Wireless Personal Communications
https://doi.org/10.1007/s11277-023-10445-w
A Machine Learning Framework for Major Depressive
Disorder (MDD) Detection Using Non‑invasive EEG Signals
Nayab Bashir1 · Sanam Narejo2 · Bushra Naz2 · Fatima Ismail3 ·
Muhammad Rizwan Anjum4 · Ayesha Butt2 · Sadia Anwar5 · Ramjee Prasad5
Accepted: 7 April 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
According to World Health Organization (WHO) report, every 40 seconds a person
attempts suicide globally. Depression, one of the world’s most prevailing diseases has
become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years
various machine learning and advanced neurocomputing techniques are being utilized in
Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine
Learning and one Deep Learning approach. The majority of previous EEG based MDD
decoding research has concentrated on a limited features. It was necessary to conduct indepth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which
is then further compared against the state of the art end to end techniques. The K-nearest
neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%.
While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study
can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.
Keywords Neurocomputing · Major depressive disorder · Feature based framework ·
Machine learning and Deep learning
* Muhammad Rizwan Anjum
engr.muhammadrizwan@gmail.com
Sanam Narejo
Sanam.narejo@faculty.muet.edu.pk
1
Department of Biomedical Engineering, Mehran University of Engineering and Technology,
Jamshoro, Pakistan
2
Department of Computer Systems Engineering, Mehran University of Engineering
and Technology, Jamshoro, Pakistan
3
Department of BBT, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
4
Department of Electronic Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100,
Pakistan
5
Department of Business Development and Technology, Aarhus University, Aarhus, Denmark
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N. Bashir et al.
1 Introduction
Behavioral health diseases, particularly depression, are a critical health matter worldwide.
The results of unattended behavioral health disorders are multifarious and have a serious
and sometimes negative impact on a person’s being, combined, associative and societal
levels. The distress of people suffering from depression is often unidentified even while
going through the treatment. Modern technology holds an unrealized ability to recognize
individuals at high risk for behavioral health conditions and in result to develop precaution
and arbitration techniques. Around one in every eight children agonizes from a psychiatric
disease that could be heinous enough to result in any particular functional defacement [1].
Major depressive disorder often termed as depression is one of the most prevalent mental
health pathology, and it is prognosticated to be the highest contributor to the Global Burden of Disease (GBD) by the year 2030 [2].
The exact reason for major depressive disorder (MDD) is ambiguous, genetic, environmental and psychological factors can trigger this impairment [3]. Even the stress and anxiety linked with early traumatic experiences and poverty have also been associated with the
spread of mental health problems [4]. Various symptoms and indications of ailing mental
health can be decreased with different services; however, if mental health challenges left
unattended and untreated, can have critical and problematic results.
Electroencephalograph (EEG) is an effectual and acknowledged tool to acquire and
record the electrical activity of the human brain [5]. It is being utilized substantially in the
current years to research on and detect multiple encephalopathies which may involve seizure prediction [6, 7], mild cognitive impairment (MCI) [8], the Alzheimer’s disease [9],
epileptic seizures [10–12], Creutzfeld–Jakob disease [13] the Parkinson’s disease [14, 15],
schizophrenia [16], evaluation of different emotional states [17], multiple sleep studies [18,
19], and brain computer interfaces (BCI) [20, 21]. EEG is highly recommended and most
desired diagnostic tool in the study of these neurological diseases due to its properties of
being non-invasive, reasonable, higher time resolution and having ease of operation and
comparatively less expensive than the magnetic resonance imaging (MRI) and computed
tomography (CT) scan.
According to studies in the domains of neuroscience, psychology, and cognitive science, EEG signals can reveal the bulk of brain functions and cognitive behaviour. The EEG
signals are closely linked to psychological processes and emotional states, and it’s likely
that they can reflect emotional changes in real time. Additionally, the electroencephalograph (EEG) acquires the electrical activity of human brain exceeding over a period of
time whereas the MRI is an imaging modality which records the variations in flow of blood
of brain within few seconds to about a minute. Therefore, the EEG signals are preferred
instead of MRI scans to recognize major depressive disorder subjects. It is quite evident
that the EEG signals of normal and depressed subjects are tumultuous and complicated
in nature. While having subtle variations that reflect multiple brain activities of these two
mentioned categories that could not be firmed readily with the help of visual observations.
