REM sleep. Artifacts, particularly certain types, may be more likely found in particular settings and stages of sleep. To overcome the limitation of regression ...
Proceedings of the 25" Annual International Conference of the IEEE EMBS Cancun, Mexico September 17-2 1,2003
Reduction of EEG Artifacts by ICA in Different Sleep Stages S. Romero1'2,M.A. Mailanas', S. Clos2, S. Gimenez2,M.J. Barbanoj2 'Centre de Recerca en Enginyeria Biomkdica, Dep. ESAII., UPC, Barcelona, Spain 2Centre d'Investigaci6 de Medicaments, Institut de Recerca HSCSP. Dep. of Pharmacology and Therapeutics, UAB, Barcelona, Spain
Abstruct--Contamination of sleep EEG signals by the eye, muscle and heart activity is a problem for EEG interpretation and analysis of sleep disorders and influence of drugs. The aim of this paper is to evaluate a method of artifact reduction applied in different sleep stages: awakeness, stage 2, delta and REM sleep. Artifacts, particularly certain types, may be more likely found in particular settings and stages of sleep. To overcome the limitation of regression methods in bidirectional contamination, a method based on Independent Component Analysis (ICA) using time structure is applied. Artifact identification is based on time, frequency and scalp topography aspects of the independent components. Influence of artifacts is evaluated by calculating some target spectral variables before and after their reduction, using significance probability maps. Results show that ICA is a useful technique for the evaluation of these variables with clinical interest in different sleep stages. Keywords-Artifacts, ICA, sleep EEG, spectral bands
I. INTRODUCTION Sleep is divided into two types of states known as nonrapid eye movement (NREM) and rapid eye movement sleep (REM). NREM sleep include four stages, named 1 to 4. In each NREM stage, brain waves become progressively larger and slower, and sleep becomes deeper. Stage 2 is characterized by sleep spindles and K complexes in the EEG. A K complex is a sharp, negative EEG wave followed by a high-voltage slow wave. NREM stages 3 and 4 are known as delta sleep [I]. A widely used method for evaluating sleep disorders and the influence of drugs on sleep EEG is based on the changes obtained in some spectral variables. There are five spectral bands of clinical interest in sleep studies: delta (1.33.5 Hz), theta (3.5-7.5 Hz), alpha (7.5-12 Hz), sigma (12-14 Hz) and beta (14-35 Hz). Not everyone agrees on the exact boundaries of these rhythms and many people subdivide these bands. A pathology typically increases slow activities (delta, theta) and decreases fast activity (alpha, beta) [2]. One of the main problems in the spectral sleep EEG analysis is the minimization of the different kinds of interference waveforms (artifacts) added to the EEG signal during the recording sessions. The most important physiological sources of the artifacts are the normal electrical activity of the heart, muscles and eyes. The most relevant case is the ocular artifact, when the globe rotates about its axis, it generates a large-amplitude altemate
current field, which can be detectable by electrodes near the eyes [3]. The procedure commonly used to minimize eye movement artifacts is based on regression analysis in time or frequency domain, subtracting from the EEG signal a portion of the contribution of the electrooculogram (EOG) due to the propagation of this signal in the EEG signals [4]. Therefore, subtracting a linear combination of the recorded EOG from the EEG may not only reduce ocular artifacts but may also remove interesting cerebral activity. One concern often raised regarding the use of regression analysis is the inability to account for bidirectional contamination [ 5 ] . In sleep, there are some stages where EEG is mainly contaminated by the EOG, like awakeness and REM sleep. On the other hand, in some sleep stages the EEG interferences mainly the EOG channels, like delta sleep and stage 2 with the K complexes. Another kind of methods is based on a linear decomposition of the EEG and EOG leads into source components, identifying the artifactual ones, and then reconstructing the EEG signals without them. One of these approaches is the Independent Component Analysis (ICA) [6], which was developed in the context of blind source separation problems in order to obtain components that are approximately independent. ICA gives a method for artifact removal where it is neither necessary an accurate model of the process that generates the artifacts nor the specified observation intervals that contain mainly the artifact. The purpose of this paper is to evaluate an ICA method for removing a wide variety of EEG artifacts in different situations of contamination. These situations are represented by different stages of sleep. Finally, the influence of ICA reduction of artifacts in the spectral bands of clinical interest is analyzed.
