Australas Phys Eng Sci Med (2016) 39:147–155 DOI 10.1007/s13246-015-0409-7
SCIENTIFIC PAPER
Temporal correlation between two channels EEG of bipolar lead in the head midline is associated with sleep-wake stages Yanjun Li1,2 • Xiaoying Tang1 • Zhi Xu1,2 • Weifeng Liu1 • Jing Li1
Received: 8 June 2015 / Accepted: 30 November 2015 / Published online: 2 March 2016 Ó Australasian College of Physical Scientists and Engineers in Medicine 2016
Abstract Whether the temporal correlation between inter-leads Electroencephalogram (EEG) that located on the boundary between left and right brain hemispheres is associated with sleep stages or not is still unknown. The purpose of this paper is to evaluate the role of correlation coefficients between EEG leads Fpz-Cz and Pz-Oz for automatic classification of sleep stages. A total number of 39 EEG recordings (about 20 h each) were selected from the expanded sleep database in European data format for temporal correlation analysis. Original waveform of EEG was decomposed into sub-bands d (1–4 Hz), h (4–8 Hz), a (8–13 Hz) and b (13–30 Hz). The correlation coefficient between original EEG leads Fpz-Cz and Pz-Oz within frequency band 0.5–30 Hz was defined as rEEG and was calculated every 30 s, while that between the two leads EEG in sub-bands d, h, a and b were defined as rd, rh, ra and rb, respectively. Classification of wakefulness and sleep was processed by fixed threshold that derived from the probability density function of correlation coefficients. There was no correlation between EEG leads Fpz-Cz and Pz-Oz during wakefulness (|r| \ 0.1 for rh, ra and rb, while 0.3 [ r [ 0.1 for rEEG and rd), while low correlation existed during sleep (r & -0.4 for rEEG, rd, rh, ra and rb). There were significant differences (analysis of variance, P \ 0.001) for rEEG, rd, rh, ra and rb during sleep when in comparison with that during wakefulness, respectively. The
& Xiaoying Tang
[email protected] 1
School of Life Science, Beijing Institute of Technology, Beijing 100081, China
2
State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China
accuracy for distinguishing states between wakefulness and sleep was 94.2, 93.4, 89.4, 85.2 and 91.4 % in terms of rEEG, rd, rh, ra and rb, respectively. However, no correlation index between EEG leads Fpz-Cz and Pz-Oz could distinguish all five types of wakefulness, rapid eye movement (REM) sleep, N1 sleep, N2 sleep and N3 sleep. In conclusion, the temporal correlation between EEG bipolar leads Fpz-Cz and Pz-Oz are highly associated with sleepwake stages. Moreover, high accuracy of sleep-wake classification could be achieved by the temporal correlation within frequency band 0.5–30 Hz between EEG leads FpzCz and Pz-Oz. Keywords EEG Sleep stage classification Sleep scoring Sleep-wake classification Correlation coefficient Temporal correlation Synchronization
Introduction The scalp Electroencephalogram (EEG) reflects the synchronous activity of neurons with great populations in the brain cerebral cortex with weak spatial resolution but great temporal resolution [1]. Synchronization of EEG is important for decoding information in the human brain [2]. Nowadays, EEG synchronization has often been used for exploring pathological brain activity and determining different stages of sleep. In a given pathological state or a cognitive task, the EEG synchrony might increase or decrease [3]. The common synchronization of pathological brain activity includes schizophrenia, Alzheimer’s disease, epilepsy, Parkinson’s disease, and autism [2]. The synchronization of neuronal assemblies has been widely studied using the correlation of intra-lead EEG and the correlation of inter-leads EEG. There are various ways of