Hence, a computer aided detection (CAD) structure is developed to detect depression from
the EEG signals of individuals.
Machine learning in the field of health care has offered promising services to enhance
the detection and treatment of some serious diseases, and today various promising
researches in healthcare sector can be credited to machine learning [22–24]. The machine
learning techniques related to the behavioral health offers tremendous benefits and ability
to aid in earlier detection and support towards the prevention of such disorders. However,
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A Machine Learning Framework for Major Depressive Disorder…
the preceding research has determined the usefulness of utilizing the EEG signals in the
classification of patients with major depressive disorder (MDD) and healthy subjects is
still challenging area of research. Long preparation time for recording EEG signals owing
to high quantities of data, long acquisition time for gathering necessary EEG signals, and,
most crucially, the identification of MDD from resting state EEG signals alone; when the
individuals are in the condition of either closed eyes or opened eyes. These limitations for
acquiring EEG for a long session would cause the subjects either fall sleep or get bored.
Apart from this, achieving greater and better accuracy for classifying MMD from normal
subjects with less recording time and short EEG signal recording sites can be a matter of
concern to address.
This study demonstrates a non-invasive short-time data acquisition system that will
diagnose the MDD from the subjects who tend to be in a resting and non-resting state
while performing a cognitive task. Along with it, various features (time-domain, frequency-domain and non-linear) of EEG signals are explored and a complete feature-based
framework was developed to analyze the EEG signals more extensively. By applying a feature selection technique, an optimal feature matrix was constructed for the major depressive disorder (MDD) classification process. Consequently, 242 features were identified and
extricated from the recorded EEG signals. Later all the extracted and selected features were
fed into a variety of Machine Learning models to evaluate the effect of selected features on
these ML models.
2 Related Work
The research mentioned in [5], researchers worked on the non-linear features of EEG
signals and computed Higuchi’s fractal dimension (HFD) and found out that MDD and
healthy controls are classified better in the beta band of EEG signals based on HFD, it
opposed the previous study which stated that the separation is best computed in the alpha
band. The HFD computed was higher in both beta and gamma bands of MDD subjects.
On the basis of HFD, they obtained a high accuracy of 91.3% and according to the study
HFD performed well than Katz’s Fractal Dimension (KFD). The study proposed in [25],
used Linear Discriminant Analysis (LDA) and Logistic Regression (LR) for the classification and acquired an accuracy of 73.3% from the alpha bands of EEG signals only. The
characterization of nonlinear features such as correlation dimension resulted in the high
accuracy of the LR classifier where it reached to 91%. Both the classifiers LR and LDA
worked well on non-linear features only. It also concluded that the right hemisphere of
brain differentiates the depression with better results as compared to the left part of brain.
Additionally, findings included that nonlinear features provides better classification results
of depressed and healthy subjects as compared to the linear features. The study proposed
by Acharya et al. [26] in the year 2015, showed the feature ranking acquired accuracy of
98% and sensitivity of 97%, whereas specificity of 98.5%. In this study the SVM classifier outperformed the rest of the classifiers. SVM classifiers having a polynomial kernel of
3rd order was used for left and right hemispheres of brain, using averaged values for both
hemispheres.
Bairy et al. [27] in the same year 2015 worked on the non-linear features (Sample
Entropy, Fractal Dimension, Correlation Dimension, Hurst exponent) of EEG signals only
and later acquired an accuracy of 93.8% having sensitivity of 92%, and specificity 95.9%.
From the study it is hard to understand the type of validation was performed whether
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N. Bashir et al.
internal or external validation. For instance, the technique utilized to measure the fractal
dimension was not reported properly thus resulting in limited reproducibility. The study
proposed by Liao et al. [28] obtained 80% of accuracy in which a spectral-spatial Electroencephalogram feature extractor was developed, called the kernel Eigen-filter-bank common spatial pattern (KEFB-CSP). The data were collected covering the range of all the
bands of EEGs (alpha, beta, gamma, theta and delta). In the year 2018, Mumtaz et al. [29]
proposed a study using various classifiers to differentiate EEGs of MDD and normal subjects. SVM classifier acquired an accuracy of 98%, the classification accuracy of LR was
91.7%, and NB acquired an accuracy of 93.6%. With the HFD and detrended fluctuation
analysis (DFA) along with HFD and Lempel–Ziv complexity (LZC) Bachmann et al. [30]
in the year 2018 obtained the maximal accuracy of 85% by using LR classifier for differentiating MDD and normal subjects. The various classifiers reported by Čukić et al. [31] LR,
SVM (with linear and polynomial kernel), DT, and NB achieved the accuracies ranging
from 90.24 to 97.56%. Among the computed two measures, the sample entropy (SampEn)
exhibited better performance. The number and placement of electrodes have been an
important factor while acquiring the EEG signals from the subjects from the resting-state,
because the principle component analysis (PCA) study demonstrated that each electrode
has a contribution of its own to the results acquired [31, 32].
