title: Denoising preterm EEG by signal decomposition and adaptive filtering: a comparative study.
abstract: Electroencephalography (EEG) from preterm infant monitoring systems is usually contaminated by several sources of noise that have to be removed in order to correctly interpret signals and perform automated analysis reliably. Band-pass and adaptive filters (AF) continue to be systematically applied, but their efficacy may be decreased facing preterm EEG patterns such as the tracé alternant and slow delta-waves. In this paper, we propose the combination of EEG decomposition with AF to improve the overall denoising process. Using artificially contaminated signals from real EEGs, we compared the quality of filtered signals applying different decomposition techniques: the discrete wavelet transform, the empirical mode decomposition (EMD) and a recent improved version, the complete ensemble EMD with adaptive noise. Simulations demonstrate that introducing EMD-based techniques prior to AF can reduce up to 30% the root mean squared errors in denoised EEGs.
authors: INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image, Rennes, F-35000, France; Sorbonne Universités, UPMC Univ Paris 06, UMRS-1158, Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, F-75005, France. Electronic address: xavier.navarro.s@upmc.com.
authors: X
authors: X
authors: Navarro
authors: INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image, Rennes, F-35000, France.
authors: F
authors: F
authors: Porée
authors: INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image, Rennes, F-35000, France; CHU Rennes, Pôle Médico-Chirurgical de Pédiatrie et de Génétique Clinique, Rennes, F-35000, France.
authors: A
authors: A
authors: Beuchée
authors: INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image, Rennes, F-35000, France; INSERM, CIC-1414, Rennes, F-35000, France.
authors: G
authors: G
authors: Carrault
mesh_terms: D004569:Electroencephalography; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis
keywords: Adaptive filter; Electroencephalography; Empirical mode decomposition; Mode mixing; Preterm infants