EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP
COMPOSITION TYPES
Amro Khasawneh
1
, Sergio A. Alvarez
2
, Carolina Ruiz
1
, Shivin Misra
1
and Majaz Moonis
3
1
Dept. Computer Science, Worcester Polytech. Inst., Worcester, MA 01609, U.S.A.
2
Dept. Computer Science, Boston College, Chestnut Hill, MA 02467, U.S.A.
3
Dept. Neurology, U. Massachusetts Medical School, Worcester, MA 01655, U.S.A.
Keywords:
Clustering, EEG, ECG, HRV, Sleep.
Abstract:
Unsupervised clustering of staged human polysomnographic recordings reveals a hierarchy of sleep compo-
sition types described primarily by sleep efficiency and slow wave sleep content. Associations are found
between these sleep clusters and health-related variables including BMI, smoking habits, and heart disease,
showing that sleep types correspond to objective and medically relevant groupings. The present work describes
the sleep type hierarchy, and studies the EEG and ECG correlates of sleep composition type. It is found that
measures of EEG variation such as δ, θ, and α spectral content, as well as average heart rate, and measures
of heart rate variability, including the standard deviation of the sequence of RR intervals, and Hj¨orth activity
and mobility of the ECG signal, differ significantly among sleep composition type clusters. EEG analysis is
shown to allow approximate reconstruction of sleep type without the need for ECG data, while ECG alone is
found to be insufficient for accurate sleep type classification.
1 INTRODUCTION
The idea that human sleep may be segmented into
identifiable stages based on electrical potentials mea-
sured on the surface of the scalp dates back at
least as far as the work of Loomis et al in the
1930’s (Loomis et al., 1937). Contemporary all-
night human sleep studies employ not only elec-
troencephalography (EEG), which records electri-
cal brain potentials, but also electrocardiography
(ECG), which records heart potentials, electroocu-
lography (EOG), which records eye movements, and
electromyography (EMG), which records chin mus-
cle movements (Kryger et al., 2005). Following
standard rules of sleep scoring, a sleep technician
scores these polysomnographic recordings into sleep
stages one epoch (typically 30 sec) at a time, re-
sulting in a sequence of sleep stage labels known
as a hypnogram. Fig. 1 shows a hypnogram, from
one of the polysomnographic recordings used for the
present paper, that is labeled according to the clas-
sical Rechstchaffen and Kales (R&K) staging stan-
dard (Rechtschaffen and Kales, 1968). As illustrated
in Fig. 1, sleep typically follows an overall tempo-
ral pattern of sleep stages, with several cycles involv-
ing alternation between REM (Rapid Eye Movement)
and NREM (non-REM) sleep during the night, with a
greater fraction of slow-wave sleep (stages NREM 3,
4) during the first half of the night, and a greater frac-
tion of stage NREM 2 sleep during the second half of
the night. Occasional stage fragmentations, including
periods of wakefulness after the beginning of sleep,
are also observed.
1.1 Scope of the Paper
The present paper describes work by the authors
that shows that staged human sleep studies may be
grouped naturally into a small number of distinct
sleep types, each of which is characterized by a differ-
ent sleep stage composition, with sleep efficiency and
time in slow wave sleep differentiating among sleep
types. Associations between sleep type and health-
related factors such as age, Body-Mass Index (BMI),
and smoking are also found, which supports the ob-
jectivity and medical relevance of sleep types. The
previous work (Khasawneh et al., 2010) only consid-
ers all-night summaries of staged sleep studies, while
full polysomnographic recordings comprise more de-
tailed electroencephalogram (EEG) and electrocar-
diogram (ECG) time series data. The present paper
also addresses the underlying EEG and ECG data, fo-
97
Khasawneh A., A. Alvarez S., Ruiz C., Misra S. and Moonis M..
EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES.
DOI: 10.5220/0003173900970106
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 97-106
ISBN: 978-989-8425-34-8
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
22:00 22:30 23:00 23:30 00:00 00:30 01:00 01:30 02:00 02:30 03:00 03:30 04:00 04:30 05:00 05:30 06:00
S4
S3
S2
S1
REM
W
Sleep Stages
Time
Hypnogram (sid=0161) [920 epochs]
Figure 1: Hypnogram from the present study, staged by Rechstchaffen and Kales standard.
cusing on variations in summary statistics of the EEG
and ECG signals among sleep composition types. It is
found that heart rate, Hj¨orth activity and mobility, and
normalized low frequency content of ECG all differ
significantly among sleep types. These results pro-
vide further evidence that sleep types constitute ob-
jective and medically relevant concepts. Furthermore,
it is found that analysis of EEG signals alone, in the
absence of ECG data, is sufficient for approximate re-
construction of sleep type based on unstaged sleep
data. On the other hand, ECG data alone does not
provide accurate sleep type classification.