To sum up the related work that has been stated so far, the proposed studies focused on
the improvement of the classification results with the necessary measures to be taken. Most
of the studies are based on the classification of individuals with depression and healthy
participants on their resting-state EEGs only, majority of them exhibited high accuracy
while using different combinations of features used and machine learning models. But the
studies conducted on differentiating depressed and normal subjects based on resting–state
EEGs only (either eyes open or eyes closed) has reached to a bottleneck, which needs to
be addressed. Lastly, all the studies had a modest number of sample sizes, which in result
affected the generalizability of model. A summarized form of related research is presented
in the Table 1.
3 Non‑Invasive EEG Based Acquisition System
Electroencephalography (EEG) is a tool used to acquire and record the electrical activities which are originated from nerve cells in the area of cerebral cortex of human brain.
Being the solely non-invasive technique for analyzing and obtaining the brain activities
from scalp, it is being broadly utilized in various areas of neuroscience research, commercial applications and different medical diagnosis for research purposes. In the proposed
study, the experimental dataset to be used was obtained from 34 major depressive disorder
(MDD) patients which included 18 females and 16 males whereas the mean age was 40.33
and standard deviation was ± 12.861. Along with it a group of age-matched 30 healthy
individuals were asked to volunteer, which included 9 females and 21 males. Their mean
age was 38.227 and standard deviation was ± 15.64. The major depressive disorder subjects
were the ones who underwent the diagnostic standards for depression as per the instructions of Diagnostic and Statistical Manual-IV (DSM-IV) [33], these subjects were inducted
from Hospital Universiti Sains Malaysia’s outpatient clinic. The criterion of identification
for major depression was cleared by the MDD subjects with the absence of any demented
signs and symptoms. The consent form was signed by both the group participants. The
experimental procedure which was guided to both the groups was accepted by the ethics
13
12 + 12
Ahmadlou et al. [5]
15 + 15
12 + 12
34 + 30
13 + 13
26 + 20
Acharya et al. [26]
Liao et al. [28]
Mumtaz et al. [29]
Bachmann et al. [30]
Čukić et al. [32, 33]
Broadband EEG, PCA and Ten-fold
cross validation
Common spatial pattern
REST
Fourier
Broadband
Discrete cosine transforms
30 + 30
(left brain
only)
Standard spectral bands
Wavelets and spectral bands (Fourier), bootstrap
Preprocessing
Bairy et al. [27]
Hosseinifard et al. [25] 45 + 45
Samples
Study
Table 1 Summarizes the related work which has been stated
Spectral (common spatial pattern)
Synchronization likelihood
HFD, DFA, Lempel–Ziv complexity,
and SASI
HFD and SampEn
FD, LLE, SampEn, DFA, DET,
ENTR, LAM, T2 (DDI)
Power, DFA, HFD, CD, Lyapunov
exponent
SampEn, FD, DFA, CD, Hurst exponent, LLE
HFD and KFD
Features
LR, SVM (with linear and polynomial kernel), DT, NB
KEFB-CSP
SVM, LR, NB
Logistic regression
SVM, KNN, NB, PNN, DT
DT, KNN, NB, SVM
KNN, LR, LDA
Enhanced probabilistic neural
networks
Ml models
Accuracy 97.50
Accuracy 93.80
Specificity 95.9
Sensitivity 92
Accuracy 98
Specificity 98.5
Sensitivity 97
Accuracy 80
Accuracy 87.50
Accuracy 88
Accuracy 90
Accuracy 91.30
Results
A Machine Learning Framework for Major Depressive Disorder…
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N. Bashir et al.
committee of the hospital. An examination of the healthy individuals was done in order to
eliminate the possibility of physical or mental impairment which then was found normal.
