A two-stage framework for denoising electrooculography signals. (January 2017)

• Record Type: Journal Article
• Authors: Dasgupta, Anirban; Chakraborty, Suvodip; Routray, Aurobinda
• Is Part Of: Biomedical signal processing and control. Volume 31(2017)
• Page Start: 231
• Page End: 237
• Publication Date: 2017-01
• Subjects: EOG -- Kalman filter -- Particle filter -- EMD -- SWT -- SNR; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.08.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: A two-stage denoising framework for EOG signals has been proposed. In Stage I, the algorithm uses one of the four techniques – band-pass filter, EMD, SWT and median filter, which gives the highest SNR estimate. An important finding of this work is the use of recursive estimators, which helps in improving the SNR of the EOG data in Stage II. Recursive estimators take reasonably higher CPU time and hence are called upon only in case the Stage I methods do not provide a significant SNR. The developed algorithm can be extended to other biomedical signals with proper modelling. Abstract: Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same frequency band. This paper proposes a two-stage framework for denoising EOG signals. The first stage approach is based on preserving the nature of eye movements while the second stage is based on the nature of noise (Gaussian or not). In the first stage, denoising is carried out using one out of four filtering methods, each filter being optimal for a particular EOG pattern. The four methods used in the first stage are linear bandpass filtering, stationary wavelet transform (SWT), empirical mode decomposition (EMD) and median filtering. The Stage I framework selects the output that provides the highest estimated signal to noise ratio (SNR). In case, the Stage I filtering does not provide a significant SNR, the system uses Stage II filtering. In the second stage, we use two recursive state estimators, i.e. a Kalman filter and a particle filter for further denoising. The two-stage method is found to provide a better SNR as compared to a single stage method.