1.2 Related Work
Previous works consider variations in sleep composi-
tion associated with factors such as medication (Smith
et al., 2006), smoking (Zhang et al., 2006), the prac-
tice of yoga (Sulekha et al., 2006), body composi-
tion (Rao et al., 2009), handedness (Propper et al.,
2007), and autism spectrum conditions (Limoges
et al., 2005). Work that relates sleep stage structure to
subjective assessments of sleep quality include (Bon-
net and Johnson, 1978) and (Keklund and Akerstedt,
1997). Such works use groupings in sleep structure
that are explicitly guided by existing sleep measures,
such as the Pittsburgh sleep quality index (Buysse
et al., 1989), or measures extracted from the Karolin-
ska Sleep Diary (Keklund and Akerstedt, 1997). The
work described in the present paper is the only one of
which we are aware that addresses a general descrip-
tion of intrinsic sleep types based on sleep architec-
ture itself. Specifically, the present work uncovers a
natural hierarchy of sleep types based on differences
in measures of overall sleep stage composition such
as sleep efficiency and time in slow wave sleep. As-
sociations between sleep type and health-related indi-
cators, including age and BMI, support the medical
objectivity of these sleep types.
Plan of the Paper
Study data and methodology are described in sec-
tion 2. Section 3 summarizes the results described in
the prior work (Khasawneh et al., 2010), followed by
the new results of the present paper involving EEG
and ECG. Section 4 concludes with a summary of
findings and ideas for future work.
2 METHODOLOGY
2.1 Data
We conduct our study on a dataset extracted from 244
recorded polysomnographic overnight sleep studies
performed in the Sleep Disorder Center at Day Kim-
ball Hospital in Putnam, CT, corresponding to 122
male and 122 female subjects. Summary statistics for
patients associated with the dataset appear in Table 1.
2.1.1 Staging
Most of the sleep studies used for the present study
were conducted prior to 2007, and hence the available
staging information follows the Rechstchaffen and
Kales (R&K) recommendations (Rechtschaffen and
Kales, 1968), which were the standard at that time.
In 2007, the American Academy of Sleep Medicine
(AASM) revised their staging recommendations (Iber
et al., 2007). The rationale for the new staging recom-
mendations is discussed in (Silber et al., 2007). We
briefly discuss the relationship between the two stan-
dards below.
The most immediately noticeable change in the
AASM system as compared with the R&K system is
the use of only three non–REM stages N1, N2, N3
instead of four, with the new N3 stage essentially re-
placing R&K NREM stages 3 and 4 corresponding
to slow wave sleep (SWS). The AASM system also
contains a revision to the rule for scoring stage N2,
namely the elimination of the “3 minute rule” that al-
lows continuation of NREM stage 2 labeling for up
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98
Table 1: Summary statistics of the sleep dataset used in this paper.
± σ ± ± ± ± ± ±
± σ ± ± ± ± ± ±
± σ ± ± ± ± ± ±
to three minutes in the absence of the K–complex
and sleep spindle EEG features that characterize stage
2. The AASM standard also includes clarified spec-
ifications for electrode placement. The variation in
stage content between the AASM and R&K systems
is studied in (Moser et al., 2009). Because of the new
guidelines for scoring stage 2, AASM N2 content in a
given sleep recording, as a fraction of total sleep time,
is found to be 4.9% less than R&K NREM2 content,
with the increase balanced by greater AASM N1 and
N3 content: N1 content is 2.8% greater than NREM
1, and N3 content is 2.4% greater than NREM 3, 4
combined (Moser et al., 2009). No statistically sig-
nificant differences are found in (Moser et al., 2009)
between R&K and AASM stagings in total sleep time,
sleep efficiency, or REM duration.
Given the relatively small differences between the
AASM and R&K systems in terms of relative time
spent in each stage (with combined time in R&K
stages NREM 3, 4 corresponding to time in AASM
stage N3), the results of this work should be similarly
relevant to sleep studies staged by the AASM system.