The consumption of caffeine, alcohol and nicotine was strongly inhibited for the participants before recording the EEG signals. The experiment of data acquisition from the
participants was done at the same time of the day. The experimental data procurement
comprised of EEG data recordings for five minutes during three different conditions; eyes
closed (EC); eyes open (EO) and a decision making task (TASK). The data acquisition
of electroencephalograph included 22 channeled EEG cap sensors which were allocated
by following the placement standard of the 10–20 electrode system [34] with electrodes
placed all over the scalp and on four different lobes. Inion is external occipital protuberance tip. Nasion is present in middle of eyes and is a bony depression where the two nasal
and frontal bones meet as recommended in [35] which is shown in Fig. 1. The total number
of electrodes connected to sensors while sheathing the scalp were acquired from the four
lobes of brain, temporal lobe, the frontal lobe, occipital lobe, parietal lobe and the central
area.
4 Data Preprocessing
Electroencephalography is a procedure to capture signals of brain activity by sensors.
However, the recorded EEG data is contaminated with various kinds of interferences. The
main focus while preparing the data is to attenuate the artifacts and noise from the environment or surroundings (exogenous) and organic (endogenous) sources [36].
4.1 Preprocessing
To set the seal on an accurate result in further processes like classification and feature
selection, all the contaminated data should be denoised initially. Noise minimization
procedure is categorized into two major parts: elimination of contaminated data chunks
and attenuation of artifacts by utilizing the signal-processing techniques [37, 38]. Use of
an adaptive filter was recommended by the National Institute of Mental Health to calculate the artifacts which could be removed from the EEG data [39]. A series of filters
Fig. 1 Illustrates the 10–20 electrode placement system
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were used to denoise and remove the interferences from the EEG signals. Butterworth
highpass and bandpass filters with cutoff frequencies ranging from 0.1 to 70 Hz were
used to attenuate the signals at a sample rate of 256 Hz. The 50 Hz power line is then
further denoised with a notch filter, which is similar to a band stop filter.
4.2 Feature Extraction
The EEG signal shows nonlinear, fragile, and time-sensitive characteristics that frequently show complicated fluctuations. With the change in emotional state, the features
of EEG signals tend to change. In recent studies [40–43], the examination of these brain
signals revealed numerous linear properties such as skewness, peak, and variance. Nonlinear metrics for pathological signals, such as correlation dimension, have been determined via research and are regarded to be valuable markers of different illnesses [44].
The feature extraction must first be conducted on the preprocessed EEG data in order
to acquire the generated feature matrix. Frequency domain and time domain features
are the two basic types of features seen in EEG data. The following characteristics were
retrieved in this investigation due to the nonlinearity and inconsistency of the EEG
signals:
Frequency Domain Features The frequency domain allows for the categorization and
characterization of EEG data. The characteristics retrieved from the frequency domain in
the proposed study are absolute and relative power.
Time Domain Features The most intuitive characteristics of EEG are those in the time
domain. The EEG signals are collected at a certain time and frequency. Contaminants from
EEG signals are removed from the time domain directly, and necessary data is generated as
a time domain feature that may be used for continuous extended EEG diagnosis. Skewness,
variance, peak, and Hjorth parameters are among the temporal domain variables investigated in this study. Hjorth introduced three parameters for measuring statistical qualities
for signal processing in the time domain in 1970 [45]. These parameters are activity, mobility, and complexity. The activity variables represent the signal’s strength and the variation
of the function of time among them. The mobility parameters, on the other hand, are the
ones that show the frequency mean or standard deviation portion of the power spectrum.
The frequency of the complexity parameters varies.
Nonlinear Features The EEG signals are dynamic and irregular; including few features
of the nonlinear dynamics system. Enormous studies being conducted on the EEG signals,
EEG’s specialty of being nonlinear has been under exclusive focus globally. As a result,
using nonlinear dynamics theory to analyze and evaluate EEG signals has been a more
advanced study field for researchers [46–48]. The non-linear characteristics recovered in
this study include Shannon Entropy, Power-Spectral Entropy, and correlation dimension.
1. Shannon in the year 1948 presented Shannon Entropy in an article titled “A Mathematical Theory of Communication” [49]. Shannon Entropy is basically a calculation of variability of a random signal and a random variable. The greater the amount of uncertainty
and randomness it will result in the larger value of entropy. In this proposed methodology, the entropy utilized to process EEG signals can be seen as a measure of the series
in the signal, which calculates the uncertainty and skewness of EEG [50]. If we take
the case of random variables whose probability distribution is known, the entropy will
be defined as:
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E(S) =
∑
p(s) log p(s),
(1)
s∈S
Here S is denoted as a random variable with probability distribution p(s) and alphabet
set S [51].
