5. 2. Feature selection
Although each extracted feature has the ability to partially classify biosignals, the performance of a classification algorithm
can degrade when all extracted features are used to determine
the sleep stages. Therefore, in order to select a set of relevant
features among the extracted ones, we compute the discriminating power of each of them19 when they are used in combination. However, it is computationally impractical to test all
of the possible feature combinations. Therefore, we adopt a
procedure called Sequential Forward Selection (SFS) 26 to identify the most effective combination of features extracted from
our in-ear signal. With SFS, features are selected sequentially
until the addition of a new feature results in no performance
improvement in prediction. To further improve the efficiency
of our selection method, we have considered additional criteria for selecting features. In particular, we assigned a weight
to each feature based on its classification capability and relevance to other features. Subsequently, these weight factors
are adjusted based on the classification error. Furthermore,
a feature is added to the set of selected features if it not only
improves the misclassification error but also is less redundant given the features already selected. With this approach,
we can efficiently rank discriminant features based on the
intrinsic behavior of the EEG, EMG, and EOG signals.
5. 3. Sleep stage classification
Various classification methods are proposed in the literature
for similar applications and each has advantages and disadvantages. Some scholars11 have chosen the Artificial Neural
Network (ANN) classification approach for sleep scoring. In
spite of the ANN ability to classify untrained patterns, long
training time and complexity for selection of parameters
such as network topology. Moreover, since decision tree
is easier to implement and interpret as compared to other
algorithms, it is widely used for sleep stage classification.
Another classification method used for sleep stage identification is SVM. SVM is a machine learning method based
on statistical learning theory. Since SVM can be used for
large data sets with high accuracy rates, it has also been
widely used by various studies18 to classify sleep stages.
However, this approach suffers from long training time
and difficulty to understand the learned function. Based
on the existing comparative studies, 19 the decision tree
(and more generally random forest) classification methods
have achieved the highest performance since the tree structure can separate the sleep stages with large variation. As
an example, decision tree classifiers are flexible and work
well with categorical data. However, overfitting and high
dimensionality are the main challenges in decision trees.
Therefore, we use an ensemble learning method for classification of in-ear signal. Particularly, we deploy random
forest with twenty five decision trees as a suitable classifier
for our system. This classifier is able to efficiently handle
high dimensional attributes and it also reduces computational cost on large training data sets. The set of features
selected through SFS are used to construct a multitude of
decision trees at training stage to identify the corresponding sleep stage for every 30sec segment of the biosignals in
the classification stage.
measurement modalities including brain activities, eye movements, and muscle contractions. In hospital, an expert can
visually inspect EEG, EOG, and EMG signals collected from
subjects during sleep and label each segment (i.e., a 30sec
period) with the corresponding sleep stage based on known
visual cues associated with each stage. Below we elaborate on
each of aforementioned steps of our data analysis pipeline.
5. 1. Feature extraction
The features selected for extraction are from a variety of categories as follows:
Temporal features. This category includes typical
features used in the literature such as mean, variance,
median, skewness, kurtosis, and 75th percentile, which
can be derived from the time series. In sleep stage classification, both EOG and EMG signals are often analyzed in the time domain due to their large variation in
amplitude and a lack of distinctive frequency patterns.
Accordingly, based on our observations about these signals, we include more features that can distinguish N1
from REM, which are often misclassified. In particular, we
consider average amplitude that is significantly low for
EMG while relatively higher for EOG during the REM stage.
Also to capture the variation in EOG during different sleep
stages, we consider the variance and entropy for EOG in
order to magnify distinctions between Wakefulness, REM,
and N1 stages.
Spectral features. These features are often extracted to
analyze the characteristics of EEG signal because brain
waves are normally available in discrete frequency ranges
in different stages. By transforming the time series signal
into the frequency domain in different frequency bands
and computing its power spectrum density, various spectral features can be studied. Here based on our domain
knowledge about the EEG patterns in each sleep stage, we
identify and leverage spectral edge frequencies to distinguish those stages.
Non-linear features. Bioelectrical signals show various
complex behaviors with nonlinear properties. In details, since
the chaotic parameters of EEG are dependent on the sleep
stages, 11 they can be used for sleep stage classification. The
discriminant ability of such features is demonstrated through
the measures of complexity such as correlation dimension,
Lyapunov exponent, entropy, fractal dimension, etc. 23
For this study, relied on the literature of feature-based
EOG, EMG, and EEG classification, 11 we consider the features
listed in Table 1 from each of the aforementioned categories.
Table 1. List of features extracted from the biosignals.
Temporal features average amplitude, variance, 75th percentile,
Spectral features absolute spectral powers
relative spectral powers
relative spectral ratio
spectral edge frequency
Non-linear features fractal dimension, entropy