2.1.2 Descriptive Variables
We summarize sleep composition in terms of seven
summary measurements, as listed in Table 2. Total
sleep time in minutes, fraction of time in bed spent
sleeping, fraction of time in bed awake, and fraction
of sleep period time in each of the sleep stages NREM
1,2, in SWS (NREM3 + 4), and REM, are used. See
Fig. 2 for an illustration. Only the variables described
in Table 2 are used for clustering. A dataset is con-
structed in which each polysomnographic sleep study
is summarized as a feature vector consisting of the
values of the variables in Table 2 for that study. Clus-
tering is performed over this dataset as described in
section 2.2. Additional descriptive variables are used
to study associations of other factors with sleep stage
composition. These variables describe health history
information such as age, Body Mass Index (BMI),
and habitual smoking.
EEG-related variables are obtained by applying a
short-time Fourier transform to extract spectral con-
tent from the C3-A2 EEG time signals on a 30-second
epoch by epoch basis, at a sampling rate of 200 Hz.
Table 2: Summary descriptors of sleep composition.
+
EEG spectra are then binned into δ (0.5 4 Hz),
θ (4 7 Hz), α (8 12 Hz) and β (12 30 Hz)
ranges. Summary descriptors of the EEG spectral
variables are generated by taking the all-night mean
and standard deviation of the collection of epoch-
specific spectra for each spectral band, as well as by
computing other measures such as spectral entropy
and Hj¨orth activity, mobility, and complexity.
For the ECG-related variables, RR intervals, the
time durations between consecutive R peaks in the
QRS complexes of the ECG time signal, are ex-
tracted and likewise described in terms of all-night
summary variables including the mean RRm, stan-
dard deviation SDRR, entropy RRentr, autocorrela-
tion RRxcorr, and mean absolute linear predictability
error LPCerror of the sequence of RR interval dura-
tions, Hj¨orth activity, mobility, and complexity mea-
sures, the standard deviation SDSD of the sequence of
differences between successive RR intervals, the root
mean squared difference RMSSD of successive RR
intervals, the fraction pNNx of consecutive RR inter-
vals that differ by more than x milliseconds (Mietus
et al., 2002) for x = 10, 20, 30,40,50, and the lengths
SD1, SD2 of the principal axes of the Poincar´e plot,
which provides graphical information on parasym-
pathetic nervous system activity and sympathovagal
balance (Kamen et al., 1996). The Lomb-Scargle
method (Moody, 1993) is used to compute spectral
content of the RR interval sequence, and normal-
ized RR spectral components within low frequency
(0.04 0.15 Hz) and high frequency (0.15 0.4 Hz)
bands are extracted. See (M Malik et al, 1996) for
further information on the above and other commonly
used measures of Heart Rate Variability (HRV).
EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES
99
Figure 2: Summary-level descriptors of sleep composition. NREM sleep lightly shaded.
2.2 Clustering
Expectation-maximization (EM) (Dempster et al.,
1977; Neal and Hinton, 1998) is a powerful itera-
tive search technique for finding members of param-
eterized families of probabilistic models that locally
maximize the likelihood of a given set of data. We
use the EM clustering implementation in the Weka
machine learning toolkit (Hall et al., 2009), version
3.7.1, which uses Gaussian mixture components as a
basis for the models, and initializes the mixture pa-
rameters by k-means clustering. Unsupervised EM
clustering is applied to the set of sleep composition
feature vectors consisting of the values of the sum-
mary variables of Table 2 for the available staged PSG
studies, yielding k clusters. The values k = 3,4,5 are
considered in the present paper. Consistency of clus-
tering results is evaluated by using two metrics, the
cluster purity and the normalized mutual information
(NMI) (Manning et al., 2008; Strehl, 2002). Each of
these measures the agreement between two cluster-
ings on a scale of 0 to 1, with higher values indicating
better agreement.
2.3 Statistical Significance
Statistical significance is assessed by using a χ
2
test
for independence in the case of nominal variables; for
continuous variables, a t-test or Wilcoxon signed rank
test are used for pairwise comparisons, and ANOVA
or a Kruskal-Wallis test for multifactor comparisons.
Control of the increased risk of false positives that
is associated with simultaneous multiple tests of sig-
nificance is accomplished by using the Benjamini-
Hochberg method (Benjamini and Hochberg, 1995).