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measuring the synchronization degree between or within EEG signals, including Pearson Product-Moment correlation in time domain, Itakura distance and coherence function in frequency domain, and time–frequency characterization. Cross correlation measures the linear correlation between two signals in the time domain, while coherence identifies the synchrony within EEG frequency components [4]. The fluctuations in EEG synchronization during sleep might play an important role for cognitive processing, and synchronization has always been related to the occurrence of high amplitude and slow wave in EEG, i.e., slow wave sleep
Fig. 1 Electrodes location of EEG Fpz-Cz and Pz-Oz
(a)
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(SWS) [5]. Slow wave sleep carries a meaning of synchronous discharge of the cortical neurons close to the EEG electrode [5]. Moreover, slow wave of EEG during sleep is accompanied by a large-scale increase in synchronization between different scalp areas [5]. The rapid eye movement (REM) sleep was associated with decreased correlation coefficients between right and left hemispheres, leading to the hypothesis that dreams are mediated by the right hemisphere [6]. According to the Manual for the Scoring of Sleep and Associated Events that made by the American Academy of Sleep Medicine (AASM) [7], human EEG patterns during sleep are divided into frequency bands d (1–4 Hz), h (4–8 Hz), a (8–13 Hz) and b (13–30 Hz). These sub-bands contain plenty of information that related to the brain activity. Some studies have focused on intra- or interhemispheric synchronization. The left and right hemispheres of the brain were affected by events of apnea, leading to a decrease in hemispheric symmetry in the d, a and b frequency bands [8]. Performance decrement was associated with an increase in h and a bands activity, accompanied by a decrease in b band activity from the power spectra of the EEG [9]. In addition, the extent of theta-gamma synchronization was related with the working-memory ability [4]. However, synchronization on the boundary of left and right brain hemispheres has been so far neglected. Whether the correlation of inter-leads EEG in the head midline is associated with sleep stage or not is still unknown. The role
(b)
Fig. 2 EEG and its sub-bands d, h, a and b. a EOG, EEG of Fpz-Cz and EEG of Pz-Oz during wakefulness; b EOG, EEG of Fpz-Cz and EEG of Pz-Oz during sleep
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of EEG correlation coefficients for automatic sleep scoring is unknown, either. These issues form the main focus of the work presented in this paper. The present study was undertaken to assess the role of inter-leads EEG correlations for sleep stage scoring, to study the dynamics of the synchronization between EEG bipolar leads Fpz-Cz and Pz-Oz during wakefulness and sleep in healthy normal controls.
Materials and methods
the boundary between left and right brain hemispheres. Moreover, stages of ‘wakefulness’, ‘S1’, ‘S2’, ‘S3’, ‘S4’ and ‘REM’ are represented in the public sleep dataset according to the Rechtschaffen and Kales (R&K) standard. About 894.0 h recording was analyzed in this paper, including 610.0 h during wakefulness and 284.0 h during sleep. According to the difference between the R&K standard and the AASM manual [7], stages S3 and S4 were combined as stage N3 in this paper. During sleep-wake discrimination, ‘S1’, ‘S2’, ‘S3’, ‘S4’ and ‘REM’ was grouped as sleep stages.