2. The power-spectral entropy is described as a sequence of power densities and frequency
distributions obtained by the Fourier transform. The calculation of the entropy of the
power spectrum, also known as the Power-Spectral Entropy, may then be carried out
effortlessly. The Power-Spectral Entropy is used to evaluate the timing of signals in EEG
data. The entropy may be used as a physical indicator to estimate the frequency and
strength of brain activity. A more functional brain will result in a larger level of entropy.
3. The elementary correlation dimension. In 1983, Grassberger and Procacia devised the
simple correlation dimension method [52]. Correlation Dimension depicts the dynamic
features of EEG signals. When the value of correlation dimension is larger, the EEG
time series is considered difficult. It’s essentially a fractal dimension that’s often established using a time series diagram. Correlation dimension is a straightforward phase
space diagram that is constructed from a single data vector, as seen below:
(
)
ln C(d)
CR = lim
,
(2)
d→0
ln d
Here C(d) is exhibiting the correlation integral and d is the radial interspace throughout
each reference point.
4.3 Feature Selection
The feature Selection approach selects a related subset of all the collected features, which
not only assists to provide a low dimensionality of the classification issue but also reduces
noise and irrelevant features. The applied approach was used to further reduce the various
types of data in order to choose the important features for EEG signal detection.
In order to acquire better results, the technique of minimal redundancy-maximal relevance is applied to perform the feature selection process. The minimal redundancy-maximal relevance (MRMR) feature selection principle was introduced by Peng et al. [53] to
sort out the concern by analyzing both the relevance and feature redundancy concurrently;
specifically, maximum relevance, shown as MAX R (F,l), it involves to increase the relevance of a particular feature subset F in accordance to the class label l. The relevance
property of the feature subset is described in [53] as:
MAX R(F, l) =
1 ∑ ( )
𝜑 ri , l ,
|F| ri∈F
(3)
( )
where 𝜑 ri , l describes the relevance property of a feature ­ri to l. 𝜑 can be approximated by
utilizing some particular correlation operations.
When two related features are highly dependent on one another, and when one of the
features is eliminated the strength of class-discrimination would not vary drastically.
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A Machine Learning Framework for Major Depressive Disorder…
Minimum redundancy, MIN D (S), is made to choose a subset of feature mutually. The
redundancy of the subset of feature is described as below:
MIN D(S) =
1 ∑
𝜑(pj , pk ).
|S|2 p p ∈S
(4)
j k
The minimal-redundancy-maximal-relevance (MRMR) is described as the common operator maximizing R and minimizing D simultaneously. In [54], the increasing
research technique was utilized to achieve the near-optimal characteristics. The feature
subset ­St−1 of t − 1 chosen feature is used to pick the t-order feature that optimizes the
following mentioned equation:
( )
max [𝜑 pj , l −
pk ∉St−1
1 ∑
𝜑(pj , pk )].
t − 1 p ∈S
j
(5)
t−1
Consequently, a total of 242 features (11 basic features × 22 electrodes) were identified and extricated from the five brain waves. The entire mentioned frequency domain,
time domain and the nonlinear features have regular information about the EEG signals.
5 Classification
As literature suggests, various classifiers for instance, SVM, KNN, and DT are broadly
utilized as classification algorithms in the most of the researches related to EEG signals.
In the proposed study, the performance of these three classifiers (SVM, KNN, and DT)
was analyzed. Also, one deep learning classifier [long short term memory (LSTM)] was
also evaluated in the diagnosis of MDD. All classifications done by these classifiers and
the10-fold cross-validations were executed. The proposed methodology is illustrated in
the Fig. 2.
6 Machine Learning Classifiers
6.1 Decision Tree
Decision trees [55, 56] which are non-parametric and comes under the category of supervised learning recurrently partitions the space of feature in the areas which correlate to the
classes by selecting a feature, which will then supply the high-raised information gain. The
recursively partition of the feature space stops when the minimum number of specimen per
node of a decision tree is extended to the value of two. In this study, the random state of
DT is set to 0 to achieve a consistent output in every call. During the phase of pruning, that
is basically based on estimation of the error of classification, the complication of the model
can be decreased which will result in an improved generalization capacity. 80 percent of
the data was set for training and 20% for testing from the whole dataset using the frequency
domain, temporal domain, and time–frequency domain feature extractions. Figure 3a, b
shows the training accuracy, validation accuracy, training loss and validation loss of this
model for 50 epochs.