Given n individual findings with corresponding p-
values p
1
< p
2
< · ·· < p
n
, and given a desired over-
all level of significance (p-value) p, the Benjamini-
Hochberg procedure declares as significant the first k
findings, where k is the largest index i, 1 i n, for
which p
i
i/n < p. The Benjamini-Hochberg method
provides a rigorous bound on the false discovery rate
(FDR), the fraction of predicted positives that are ac-
tually negatives. Indeed, the procedure as described
here guarantees an overall FDR below the desired
level p (Benjamini and Hochberg, 1995). The FDR is
distinct from the traditional type I error rate, or fam-
ilywise error rate (FWR), which is the probability of
one or more false positives, regardless of the number
of positive predictions. The FDR is generally consid-
ered to be a better choice of significance criterion than
the FWR for exploratory data analysis tasks in which
there are a large number of findings to evaluate.
3 RESULTS
We discuss the results of EM clustering of the sleep
composition instance data as described in section 2,
beginning with a summary of the prior work (Kha-
sawneh et al., 2010), and show that the clusters found
are stable, that they can be described by sleep effi-
ciency and fraction of sleep time in slow wave sleep,
and that the family of clusterings for k = 3,4,5 has
a hierarchical structure. Health-related association
results suggest that the clusters represent medically
meaningful groups of distinct sleep behaviors. We
then proceed to describe new results involving dif-
ferences in EEG and ECG variables among the sleep
type clusters. We find significant differences among
clusters in EEG spectral band content, mean heart
rate, Hj¨orth activity and mobility of both the EEG
and ECG signals, and ECG normalized low frequency
content, among others.
3.1 Salient Properties of Sleep Type
Clusters
Kruskal-Wallis multiway comparison analysis of the
clusters found by the EM algorithm for k = 3,4,5
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100
clusters indicate significant differences among clus-
ters in the means of all sleep composition variables
used for clustering (Benjamini-Hochberg FDR p <
0.01, cf. section 2.3). Because of these differences
in sleep composition, the clusters are referred to as
sleep types. Wilcoxon pairwise test results confirm
that the mean values of total sleep time, sleep effi-
ciency, and fraction of sleep period time in NREM
stages 2 and SWS, and in stages REM and wake, dif-
fer significantly (FDR p < 0.01) between all pairs of
the three clusters in the case k = 3. However, the only
variables found to be significantly different among all
pairs of clusters for k = 4,5 are sleep efficiency and
fraction of time in SWS.
3.1.1 Visualization of Sleep Types in Sleep
Composition Space
Fig. 3 displays sleep types for three prespecified num-
bers of EM clusters: k = 3 (left), k = 4 (center), and
k = 5 (right), in terms of sleep efficiency and frac-
tion of sleep period time in SWS. Classification mod-
els were constructed to predict the cluster label based
on sleep efficiency and SWS. Pruned decision trees
achieved a classification accuracy of at least 0.91 for
k = 3,4,5, confirming that these variables provide
good separation among clusters in all cases. Cluster
decision boundaries for k = 3 are vertical lines of con-
stant sleep efficiency, while the cases k = 4, 5 require
the use of time in SWS also.
3.1.2 Hierarchical Structure of Sleep Types
Fig. 3 suggests that the family of clusterings for
k = 3,4,5 has an approximately hierarchical struc-
ture: clusters 1 and 2 are relatively stable across the
family, while the cluster labeled 3 in the leftmost im-
age (k = 3) splits into the two clusters labeled 3 and
4 in the middle image (k = 4); in turn, the cluster la-
beled 4 in the middle image (k = 4) splits into the
two clusters labeled 4 and 5 in the rightmost image
(k = 5). Further evidence of the existence of this hi-
erarchical structure is provided by a visualization of
the decision boundaries between cluster regions that
are found using Linear Discriminant Analysis (LDA).
See Fig. 4. Classification accuracies are 90%, 86%,
and 86% for k = 3,4,5, respectively. Despite some
variation in the sizes and boundaries of the clusters
for k = 3,4,5, support for the hierarchical structure of
sleep types, including the stability of clusters 1 and 2
as conceptual entities across different values of k, de-
rives from the summary statistics of the clusters (Kha-
sawneh et al., 2010). Sleep efficiency and stage com-
position of clusters 1 and 2 are seen to remain nearly
constant across the values k = 3,4,5. Cluster 2 has the
lowest sleep efficiency across values of k, followed by
cluster 1. The remaining clusters, which we loosely
associate with subgroups of cluster 3 for k = 3, con-
sistently have higher sleep efficiency than clusters 2
and 1. The transition from k = 3 to k = 4 produces an
approximate subdivision of cluster 3 into a new clus-
ter 3 that is SWS–heavy, and a cluster 4 that is stage
NREM2–heavy. Stage composition of these two clus-
ters is similar in other regards. Likewise, the tran-
sition from k = 4 to k = 5 generates a new cluster,
5, characterized by higher sleep efficiency and SWS
content than cluster 4, but similar NREM2 content.
