). Introduction to Fourier analysis and wavelets. Graduate Studies in Mathematics, Volume 102, American Mathematical Society.Search in Google Scholar

Ravanfar, S. A., Razak, H. A., Ismail, Z., & Monajemi, H. (2015). An improved method of parameter identification and damage detection in beam structures under flexural vibration using wavelet multi-resolution analysis. Sensors, 15(9), 22750–22775.10.3390/s150922750Search in Google Scholar

Rosenblatt, M., Figliola, A., Paccosi, G., Serrano, E., & Rosso, O. A. (2014). A Quantitative Analysis of an EEG Epileptic Record Based on Multiresolution Wavelet Coefficients. Entropy, 16(11), 5976–6005.10.3390/e16115976Search in Google Scholar

Shoeb, A. H. (2009). Application of machine learning to epileptic seizure onset detection and treatment. Massachusetts Institute of Technology.Search in Google Scholar

Stark, H.-G. (2005). Wavelets and signal processing: an application-based introduction. Springer Science & Business Media.Search in Google Scholar

Strambi, S.-k., Rossi, B., De Michele, G., & Sello, S. (2004). Effect of medication in Parkinson’s disease: a wavelet analysis of EMG signals. Medical Engineering & Physics, 26(4), 279–290.10.1016/j.medengphy.2004.01.006Search in Google Scholar

Subasi, A. (2012). Classification of EMG signals using combined features and soft computing techniques. Applied Soft Computing, 12(8), 2188–2198.10.1016/j.asoc.2012.03.035Search in Google Scholar

Subha, D. P., Joseph, P. K., Acharya, R., & Lim, C. M. (2010). EEG signal analysis: a survey. Journal of Medical Systems, 34(2), 195–212.10.1007/s10916-008-9231-zSearch in Google Scholar