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Fig. 2 Illustrates the block diagram of the proposed methodology
6.2 Support Vector Machine
SVM divides the feature space into decision boundary lines, linear in the amended area,
justified by the kernel function, and uniquely given by a subset of the data [57]. Support
vector machines creates a maximum margin classifier that escalates the gap between the
boundary of decision and the support vectors. In this proposed research, the radial basis
function (RBF) kernel function was utilized with the classifier soft-margin along with the
regularization persistent value a constant C = 1 and an optimization algorithm [58]. The
degree was set to 3 and cache size was 200. SVMs are supervised by nature and by design
it enhances the classifier margin, and therefore, most probably, minimizes the phenomenon
of overfitting. For 50 epochs the training accuracy, validation accuracy, training loss and
validation loss of SVM was attained which is further presented in Fig. 4a, b.
6.3 K‑Nearest Neighbors
K-Nearest Neighbors is a supervised classifier and is one of the simple classification models. KNN relies on an in-space function for pairs of monitoring. In K-Nearest Neighbor
algorithm, k-nearest sample of training is found for a test sample. Later, the sample for
testing is allocated to a certain class which tends to be the most frequent class out of all the
k-nearest training data. KNN algorithm is only in need of a value of integer for the variable k and a metric to calculate the closeness [59]. In this proposed study, the n-neighbors
hyper parameter was set to 5 which is a default value. The odd number is chosen to avoid
any hitch in the classification. To check the performance the model the training accuracy,
validation accuracy, training loss and validation loss was obtained as shown in Fig. 5a, b.
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A Machine Learning Framework for Major Depressive Disorder…
Fig. 3 a The training and validation accuracy of the Decision Tree model. b The training and validation
loss of the Decision Tree model
7 Deep Learning Aproach
7.1 Long Short Term Memory (LSTM)
LSTM is an augmentation of the recurrent neural networks which was introduced by
Hochreiter and Schmidhube in the year 1997. By nature these kinds of networks have the
capacity to relocate a hidden state as a reflection of what is being going across the network.
LSTMs are credited to solve various sequential classification concerns with flying colors
[60]. The purpose to introduce LSTMs was the inflating problem in RNN’s gradient loss
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Fig. 4 a The training and validation accuracy of the support vector machine model. b The training and validation loss of the Support Vector Machine model
[61], LSTM aids to intercept the initial layers of RNN which needs to be upgraded with
gradient vector. Here the real concern is related to the local gradient which gets near to the
value of zero as it reaches the first layers, which results in reduction of the learning effect.
LSTM comprises of number of gates which have an ability to carry important data for huge
sequences and also give supervision over flow of data. LSTMs are similar to RNNs in a
way that, every cell of LSTM progresses a hidden state to the upcoming layer.
The architecture of LSTM model is based on variety of layers, for instance, there is
an input layer and an output layer. Apart from this, there can be one or more LSTM layers and dense layers as well. The total number of iterations employed for training were
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Fig. 5 a The training and validation accuracy of the developed K-Nearest Neighbors model. b The training
and validation loss of the developed K-Nearest Neighbors model
500 epochs and along with it the sigmoid was selected as an activation function. The
model utilized in this proposed study had a LSTM layer with the number of 64 neurons,
having 50% dropout along with it 32 neurons were present in the dense layer. As LSTM
extracts the temporal information quite efficiently so as EEG has an excellent temporal
resolution. Therefore, the proposed model obtained satisfactory performance. Whereas,
the training accuracy, validation accuracy, training loss and validation loss of the LSTM
model for 500 epochs are shown in Fig. 7a, b.