3.1.3 Relationships between Sleep Type and
Health History Factors
For k = 3, Kruskal-Wallis multiway analysis reveals
significant differences among clusters in mean patient
age, collar size, BMI, smoking habit, and heart dis-
ease (Benjamini-Hochberg FDR p < 0.05). These
results show that sleep types are meaningfully con-
nected with overall health.
Pairwise differences in numerical variables are as-
sessed using a Wilcoxon test. The cluster with high-
est sleep efficiency (cluster 3 on the left in Fig. 3) has
significantly lower mean patient age (42.2 ± 12.7 vs
52.0± 14.3 and 57.0± 15.2), collar size (15.5± 2.1
vs 16.3± 2.1 and 16.5± 1.6), and habitual smoking
than clusters 1 and 2, respectively. Body Mass Index
(BMI) differs significantly between the two clusters at
opposite ends of the sleep efficiency scale (2 and 3).
Heart disease is significantly more frequent in cluster
2 than in the two clusters with higher sleep efficiency.
Pairwise comparison tests for k = 4 refine these
results. The mean ages in cluster 3 (47.0±14.7)and 4
(42.0±12.0), the clusters into which cluster 3 for k =
3 approximately splits, are significantly lower than in
cluster 2 (57.5 ± 15.2), the cluster with lowest sleep
efficiency. Age is also significantly lower in cluster 4
than in the cluster with second lowest sleep efficiency,
cluster 1 (age 52.1± 14.7). Collar size is significantly
lower in cluster 4 (15.6± 2.1) than in cluster 2 (16.5±
1.6). Smoking is significantly less frequent in cluster
4 than in cluster 2. Heart disease is significantly more
frequent in cluster 2 than in any other cluster.
3.2 EEG Characteristics of Sleep Type
Clusters
We consider the variation of EEG signal summary de-
scriptors among sleep type clusters.
EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES
101
0 0.2 0.4 0.6 0.8 1
0
10
20
30
40
50
60
70
sleep_efficiency_perc
SWS
1
2
3
0 0.2 0.4 0.6 0.8 1
0
10
20
30
40
50
60
70
sleep_efficiency_perc
SWS
1
2
3
4
0 0.2 0.4 0.6 0.8 1
0
10
20
30
40
50
60
70
sleep_efficiency_perc
SWS
1
2
3
4
5
Figure 3: Sleep types in terms of sleep efficiency and fraction of SWS, k = 3,4,5.
0.2 0.4 0.6 0.8
0
10
20
30
40
50
60
K=3 (90.16%)
sleep_efficiency_perc
SWS
0.2 0.4 0.6 0.8
0
10
20
30
40
50
60
K=4 (85.66%)
sleep_efficiency_perc
SWS
0.2 0.4 0.6 0.8
0
10
20
30
40
50
60
K=5 (85.66%)
sleep_efficiency_perc
SWS
Figure 4: LDA decision boundaries in stage composition space. Classification accuracies along top.
3.2.1 EEG Summary Statistics
We begin with the case k = 3. ANOVA / Kruskal-
Wallis multiway comparison finds significant differ-
ences among clusters in the variables listed in Ta-
ble 3. Given the differences in stage composition
among clusters as described in section 3.1, the cor-
responding differences in spectral content seen in ta-
ble 3 are not entirely unexpected. For example, SWS
content increases from cluster 2 to cluster 3 to cluster
1, which is the same cluster ordering obtained accord-
ing to δ band (slow wave) spectral content. Likewise,
α band spectral content is highest in cluster 2, which
corresponds to the fact that cluster 2 has the lowest
sleep efficiency, and hence the highest occurrence of
wakefulness after sleep onset.
3.2.2 Sleep Type Classification based on EEG
Alone
The MultiDimensional Scaling (MDS) visualization
in Fig. 5 shows considerable separation among clus-
ters in the space described by the EEG variables. This
suggests that EEG may provide enough information
to characterize the sleep type clusters. However, sep-
aration is not as marked as for the stage composition
attributes used by EM to produce the clusters origi-
nally (see section 3.1).
−10 −8 −6 −4 −2 0 2 4 6 8
−2
−1
0
1
2
3
4
MDS−dim1
MDS−dim2
1
2
3
Figure 5: MDS visualization of sleep type clusters in EEG
space, k = 3.
