Noise Removal from EEG Signals in Polisomnographic Records Applying Adaptive Filters in Cascade

Polisomnography (PSG) is the standard technique used to study the sleep dynamic and to identify sleep disorders. In order to obtain an integrated knowledge of different corporal functions during sleep, a PSG study must perform the acquisition of several biological signals during one or more nights in a sleep laboratory. The signals usually acquired in a PSG study include the electroencephalogram (EEG), the electrocardiogram (ECG), the electromiogram (EMG), the electro oculogram (EOG), the abdominal and thoracic breathings, the blood pressure, the oxygen saturation, the oro-nasal airflow and others biomedical records (Collop et. al., 2007). Particularly, the EEG is usually analyzed by physicians in order to detect neural rhythms during sleep. However, it is generally contaminated with different noise sources and mixed with other biological signals. Their common artifacts sources are the power line interference (50 or 60 Hz), the ECG and EOG signals. Figure 1 shows an example of real EEG ECG and EOG signals recorded simultaneously in a PSG study. It can be seen that EEG signal is contaminated by the QRS cardiac complexes which appear as spikes at the same time in ECG record. Likewise, the low frequencies present in the contaminated EEG correspond to the opening, closing or movements of the eyes recorded in EOG signal. These noise sources increase the difficulty in analyzing the EEG and obtaining clinical information. To correct, or remove the artifacts from the EEG signal, many techniques have been developed in both, time and frequency domains (Delorme et. al., 2007; Sadasivan & Narayana, 1995). More recently, component-based techniques, such as principal component analysis (PCA) and independent component analysis (ICA); (Akhtar et. al., 2010; Astolfi et. al., 2010; Jung et. al., 2000), have also been proposed to remove the ocular artifacts from the EEG. The use of Blind Source Separation (BSS) (De Clercq et. al., 2005) and Parallel Factor Analysis (PFA) methods to remove artifacts from the EEG have been used in this area too (Cichocki & Amari, 2002; Makeig et. al., 2004). Wavelet Transform (WT) (Senthil Kumar et. al., 2009), WT combined with ICA (Ghandeharion et. al., 2009) and Autoregressive Moving Average Exogenous (ARMAX) (Hass et. al., 2003; Park et. al., 1998), have been applied too, to remove artifacts from EEG. In this chapter, it is described a cascade of three adaptive filters based on a Least Mean Squares (LMS) algorithm to remove the common noise components present in the EEG signal recorded in polysomnographic studies.