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Table 2 Illustrates the
summarized results of proposed
study
Performance measures
SVM
DT
KNN
LSTM
Sensitivity
60
86.6
93.3
86.7
Specificity
Precision
Accuracy
77.78
56.25
676.6
66.66
68.42
79.1
77.78
66.7
87.5
77.7
65
83.3
8 Results
The primary aim of this research was to provide a feature based machine learning frame
work for MDD detection using EEG data. In order to execute this, the total number of
4224 EEG signals consisting of 22 channels × 3 states (EO, EC, TASK) of 64 subjects were
explored, preprocessed and further analyzed. This was accomplished by computing frequency domain, time domain and nonlinear features. Consequently, three ML based classifiers, KNN, SVM and DT were trained to differentiate the EEG signals of major depressive
disorder subjects from the normal subjects. Moreover, due to the advent and wide espousal
of deep learning architectures LSTM model was also implemented and analyzed. Initially,
the EEG signals were preprocessed with the help of filters, such as, Butterworth and Notch.
Subsequently, around 11 features (absolute power, relative power, variance, skewness,
peak, mobility, complexity, activity, correlation dimension, Shannon and Power-Spectral
Entropy) were extracted from the preprocessed data. These extracted features are based on
frequency domain, time domain and are non-linear in nature.
Later, feature selection technique minimal redundancy-maximal relevance (MRMR)
was applied to obtain only significant attributes which were to be fed into the machine
learning classifiers. The machine learning classifiers used were DT, SVM and KNN. The
performance measuring variables such as, accuracy, sensitivity, specificity and precision
were computed and later the results were compared.
The obtained results comprised on the classification accuracy, loss and computational resource in terms of training time. The KNN classifier outperformed the other
two machine learning classifiers by achieving the highest accuracy of 87.5%, specificity
of 77.78%, sensitivity of 93.3% and 66.7% precision. DT achieved an accuracy of 79.1%
specificity of 66.66%, 86.67% sensitivity and 68.42% precision. SVM was the classifier
with least accuracy of 66.6% specificity of 77.78%, sensitivity of 60% and 56.25% precision. On the contrary the deep learning approach, i.e., LSTM model did competently
well and achieved an accuracy of 83.3% specificity of 77.77%, sensitivity of 86.67%
Fig. 6 The architecture of a single LSTM cell
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Fig. 7 a The training and accuracy of the developed LSTM model. b The training and validation loss of the
developed LSTM model
and 65% precision as d indicated in the Table 2. To conclude the results, three machine
learning (KNN, DT, and SVM) and a deep learning (LSTM) model was computed and
analyzed by using the EEG dataset. KNN proved to be the best in terms of accuracy
and acquired 87.5% of accuracy. The training and validation accuracies, training and
validation losses of all the models are exhibited in the Figs. 4, 5, 6 and 7. While the
ROC curve in Fig. 8 indicates that the performance of all the models based on tradeoff
between true positive rate (TPR) and false positive rate (FPR). Additionally, the computational time was also noted for the training process of each model. It was observed that
LSTM model consumed more training time whereas DT took the least time to get train
as further shown in Table 3.
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N. Bashir et al.
Fig. 8 ROC curve for the trained ML models
Table 3 The training time of all
the models used in the proposed
study
Models
Time (s)
KNN
198.8
DT
SVM
LSTM
193.3
207.3
1252.17
9 Conclusion and Future Work
There are various health diseases that arise from weak and fragile mental states. One of
the most common heinous deformities is depression. Physical injuries result in visible
and aching signs and because of these obvious symptoms, they are taken seriously and
recognized. There are no such visible signs and symptoms of mental ailment. Most of
people are not even well informed about them, counting the victims as well. EEG signals are helpful for the analysis of MDD. These brain signals provide better information
for depression analysis and are easy to acquire and economical as well. Compared to the
previous research, the proposed experiment exhibited more reliable data including the
resting and non- resting EEGs, for obtaining better experimental results.
Altogether, the proposed research for MDD detection via a Machine learning
approach based on feature extraction and selection decreased the number of data to
be processed with vigorous data processing ability. To avoid the complexities in the
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A Machine Learning Framework for Major Depressive Disorder…
classifier, the selection of less number of features was encouraged to help in the simple
interpretation; resulting in a decreased computational burden on the system. Therefore,
the current study to differentiate depressed patients from healthy subjects and their mental states can help medical care experts with early intervention and prevention of this
detestable deformity.
An extension of this method on the larger dataset is needed to emphasize. Along
with it the signals from various modalities like fNIRs or MIRs should be analyzed and
compared with EEG signals, this will further provide support in obtaining more robust
results. Meanwhile, use of fewer electrodes should be tested to optimize the existing
models and more emotion or non-resting datasets should be utilized for exploring and
knowing the potential applicability of various ML-based classification methods for
diagnostic purposes.