The result of Linear Decision Analysis (LDA) ap-
plied to the δ band power and spectral entropy at-
tributes for k = 3, 4, 5 clusters exhibits the character-
istic hierarchical structure discussed in section 3.1,
as shown in Fig. 6. Classification accuracies for
k = 3, 4, 5 are 62%, 50%, and 42%, respectively.
Slightly higher LDA classification accuracies (75%,
59%, and 54%, respectively) are obtained for the at-
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102
Table 3: EEG variables that differ significantly among sleep types, k = 3.
Cluster 1 Cluster 2 Cluster 3 Kruskal-Wallis p-value
No. instances 87 36 121 to 3 digits:
mu deltaRelPower 0.550±0.079 0.438±0.112 0.634±0.069 0.000*
mu
thetaRelPower 0.100±0.022 0.088±0.026 0.106±0.028 0.000*
mu alphaRelPower 0.110±0.045 0.141±0.073 0.084±0.026 0.000*
mu
betaRelPower 0.115±0.041 0.135±0.042 0.092±0.036 0.000*
mu spectralEntropy 0.568±0.049 0.611±0.081 0.516±0.048 0.000*
sd
deltaRelPower 0.211±0.032 0.226±0.037 0.184±0.027 0.000*
sd alphaRelPower 0.081±0.037 0.091±0.042 0.064±0.025 0.000*
sd
totalPower 2.076E5±12.530E5 2.489E5±9.980E5 0.603E5±2.429E5 0.000*
sd medianFrequency 6.777±0.422 7.215±0.414 6.286±0.445 0.000*
sd
spectralEntropy 0.136±0.025 0.146±0.026 0.125±0.021 0.000*
md deltaRelPower 0.575±0.094 0.414±0.141 0.654±0.082 0.000*
md
alphaRelPower 0.089±0.037 0.125±0.077 0.071±0.023 0.000*
md betaRelPower 0.105±0.044 0.134±0.045 0.080±0.040 0.000*
md
spectralEntropy 0.567±0.053 0.629±0.091 0.520±0.056 0.000*
mu hjorthMobility 0.230±0.035 0.265±0.049 0.197±0.032 0.000*
mu
hjorthComplexity 2.524±0.384 2.407±0.762 2.714±0.353 0.000*
sd hjorthActivity 5.315E5±34.771E5 5.816E5±25.234E5 1.434E5±6.339E5 0.000*
sd
hjorthMobility 0.084±0.019 0.090±0.023 0.073±0.014 0.000*
md hjorthMobility 0.218±0.039 0.269±0.055 0.189±0.036 0.000*
md hjorthComplexity 2.360±0.390 2.134±0.776 2.544±0.352 0.000*
md
thetaRelPower 0.097±0.023 0.084±0.028 0.102±0.030 0.004*
md hjorthActivity 0.197E5±0.173E5 0.219E5±0.346E5 0.258E5±0.429E5 0.005*
sd
betaRelPower 0.071±0.021 0.073±0.021 0.064±0.017 0.010*
mu totalPower 0.388E5±2.102E5 0.573E5±2.333E5 0.150E5±0.267E5 0.042*
tribute pair consisting of mean δ relative power and
median Hj¨orth complexity (not shown). These results
confirm that the EEG attributes allow relatively accu-
rate sleep type labeling if full polysomnography is not
available.
3.3 ECG and Heart Rate Variability
(HRV)
We proceed to discuss the variation of ECG variables
among sleep type clusters.
3.3.1 ECG Summary Statistics
In the case k = 3, ANOVA / Kruskal-Wallis multiway
comparison finds significant differences among clus-
ters in the variables listed in Table 4. The mean du-
ration of RR intervals, that is, the time lapse between
successive R peaks in the ECG signal (correspond-
ing to overall heart rate), occupies the top position on
this list. However, many of the remaining variables
with the greatest significance, that is, with lowest p-
values in Table 4, represent measures of the difference
between consecutive RR intervals. Examples include
SD2, SDRR, and S. In contrast, the values of variables
such as SD1 that measure longer term variation in RR
interval duration, do not differ significantly among
sleep types. Consistent with this, the variables pNNx
that measure the fraction of consecutive RR intervals
that differ by more than x milliseconds are more sig-
nificant the smaller the value of x, and the variable
pNN50 with the largest value of x does not even meet
the significance threshold p < 0.05.
The mean duration of RR intervals is found by a t
test to be significantly higher in cluster 3 than in both
cluster 1 and cluster 2. Recall that the latter two clus-
ters are the ones with lower sleep efficiency. Note
also that mean RR interval duration increases with
sleep efficiency across the three clusters. Hj¨orth ac-
tivity likewise is significantly higher in cluster 3 than
in both cluster 1 and cluster 2. Overall heart rate vari-
ability as measured by the standard deviation SDRR
of the sequence of RR intervals, is lowest in cluster
2, intermediate in cluster 1, and highest in cluster 3,
that is, it increases with sleep efficiency (although the
difference in SDRR between clusters 1 and 2 is not
significant at the level p < 0.05). Also, RRxcorr, the
autocorrelation of the RR interval sequence, is signif-
icantly lower in cluster 3 than in cluster 2, and linear
prediction error LPCerror is correspondingly higher.
Significance statements are at an overall Benjamini-
Hochberg FDR level p < 0.05. Decreased heart rate
variabilityis known to be associated with an increased
risk of coronary heart disease and mortality from mul-
tiple causes (Dekker et al., 2000). Thus, our HRV
findings are consistent with the distribution of heart
disease among the clusters described in section 3.1.3.
These results support the view that, for k = 3, the
sleep type clustering is linearly ordered by overall
health and sleep quality.
EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES
103
0.3 0.4 0.5 0.6 0.7 0.8
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
K=3 (65.98%)
mu_deltaRelPower
mu_spectralEntropy
0.3 0.4 0.5 0.6 0.7 0.8
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
K=4 (50.41%)
mu_deltaRelPower
mu_spectralEntropy
0.3 0.4 0.5 0.6 0.7 0.8
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
K=5 (42.21%)
mu_deltaRelPower
mu_spectralEntropy
Figure 6: LDA decision boundaries in EEG space. Classification accuracies along top of figure.
Table 4: ECG variables that differ significantly among sleep types, k = 3.
Cluster 1 Cluster 2 Cluster 3 Kruskal-Wallis p-value
No. instances 87 36 121 to 3 digits:
heartrate
mean 72.046 ± 9.749 74.722 ± 14.561 66.438 ± 8.507 0.000*
RRm 0.852 ± 0.123 0.840 ± 0.179 0.924 ± 0.125 0.000*
pNN10 70.565 ± 18.404 68.674 ± 17.588 77.529 ± 12.783 0.003*
SD2 0.101 ± 0.041 0.087 ± 0.039 0.111 ± 0.040 0.004*
SDRR 0.075 ± 0.031 0.065 ± 0.028 0.083 ± 0.030 0.004*
hjorthActivity 0.007 ± 0.006 0.005 ± 0.004 0.008 ± 0.006 0.004*
SDNNindex 0.053 ± 0.027 0.046 ± 0.022 0.058 ± 0.025 0.008*
S 0.009 ± 0.008 0.008 ± 0.007 0.012 ± 0.010 0.008*
SDANNindex 0.048 ± 0.022 0.043 ± 0.023 0.054 ± 0.021 0.009*
pNN20 47.104 ± 22.316 45.909 ± 22.383 55.495 ± 19.315 0.010*
nLF 33.031 ± 7.146 30.938 ± 7.788 34.781 ± 7.402 0.017*
pNN30 32.744 ± 21.494 32.761 ± 20.596 40.317 ± 20.122 0.018*
hjorthMobility 0.482 ± 0.190 0.590 ± 0.241 0.508 ± 0.161 0.022*
RRentr 5.080 ± 0.590 4.828 ± 0.688 5.169 ± 0.379 0.030*
LPCerror 0.025 ± 0.017 0.024 ± 0.014 0.028 ± 0.016 0.032*
FreqRatio 1.944 ± 1.308 1.514 ± 1.167 1.920 ± 1.141 0.034*
pNN40 23.671 ± 19.357 24.008 ± 17.906 29.595 ± 18.917 0.034*
RRxcorr 0.996 ± 0.005 0.997 ± 0.003 0.997 ± 0.003 0.046*
It is interesting that FreqRatio, the ratio between
low and high frequency power, is significantly lower
in cluster 2, which has the lowest sleep efficiency
among the three clusters, than in the other two clus-
ters. This is surprising at first sight, since cluster 2
has the greatest proportion of wakefulness after sleep
onset, and hence one would expect increased sympa-
thetic nervous system activity in this cluster than in
the others, and hence a higher ratio of low to high fre-
quency power. However, as found in (Vanoli et al.,
1995), this expected behavior is reversed in patients
that have suffered a myocardial infarction. Since
cluster 2 contains the greatest incidence of heart dis-
ease among the clusters as discussed in section 3.1.3,
this reversal is likely behind the lowered FreqRatio in
cluster 2 here.
For k = 4, mean RR interval duration, standard
deviation of the RR interval sequence, ratio of low to
high frequency power, and ECG Hj¨orth activity are
again significantly higher in the cluster with the high-
est sleep efficiency, cluster 4, than in the two clusters
with the lowest sleep efficiency, 1 and 2.
The findings for k = 3, 4 persist for k = 5, as
mean RR interval duration, standard deviation of the
sequence of RR intervals, ratio of low to high fre-
quency power, and ECG Hj¨orth activity are signifi-
cantly higher in cluster 5, which has the highest sleep
efficiency, than in the three clusters with the lowest
sleep efficiency, clusters 2, 1, 3. Autocorrelation of
the RR sequence is significantly lower in cluster 5
than in clusters 2,1,3.
3.3.2 Sleep Type Classification based on ECG
An attempt to discriminate among the sleep type clus-
ters in terms of ECG-related variables yields mixed
results, with considerable overlaps between clusters
(visualization not shown due to lack of space). For
example, if cluster labels are assigned by LDA classi-
fication, using mean RR interval duration and Hj¨orth
HEALTHINF 2011 - International Conference on Health Informatics
104
mobility as predictive variables, the resulting classifi-
cation accuracy is only 47% for k = 3 clusters, and
decreases to 32% for k = 5. Equally significantly,
while the hierarchical structure of the clustering fam-
ily is reflected in the relationship between the cases
k = 3,4, the structure breaks down for k = 5. Indeed,
as expected based on the discussion in section 3.1.2
and section 3.2.2, the LDA-predicted cluster that is
most closely associated with intermediate sleep effi-
ciency in the case k = 3 persists relatively unchanged
for k = 4, while the cluster with the highest sleep ef-
ficiency splits into two clusters from k = 3 to k = 4.
However, in the transition from k = 4 to k = 5, the
LDA-predicted cluster with intermediate sleep effi-
ciency changes substantially. Comparable results are
obtained using other pairs of ECG variables for pre-
diction. In view of these results, it is apparent that the
existing ECG data alone is insufficient to fully char-
acterize the family of sleep composition types.
Since sleep type classification relies on staged
sleep recordings, a natural route toward ECG-based
sleep type classification is to first construct a sleep
stager that uses ECG signals alone, without the need
for EEG or EMG. Classification of ECG time sig-
nals into the two classes sleep and wake has been ad-
dressed without additional data (Lewicke et al., 2005)
and with the aid of respiratory signals (Karlen et al.,
2009). However, we are not aware of techniques that
successfully perform full sleep staging based only on
ECG. The classification results based on ECG vari-
ables described in the preceding paragraph are there-
fore consistent with the current state of the art. It is
an open problem whether alternative descriptions of
ECG time series will allow accurate sleep type pre-
dictions from ECG data alone.
4 CONCLUSIONS AND FUTURE
WORK
Unsupervised clustering of all-night human
polysomnographic studies reveals a hierarchy of
sleep composition types determined primarily by
sleep efficiency, and subordinately by time in SWS.
The present paper has described these sleep type
clusters and their associations with health-related
variables, and has compared the behavior of EEG and
ECG signals among clusters. Statistically significant
differences have been found among clusters in
BMI, age, and heart disease incidence, supporting
the medical objectivity of sleep types. Significant
differences have also been found in δ, θ, α, and β
EEG spectral band content, and it has been shown
that relatively accurate prediction of sleep type is
possible based on EEG alone. Analysis of the ECG
signals has revealed significant differences among
sleep types in RR interval duration, Hj¨orth activity
and mobility, and in overall heart rate variability
as measured for example by the standard deviation
of the sequence of RR intervals. These findings
are consistent with the health-related associations
described above, the incidence of heart disease in
particular. Despite the ECG findings, we have found
that only limited information about sleep type may
be extracted from ECG recordings alone based on
the variables considered in the present paper. Future
work should further explore the possibility of deter-
mining sleep type based on alternative descriptions of
the ECG signals. Work in progress by the authors of
the present paper involves modeling the dynamics of
sleep stage transitions during sleep, and in particular
studying the differences in dynamics among sleep
type clusters.
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