Advances in Computational Biology pp 593-599 | Cite as
A Study on Discrete Wavelet-Based Noise Removal from EEG Signals
- 24 Citations
- 2.5k Downloads
Abstract
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
Keywords
Discrete wavelet transform De-noising EEGReferences
- 1.Niedermeyer E, Silva FH (1993) Electroencephalography: Basic principles, clinical applications and related fields. Lippincott, Williams & Wilkins, Philadelphia.Google Scholar
- 2.Holmes GL, Lombroso CT (1993) Prognostic value of background patterns in the neonatal EEG. J Clin Neurophysiol 10:323–352.PubMedCrossRefGoogle Scholar
- 3.Croft RJ, Barry RJ (2000) Removal of ocular artifact from the EEG: A review. Clin Neurophysiol 30:5–19.CrossRefGoogle Scholar
- 4.Unser M, Aldroubi A (1996) A review of wavelet in biomedical applications. IEEE Trans Biomed Eng 84:626–638.Google Scholar
- 5.Zikov T, Bibian S et al (2002) A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram. Proceedings of the Second Joint EMBS/BMES Conference, pp. 98–105.Google Scholar
- 6.Ramanan SV, Kalpakam NV et al (2004) A novel wavelet based technique for detection and de-noising of ocular artifact in normal and epileptic electroencephalogram. Proceedings of International Conference on Communication, Circuits and Signals 2:1027–1031.Google Scholar
- 7.Carre P, Leman H et al (1998) Denoising of the uterine EHG by an undecimated wavelet transform. IEEE Trans Biomed Eng 45:1104–1114.PubMedCrossRefGoogle Scholar
- 8.Mallat S (1998) A wavelet tour of signal processing. Academic, New York.Google Scholar
- 9.Wachowiak MP, Rash GS et al (2000) Wavelet-based noise removal for biomechanical signals: A comparative study. IEEE Trans Biomed Eng 47:360–368.PubMedCrossRefGoogle Scholar
- 10.Andrzejak RG, Lehnertz K et al (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E 64:061907.CrossRefGoogle Scholar