Database A total number of 39 files were obtained from the expanded sleep database in European data format (EDF) [10], which is freely available online at ‘‘http://www.physionet.org/phy siobank/database/’’. Two Polysomnogram (PSG) recordings with duration about 20 h were recorded during two subsequent day-night periods at the subjects’ homes. File ‘SC4ssnE0-PSG.edf’ contains the PSG of subject ss (00 B ss B 19) for the nth night (n = 1, 2), and subjects with 00 B ss B 9 are 10 females and the rest are 10 males. These 20 subjects were 25–34 years old (28.65 ± 2.94) at the time of the recordings. However, the second night PSG of subject 13 was lost due to a failing cassette or laserdisk. The EEG leads Fpz-Cz and Pz-Oz are sampled at 100 Hz each lead, while the location of the electrodes is shown in Fig. 1. The electrodes Fpz, Cz, Pz and Oz are all located on
Fig. 3 Correlation coefficients rEEG, rd, rh, ra and rb. The vertical distance between the dotted lines is 0.5 for each r. The top curve is the sleep stages according to the annotation of the database, while the curves from the second one to the bottom one are correlation coefficients rEEG, rd, rh, ra and rb, respectively
Data processing Firstly, raw EEG was filtered by Finite Impulse Response (FIR) band-pass filtering (0.5–30 Hz) to get the smoothed EEG sEEG. Sub-bands d (1–4 Hz), h (4–8 Hz), a (8–13 Hz) and b (13–30 Hz) were derived from individual FIR bandpass filter from original EEG within the frequency band 1–4 Hz, 4–8 Hz, 8–13 Hz and 13–30 Hz, respectively. And these sub-band components were defined as sd, sh, sa and sb, respectively. In this paper, all FIR filters were implemented by the function ‘fir1’ in MATLAB@. One example of the EEG sub-bands is shown in Fig. 2. Correlation coefficient reflects the linear relationship between two sequences, as a positive correlation coefficient demonstrates the consistent trend while negative one
rEEG
wakefulness and sleep
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sleep 0.5
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opposite trend [11]. The correlation coefficients were gotten by formula (1) when X represents one time series and Y another one. The correlation coefficient rEEG between sEEG of Fpz-Cz and sEEG of Pz-Oz was calculated in the time domain every 30 s epoch. The correlation coefficients rd, rh, ra and rb were also obtained each 30 s epoch between two EEG leads, respectively. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ¼ CovðX; YÞ= DðXÞ DðYÞ ð1Þ where Cov(X,Y) is the cross-covariance between X and Y, and D() denotes the variance. Figure 3 illustrates an example of the correlation coefficients and the sleep stages annotation from the EDF database, in which high correlation coefficients are associated with wakefulness while low correlation coefficients are accompanied by sleep stages. The discrimination ability of every single index rEEG, rd, rh, ra and rb was tested by analysis of variance (ANOVA) for distinguishing stages of wakefulness, REM sleep, N1 sleep, N2 sleep, and N3 sleep. If variations within groups are small relative to variations between groups, a significant difference in group means may be inferred by ANOVA [11]. In this paper, the function ‘anova1’ in MATLAB@ was used to analyze the difference level between different sleep stages, and a value of P \ 0.01 was considered statistically significant. Sleep-wake classification was processed based on the probability density function (PDF) of correlation coefficients rEEG, rd, rh, ra and rb between EEG leads Fpz-Cz and Pz-Oz, respectively. The maximum x-coordinate of PDF
Table 1 Comparison of correlation coefficients between wakefulness and sleep Indices/states
Wakefulness
Sleep
rEEG
0.12 ± 0.18
-0.41 ± 0.15**
rd
0.27 ± 0.22
-0.39 ± 0.19**
rh
-0.01 ± 0.20
-0.45 ± 0.14**
ra
-0.09 ± 0.17
-0.40 ± 0.14**
rb
-0.03 ± 0.12
-0.44 ± 0.18**
** P \ 0.001, as sleep compared with wakefulness by ANOVA
Table 2 Comparison of correlation coefficients among REM sleep, N1 sleep, N2 sleep and N3 sleep
from all 39 EDF files during wakefulness and that during sleep is defined as mS and mW, respectively. Then a fixed threshold Th is gotten by formula (2). Sleep-wake classification was as follows: An epoch is classified as sleep stage if r \ Th, else it is classified as wakefulness. Compared with the complicated machine learning methods of support vector machine (SVM), neural network, multilayer perceptron and the K-nearest neighbor classifiers [12], the classification decision with fixed threshold is rather simple and prompt. Th ¼ ðmS þ mWÞ =2
ð2Þ
Results Correlation coefficient Correlation coefficients from all 39 recordings were presented as mean ± standard deviation (SD) in Table 1 and Table 2. Remarkably, there was a significant difference (ANOVA, P \ 0.001) between wakefulness and sleep in all correlation coefficients in Table 1. There was no correlation between EEG Fpz-Cz and Pz-Oz (|r| \ 0.3) during wakefulness stage, while low correlation existed (r & -0.4) during sleep stage. For each subject, the mean and standard deviation of correlation coefficients between wakefulness and sleep is shown in Fig. 4. It was found that P \ 0.01 in all comparisons except rh in the 34th subject and ra in the 11th, 17th and 18th subjects that by ANOVA. Consequently, these correlation coefficients had the potential values to distinguish states between wakefulness and sleep. According to Table 2, a significant difference (ANOVA, P \ 0.01) in rb was found between REM sleep and N1 sleep. So was that between REM sleep and N2 sleep. Unfortunately, no other significant difference was found between any other stages. That was why only sleep-wake classification was made in this paper, rather than classification of all five patterns of wakefulness, REM sleep, N1 sleep, N2 sleep, and N3 sleep.
Indices/states
REM sleep
N1 sleep
N2 sleep
N3 sleep
rEEG
-0.37 ± 0.16
-0.32 ± 0.17
-0.44 ± 0.15
-0.42 ± 0.14
rd
-0.33 ± 0.20
-0.26 ± 0.21
-0.43 ± 0.18
-0.42 ± 0.16
rh
-0.47 ± 0.14
-0.41 ± 0.16
-0.45 ± 0.15
-0.42 ± 0.11
ra rb
-0.39 ± 0.15 -0.30 ± 0.13
-0.39 ± 0.17 -0.32 ± 0.15
-0.41 ± 0.14 -0.51 ± 0.17*
-0.38 ± 0.12 -0.46 ± 0.16*
* P \ 0.01, as REM sleep compared with other stages by ANOVA
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Fig. 4 The mean and standard deviation of correlation coefficients between wakefulness and sleep for each subject. a rEEG; b rd; c rh; d ra; e rb
wakefulness
0.05
sleep
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PDF
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mW -0.2 rEEG 0
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mW 0
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mW -0.2 r
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0.8
(e) Th mW
mS -0.6
-0.4
-0.2
r
0
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0.8
Fig. 5 Probability density function of correlation coefficients for wakefulness and sleep. a PDF of rEEG; b PDF of rd; c PDF of rh; d PDF of ra; e PDF of rb
N2
N3
-0.6
-0.4
-0.2 rEEG 0
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PDF
0 -0.8 0.05
Th mS
N1
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PDF
Th mS
REM
(a)
0 -0.8 0.05
PDF
PDF
-0.6
PDF
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mS
PDF
Th
0 -0.8 0.05
wake 0.05
(e)
0 -0.8
Fig. 6 Probability density function of correlation coefficients for wakefulness, REM sleep, N1 sleep, N2 sleep and N3 sleep. a PDF of rEEG; b PDF of rd; c PDF of rh; d PDF of ra; e PDF of rb
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Sleep stage scoring
method could achieve very good awakening and sleep detection.