11. METHODOLOGY
A . EEG signals The EEG signals, sampled at 256 Hz, were recorded from 19 electrodes placed on each subject scalp according to the intemational 10-20 system, using averaged mastoid electrodes as a reference. Vertical EOG was recorded from mid-forehead (2.5cm above the pupil) to the average of one This study was partially supported by a grant from CICYT (TIC2001-2 167CO2-01).
0-7803-7789-3/03/$17.00 02003 IEEE
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electrode below the left and one electrode below the right eye (2.5cm below the,pupil). Horizontal EOG was recorded from the outer canthi. Signals were acquired from three male subjects aged 25, 26 and 27 years. Sleep stage scoring was done by three experts according to R&K criterion. Fifteen 5-second segments were selected in each of the following sleep stages: awakeness, stage 2 containing a K complex, delta sleep and REM sleep.
B. Independent Component Analysis The term Independent Component Analysis (ICA) was first used by Jutten and Herrault to describe the linear transformation of a random vector onto a basis which minimized the statistical information between its components. ICA is a statistical signal processing technique whose goal is to express a set of random variables as a linear combinations of statistically independent component variables [ 6 ] . A way of formulating the ICA problem is to consider the parametric estimation of the following generative model for the data: X = As (1) where x is an observed m-dimensional vector, s is an ndimensional random vector whose components are assumed mutually independent, and A is a constant mxn matrix to be estimated. In other words, there are some mixtures x, of some original sources signals s,. Nothing is known about the sources st or the mixing process A. The task is to recover a version of the original sources [ 5 ] . Several different estimation principles and algorithms for ICA were recently introduced [6]. Taking into account that the sources are time signals, the algorithm used in this paper is a method using a time structure called Algorithm for Multiple Unknown Signals Extraction (AMUSE) [7]. In fact, if the independent components are time signals, they may contain much more structure than simple random variables. For example, the covariances over different time lags of the independent components are well-defined statistics. AMUSE use such additional statistics to improve the estimation of the model. The model is then expressed by: x ( t ) = As(t) Particularly, AMUSE is an algorithm that exploits the second-order statistics of the independent components. It was developed to circumvent shortcomings that present some fourth-order blind identification algorithms when the sources are gaussian. In AMUSE, an orthogonalization transformation is first constructed. In this step, AMUSE estimates the number of sources from the number of significant singular values and estimates the noise variance from the insignificant singular values. This transformation reduces the complexity of the blind identification problem. Subsequently, source and mixing matrix estimation was evaluated by a transformation matrix obtained from the
eigendecomposition of a slightly modified version of the lagged covariance matrix [ 71:
This algorithm is simple and very fast to compute. C. Spectral Analysis
The most common method used for the evaluation of EEG signals is the power spectral density (PSD) function. PSD were calculated by means of periodogram using a Hanning window to prevent leakage of spectral power. The following target variables were quantified using the mean spectral curves: the absolute power of the total frequency band (1.3-35 Hz), and the absolute and relative power of the delta (1.3-3.5 Hz) and bets (14-35 Hz) frequency bands. To evaluate the effectiveness of ICA in artifact removal, topographic maps were: computed by adjusting each individual’s brain to a mean shape according to the percentage proportionals of the 10/20 system.
IIIL RESULTS In equation (I), rows of the observed matrix x are the raw EEG signals recorded at different electrodes. Rows of the output data matrix u=Wx are the estimated source signals (time courses of activation of the independent components), and the columns of the inverse matrix W-’ give the projection strengths of the respective components onto the scalp sensors [5]. The scalp topographies of the components allow to examine the evidence for their biological origin and the determination if a independent component (IC) is cognitively related or not with an artifact. After extracting the IC’,s that represent muscle, EOG and ECG artifacts, corrected EEG can be reconstructed from the remaining components by zeroing out the corresponding rows of the activation matrix U,. Artifact identification is based on time, frequency and scalp topography aspects of the independent components. An IC related to an eye movement artifact is removed if its waveform is similar tal a blink, lid movement or slow activity that do no correspond to a cortical activity. The scalp map indicates thai. this component spreads to frontal sites. Scalp maps are necessary because K complexes can be confused with EOG interference in time domain, but the maps show that this activity are also in brain areas out of frontal sites. In addition, artifacts originated from the activity of heart can appear as ECG interference and cardiac pulse wave. This contamination is identified mainly by the visualization of the time course of the IC’s because of the periodicity of heart rate. Otherwise, its localization in the scalp map is not always determined in a specific area. Finally, muscle artifacts are detected by the burst of fast activity (20-40 Hz) [ l ] from the temporal and lateral frontal (F7,F8) sites.
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Fig 1 . Schematic view of ICA minimization of artifacts. (a) A five second REM epoch of raw EEG and EOG data. (b) Independent components activations extracted by AMUSE method. (c) Some ICA scalp maps for artifactual activity (Hot colors represent high projection strengths; green colors indicate null projection strength). (d) Artifact-free EEG signals. ICA decomposition is performed on 5-second EEG epochs from each sleep stage selected, using the AMUSE algorithm in the ICALAB toolbox v1.5. [8] for Matlab. As an example, Figure 1 shows an epoch corresponding to a raw EEG time series in REM sleep, its IC activations, the scalp topographies of 8 selected components corresponding to artifacts, and the corrected EEG signals. These last signals are obtained by reconstructing with the IC's excepting eight selected eye, ECG and muscle noise components from the data. Rapid eye movements contaminate the EEG from anteriorly placed electrodes. The eye movement artifact is isolated to ICA components 1 to 4. Scalp topography shows that these components project mainly to frontal sites. Cardiac contamination in the EEG data is concentrated in ICA component 6 , located in this example in the left-occipital electrode. Left and right temporal and lateral frontal muscle activity in the data are concentrated in ICA components 19 to 2 1. Table 1 shows the average energy of each kind of artifact components with respect to the total energy of all the EEG channels for three subjects and four stages of sleep. The influence of the ICA minimization of artifacts is checked by calculating some target spectral variables before and after the artifact identification in each EEG lead. For statistical comparison, the paired sample t test is used and plotted in descriptive significance probability maps (SPM) [4]. Figure 2 shows SPM based on t values for the total absolute power, the absolute and relative delta and beta powers for one subject between the parameters before and
after using the artifact reduction method based on ICA. According to SPM in figure 2, artifact removal do not affect in the same way to the different stages of sleep. These results are similar in the other subjects.
IV.
DISCUSSION
Artifacts, particularly certain types, may be more likely found in particular settings and stages of sleep. For example, cardiac contamination depends mainly of the position of the body and electrodes. In the EEG epochs selected from subject 2 there is no ECG artifacts in any sleep stage. In the TABLEI RELATIVE ENERGY OF ARTIFACT COMPONENTS (%) SLEEP STAGE
I
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ARTIFACT
SUBJECT
EYE
SI s2 s3 SI
HEART
s2 s3 SI
MUSCLE
s2 s3
AWAKE
STG2
DELTA
REM
25.16 13.42 53.86 3.46 0.00 6.07 1.40 0.35 1.44
2.60 1.74 2.37 6.48 0.00 0.25 0.46 0.57 0.43
1.72 2.09 0.29 0.00
6.96 12.87 21.32 14.47
0.00
0.00
1.11 0.46 0.32 0.32
3.02 0.62 0.17 0.66
ABSOLUTE TOTAL POWER
ABSOLUTE DELTA POWER
RELATIVE DELTA POWER
ABSOLUTE BETA P0WE:R
RELATIVE BETA POWER
b
P
b 2.911
-2.911
Fig 2. Differences in absolute and relative power between raw and corrected EEG signals in subject 3. Differences plotted by means of descriptive significance probability maps (SPM) based on t values. Hot colors represent increases; cold colors represent decreases of the variables (t > 2.977 correspondsto p
