Akademik Veri Yönetim Sistemi
Biomedical Signal Processing and Control, cilt.77, 2022 (SCI-Expanded)
https://www.sciencedirect.com/science/article/pii/S1746809422002622
ABSTRACT:
In this study, cognitive and behavioral emotion regulation strategies (ERS)
are classified by using machine learning models driven by a new local EEG
complexity approach so called Frequency Specific Complexity (FSC) in
resting-states (eyes-opened (EO), eyes-closed (EC)). According to international
10–20 electrode placement system, FSC is defined as entropy estimations in
Alpha (8-12Hz) and Beta (12.5-30Hz) frequency band intervals of non-overlapped
short EEG segments to observe local EEG complexity variations at 62 points on
scalp surface. The healthy adults who use both rumination and cognitive distraction
frequently are included in the 1st groups, while the others who use these
strategies rarely are included in the 2nd group with respect to Cognitive
Emotion Regulation Questionnaire (CERQ) scores of them. EEG data and CERQ
scores are downloaded from publicly available data-base LEMON. In order to test
the reliability of the proposed method, five different supervised machine
learning methods in addition to two Extreme Learning Machine models are
examined with 5-fold cross-validation for discrimination of the contrasting
groups. The highest classification accuracy (CA) of 99.47% is provided by
Class-specific Cost Regulation Extreme Learning Machines in EC state. Regarding
cortical regions (anterio-frontal, central, temporal, parieto-occipital), the regional
FSC estimations did not provide the higher performance, however, corresponding
statistical distribution shows the decrease in EEG complexity at mostly
anterior cortex in the 1st group characterized by maladaptive rumination. In
conclusion, FSC can be proposed to investigate cognitive dysfunctions often
caused by the use of rumination.
Highlights
· In
the present study, a new quantitative single-channel EEG marker called as
Frequency Specific Complexity for classification of maladaptive rumination at
resting-state.
· The
reliability of the proposed method has been provided by using seven different
5-fold cross-validated classifiers with respect to both states
(eyes-opened vs eyes-closed) and cortical regions.
· The
new findings show that maladaptive rumination cause decreases neuronal
complexity at mostly anterior regions.
DISCUSSION AND CONCLUSION
In the present study, a new indicator so
called FSC has been proposed for classification of maladaptive rumination in
resting-state, since electrophysiological findings commonly reveal close relations
between mental disorders and neuro-functional network across the whole cortex
even in the resting state [100], [101], [102]. Usefulness of WE and AE in computing
frequency band specific EEG complexity is tested for estimation of accurate
FSC. Then, the reliability of FSC is tested by examining supervised machine learning models and extreme learning machines for detection of
maladaptive rumination from resting-state EEG recordings. In addition,
statistical differences between the groups are computed through one-way Anova
tests according to the corresponding best results.
AE provided considerable better
estimations in comparison to WE due to nonlinear nature of EEG segments. AE is
a nonlinear statistical quantity of signal irregularity such that its
performance depends on the largeness of time-series. Comparison of AE with WE
has been studied in EEG analysis with several goals such as detection of
driving fatigue [103], [104] and seizure [105].
Alpha and Beta sub-band specific
complexity estimations are averaged to provide FSC in resting-state, since EEG
analysis has frequently focused on Alpha and Beta sub-bands for estimation of
cognitive skills in resting-state in both past and recent studies [106], [107], [108], [109]. Multi-channel FSC estimations are
considered as separate instants (i.e. features) in two-class classification
steps. Dimension of the features is identical to the number recording channels
that are included in regional features such SET-2, SET-3, SET-4 and SET-5. So,
the largest feature set is SET-1 including FSC values estimated from
62-channels over a 1min duration trial divided into short segments
of 2s according to 20 individuals in each group. As the number of
recording channels increases, both dimension and number of the features also
increases in feature sets. Therefore, the best classification performance is
obtained through 5-fold cross validated CCR-ELM driven by the largest feature
set including FSC values estimated with AE at EC state. In addition to
higher CA, CCR-ELM is also faster than the other
machine learning models. It can be stated that the classification performance
is decreased as the number of features is decreased, however, FSC values
estimated with AE are able to detect neuro-functional complexity differences
originated from maladaptive rumination at both states (EC, EO) in each feature
set classified by any classifier except GNB. In reference study,
GNB was reported as less successful than both SVM and k-NN [84].
In conclusion, the results reveal that
maladaptive rumination is highly relevant to FSC estimations (averaged EEG
complexity with AE in Alpha and Beta sub-bands of short EEG segments) at mostly
EC state at mostly frontal, centro-parietal and parietal locations. In detail,
FSC values are decreased in maladaptive ruminations characterized by repetitive
thoughts associated with negative feelings. In literature, the left frontal
cortex is assumed to be responsible for management of arousal and regulation of
stress response, while the right frontal cortex was reported to manage the
fight or flight response [86]. However, the current results prove that the
whole cortex is more or less affected by cognitive abilities in management of
negative emotions. Since, the people with a high tendency to rumination have
low ability to manage negative emotions, it is the important issue to correlate
maladaptive rumination with resting-state EEG based quantitative indicators.
Since, frequently use of negative ERS
called rumination induces deficits in cognitive functioning, the most important
hallmark symptoms of major depression disorder, wherein individuals retrieve
and repetitively rehearse autobiographical and negatively valance content about
both past and current issues [110], [111]. Moreover, persons who use frequently
rumination in daily life, have trouble with sustained attention. Thus,
maladaptive rumination is one of the most problematic cognitive symptoms in
adults. The present study propose a new quantitative tool to understand
the neural mechanism underlying repetitive
negative thoughts triggered by depressive feelings.