In Figs. 5 and 6, the PDF of correlation coefficients are all in the shape form of Gaussian distribution except a few curves of rb. According to the PDF, sleep-wake was distinguishable in Fig. 5 by any index from rEEG, rd, rh, ra and rb, which is consistent with the significant difference in Table 1. On the contrary, four patterns of REM sleep, N1 sleep, N2 sleep, and N3 sleep are almost indistinguishable in Fig. 6, which is also coincident with Table 2. As shown in Fig. 5, fixed thresholds Th are gotten by formula (2), which are -0.1521, -0.0767, -0.2213, -0.2339, and -0.2237 for rEEG, rd, rh, ra and rb, respectively. Record SC4181E0 represents the data of subject 18 during the 1st night sleep. Figure 7 is the results of sleepwake classification for record SC4181E0 derived from Fig. 3 by comparing with the fixed thresholds above, obtaining accuracies 99.17, 98.91, 97.93, 85.92 and 84.25 % for rEEG, rd, rh, ra and rb, respectively. As shown in Table 3, the accuracy of sleep-wake classification for all recordings was 94.2, 93.4, 89.4, 85.2 and 91.4 % in terms of rEEG, rd, rh, ra and rb, respectively. Furthermore, comparison among different ways of distinguishing between wakefulness and sleep is listed in Table 4. The presented results indicate that our suggested
Fig. 7 Sleep stage scoring results for record SC4181E0. The top curve is the sleep stages according to the annotation of the EDF database, while the curves from the second row to the bottom one are classification results of sleep stages based on rEEG, rd, rh, ra and rb, respectively
Discussion Slow-wave sleep is closely related to EEG synchronization. Very slow oscillations (0.25–2.5 Hz band) in spatial EEG synchronization might play a critical role in the long-range temporal EEG correlations during sleep, which might be responsible for the maintenance and development of sleep structure during the night [5]. It might be a partial reason why rd-based sleep-wake classification achieved better accuracy than rh-, ra- and rb- based classification in this paper. The degree of EEG synchronization is confounded by the recording reference signal, and the reference signal with high relative amplitude or power will increase the correlation of intracranial EEG [3]. The coherence of scalp EEG originated both from neocortical sources and EEG conduction over the head [21]. After estimating of the coherence at scalp electrodes due to volume conduction of unrelated source activity, electrodes within 10–12 cm appeared to correlate [21]. As shown in Fig. 1, the nearest distance of Cz and Pz is 20 % of the distance between the
rEEG
wakefulness and sleep
r
r
r
wake sleep wake sleep wake sleep wake sleep wake sleep wake sleep 0
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10
Time (hour)
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Table 3 Results of true positive detection for sleep-wake scoring (N = 39) Component
Total number of 30 s-segments of wakefulness
Total number of 30 s-segments of sleep
Accuracy (%)
Database annotation
73,201
34,084
–
Classification by rEEG
68,854
32,169
94.16
Classification by rd
68,354
31,838
93.39
Classification by rh
64,020
31,859
89.37
Classification by ra
61,346
30,021
85.16
Classification by rb
68,653
29,876
91.84
Table 4 Comparison among different ways for distinguishing states between wakefulness and sleep Papers
Signals
Methods
Recordings
Classification accuracy
[13]
RR series derived from ECG
Support vector machine
18 recordings of MIT/BIH Polysomnographic Database
80 %
[14]
Two leads EOG
Adjustable threshold parameters on spectral power
158 males and 108 females
85 %
[15]
Wrist-worn Actiwatch
Radial basis function (RBF)-kernel SVM
85 nights PSG
92.0 %
[16]
ECG, respiratory effort and accelerometer signals
Artificial neural network
250 h recordings
93.0 %
[17]
Six leads EEG, two leads EOG, and one lead EMG
Support vector machine
40 recordings
94.6 %
[18]
Bed Actigraphy, wristworn Actiwatch
Intensity and duration of activity
10 recordings
95.2 % for bed Actigraphy, 92.9 % for wrist-worn Actiwatch
[19]
Ballistocardiogram
Decision tree
10 normal subjects and 10 patients with obstructive sleep apnea (OSA)
97.4 % for normal subjects and 96.5 % for OSA patients
[9]
O1-M2 or C3-M2 channel EEG
Ratio (a ? h)/b with fixed threshold
40 healthy pilots
98.3 % for O1-M2, and 91.3 % for C3-M2
[20]
Bipolar lead EEG Pz-Oz
EEG difference visibility graphs combined with SVM
8 recordings from the Sleep-EDF database
97.9 %
Our proposed detector
Bipolar leads EEG (FpzCz and Pz-Oz)
Fixed threshold for correlation coefficients
39 recordings from the Expanded Sleep-EDF database
94.2, 93.4, 89.4, 85.2 and 91.4 % in terms of reeg, rd, rh, raand rb, respectively
nasion and occipital protuberance, i.e., 7–10 cm. Therefore, the correlation values in this paper might be the complex results from both neural activity and common input to the electrodes by volume conduction. However, the volume conduction effects might be slight, as the correlation difference between wakefulness and sleep is very significant and the main contributing factor is cerebra activity. There was no correlation between EEG Fpz-Cz and PzOz (|r| \ 0.3) during wakefulness in this paper, while low correlation existed (r & -0.4) during sleep stage. The reason to explain this phenomenon might be as follows: the brain is consisted of many functional regions. During
wakeful state, the brain activity shows a high level of asynchronism not only in the high frequency and low amplitude wave within one lead EEG, but also in the noncorrelation between inter-leads EEG. On the contrary, during sleep stage the brain shows high synchronized activity not only in the low frequency and high amplitude wave within one lead EEG, but also in the temporal correlation between inter-leads EEG. It is not fully understood yet the meaning of the increase in correlation coefficients between EEG bipolar leads FpzCz and Pz-Oz in the midline of the head during sleep. But it may provide a useful tool in the classification of sleep and wakefulness. During wakefulness, correlation coefficients
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were either positive (0.3 [ r[0.1 for both rEEG and rd) or very close to zeros (r & 0 for rh, ra and rb), indicating the nonsynchronous interactions between EEG Fpz-Cz and PzOz. During sleep, instead, correlation all represented low negative interactions (r & -0.4), showing more symmetric brain activity over sleep. The correlation degree between two cortical regions indicates the similarity degree between the EEG sites of the neuronal networks. Low correlation reflects higher functional differentiation, and vice versa, higher correlation reflects a more homogeneous functioning mode. The present results indicate that there is a higher functional differentiated mode in the brain during wakefulness, while a more homogeneous functional state during sleep on the boundary of the left and right hemispheres. The role of correlation coefficients of EEG bipolar leads Fpz-Cz and Pz-Oz for automatic sleep-wake classification was accounted for in this paper. However, only two bipolar leads EEG were studied. The correlation among more EEG leads is very likely to be existed. Moreover, the physiological foundation of EEG correlation was still unknown. In future, whether unipolar EEG correlation coefficients and bipolar EEG correlation coefficients have the same role or not should be explored for sleep stage classification. Besides, EEG-based sleepwake classification systems suffer from uncomfortable setting with electrodes attached to the scalp and skin, making it not appropriate for long-term monitoring of sleep-wake behavior. Contrarily, methods of Ballistocardiogram, wrist-worn Actiwatch are nearly unloaded, and may ultimately be an effective tool for long-term, home monitoring of sleep-wake behavior.
Conclusion The present study was undertaken to assess the role of inter-leads EEG correlations for sleep stage scoring, to study the dynamics of temporal synchronization between EEG bipolar leads Fpz-Cz and Pz-Oz during wakefulness and sleep in healthy normal controls. Our results indicated that the correlation coefficient of EEG bipolar leads Fpz-Cz and Pz-Oz changes with wake-sleep cycles, which provides a basis for novel insights into the automatic scoring of sleep stages. In conclusion, correlation coefficients of EEG bipolar leads Fpz-Cz and Pz-Oz are good potential indices for detecting changes in the sleep-wake stages classification, especially the temporal correlation within frequency band 0.5–30 Hz. Furthermore, the increased correlation coefficients between EEG Fpz-Cz and Pz-Oz during sleep might indicate the sleep-related brain connectivity in the midline of cerebral region.
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Australas Phys Eng Sci Med (2016) 39:147–155 Acknowledgments This study was funded by State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center (SMFA15B06, SMFA15A01, SMFA13B03), and it was also funded by China National Natural Science Fund (61473190, 81471743, 61401417). We gratefully acknowledge the contributions of PhysioNet for providing the Expanded EDF Sleep Database freely at the URL ‘http://physionet. org/physiobank/database/’.
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