Funding This research is partially supported by the Department of Computer Systems Engineering, Mehran
University of Engineering and Technology.
Declarations
Conflict of Interest Authors do not have any conflict of interest.
Availability of Data and Material Enquiries about data availability should be directed to the authors.
Code Availability Enquiries about code should be directed to the authors.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
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law.
Nayab Bashir received her degree in master of Engineering in the
field of Information Technology in 2022 from the department of Computer Systems Engineering, Mehran University of Engineering and
Technology, Jamshoro. She received her BE degree in Biomedical
Engineering in 2018 from MUET. Her research interest includes
Machine Learning, Deep Learning, Medical Imaging and Digital Signals Processing.
Sanam Narejo is currently working as an Associate professor at the
Department of Computer Systems Engineering, Mehran University of
Engineering and Technology (MUET), Jamshoro. She has completed
her Ph.D. from Politecnico Di Torino, Italy in 2018. She received her
Masters degree in Communication Systems and Networking from
MUET. Her research interests include Signal and Image Processing,
Machine Learning and Deep Learning Architectures. She has also
been an active member of Italian Society of Neural Networks (SIREN),
PEC, IEEE and ACM-W Jamshoro chapter.
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A Machine Learning Framework for Major Depressive Disorder…
Bushra Naz received her B.E. degree in computer systems engineering and the M.E. degree in communication systems and networks from
the Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan, in 2007 and 2009, respectively. From 2010 to 2011,
she was a Senior Research Fellow with the University of Science and
Technology, Beijing, China. She has pursued her Ph.D. degree with the
School of Computer Science and Engineering, Nanjing University of
Science and Technology, Nanjing, China in 2018. She is currently
working as Associate Professor at the Department of Computer Systems Engineering, MUET. Her research interest includes Image Processing, Regularization methods for Machine learning. Hyperspectral
Image Classification, denoising and Remote Sensing Image Analysis.
Fatima Ismail is currently working as Assistant Professor in the
department of BBT, The Islamia university of Bahawalpur, Pakistan.
Her research interest includes bioinformatics and Microbiology.
Muhammad Rizwan Anjum received his Ph.D. degree from Beijing
Institute of Technology, Beijing China in 2015. M. Engg. in Telecommunication and Control Engineering and B.E. in Electronic Engineering in 2011 & 2007 respectively from Mehran UET Jamshoro, Pakistan. Presently working as Associate Professor in the Department of
Electronic Engineering, The Islamia university of Bahawalpur, Pakistan. He has more than 30 international conferences and journal publications. He is a member of PEC, IEEEP, IEP, IJPE, UACEE, IACSIT,
ICCTD, IACSIT, IAENG, etc. and reviewer of several journals and
conferences.
13
N. Bashir et al.
Ayesha Butt is working as a lecturer at SZABIST institue. She
received her M.E degree in Information and Technology from Mehran
University of Engineering and Technology (MUET) in 2022. Her
research interests include Signal Processing, Machine learning and
Deep Learning Architectures.
Sadia Anwar completed her Ph.D. in 2020 from Aarhus University,
Denmark, in Integrated Technology-based Health Applications and
User Specificities for Treatment Adherence. She received her degree in
Doctor of Pharmacy from Government College University, Pakistan.
She had worked for three years as a community pharmacist. She also
served as a Hospital Pharmacist and Drug information consultant. She
started working as a Guest Researcher at CTiF in the department of
electronic systems, Aalborg University, under the supervision of Professor Ramjee Prasad. She served in the Interdisciplinary area specifically focused on four sections: Medicine, Telecommunication, Big
data, and economics. As Research Scientist, she joined Aarhus University in December 2016 and the Department of Business Development
and Technology. She has been a Research Assistant in the same
Department since October 2017. Her research interests are based on
the interdisciplinary area of Medicine, eHealth, ICT, Assistive technologies, and Green Business model Development. She has worked
with EU and Interreg projects. She is also involved in administrative
and managerial tasks. She has produced many research articles for conferences and Journals as an Author
and co-Author.
Ramjee Prasad is a Professor of Future Technologies for Business
Ecosystem Innovation (FT4BI) in the Department of Business Development and Technology, Aarhus University, Denmark. He is the
Founder President of the CTIF Global Capsule (CGC). He is also the
Founder Chairman of the Global ICT Standardization Forum for India,
established in 2009. GISFI aims to increase the collaboration between
European, