Classification of EEG Signals Using Adaptive Time-Frequency Distributions

• Autorzy: Khan N. A. , Ali S.

Identyfikatory

DOI

10.1515/mms-2016-0021

• Języki publikacji: EN

Abstrakty

EN

Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

Słowa kluczowe

EN

Adaptive Directional Time-Frequency Distribution; EEG signals; Time-Frequency features; pattern recognition

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2016
• Tom: Vol. 23, nr 2
• Strony: 251--260
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wzory

Twórcy

autor

Khan N. A.

• Federal Urdu University, Department of Electrical Engineering, Khayaban-e-Suhrwardy Road, G-7, Islamabad, Pakistan

autor

Ali S.

• University of Engineering and Technology Peshawar, Department of Electrical Engineering, Peshawar, Pakistan

Bibliografia

• [1] Joshi, V., Pachori, R.B., Vijesh, A. (2014). Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomedical Signal Processing and Control, 9, 1‒5.
• [2] Boashash, B., Khan, N., Ben-Jabeur, T. (2015). Time-frequency features for pattern recognition using high resolution TFDs: A tutorial review. Digital Signal Processing, 40, 1‒30.
• [3] Sharma, R., Pachori, R.B. (2015). Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications, 42(3), 1106‒1117.
• [4] Boashash, B., Azemi, G., Khan, N. (2015). Principles of time-frequency feature extraction for change detection in non-stationary signals: Application to newborn EEG abnormalitiy detection. Pattern Recognition, 48(3), 616‒627.
• [5] Sharma, R., Pachori, R.B., Acharya, U.R. (2015). Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals. Entropy, 17(2), 669‒691.
• [6] Fu, K., Qu, J., Chai, Y., Zou, T. (2015). Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomedical Signal Processing and Control, 18, 179‒185.
• [7] Boashash, B., Azemi, G. (2014). A review of time-frequency matched filter design with application to seizure detection in multichannel newborn EEG. Digital Signal Processing, 28, 28‒38.
• [8] Wang, K.C. (2015). Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition. Sensors, 15(1), 1458‒1478.
• [9] Sameh, S., Lachiri, Z. (2012). Multiclass Support Vector Machines for Environmental Sounds Classification Using log Gabor Filters. World Academy of Science, Engineering and Technology, 6(8), 1185‒1189.
• [10] Abdulla, W., Wong, L. (2011). Neonatal EEG signal characteristics using time frequency analysis. Physica A: Statistical Mechanics and its Applications, 390(6), 1096‒1110.
• [11] Boashash, B. (2015). Time-Frequency Signal Analysis and Processing: A Comprehensive Reference. 2nd ed., Elsevier.
• [12] Bastiaans, M., Alieva, T., Alieva, L. (2002). On Rotated Time-Frequency Kernels. IEEE Signal Processing Letters, 9(11), 378‒381.
• [13] Khan, N., Boashash, B. (2015). Multi-component instantaeous frequency estimation using locally adaptive directional time frequency distributions. International Journal of Adaptive Control and Signal Processing, DOI: 10.1002/acs.2583.
• [14] Liu, Y., Mejias, L., Li, Z. (2012) Fast Power Line Detection and Localization Using Steerable Filter for Active UAV Guidance. XXII Congress of the International Society for Photogrammetry, Remote Sensing, Melbourne.
• [15] Jacob, M., Unser, M. (2004). Design of steerable filters for feature detection using canny-lie criteria. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 1007‒1019.
• [16] Boashash, B. (1992). Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc. of the IEEE, 80(4), 520‒538.
• [17] Auger, F., Flandrin, P., Lin, Y.T., McLaughlin, S., Meignen, S., Oberlin, T., Wu, H.T. (2013). Timefrequency reassignment and synchrosqueezing: An overview. IEEE Signal Processing Magazine, 30(6), 32‒41.
• [18] Peng, H., Long, F., Ding, C. (2005). Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226‒1238.

Uwagi

EN

This research has been partially supported by Qatar Foundation Grant NPRP 4-1303-2-517 and NPRP 6-885-2-364.

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.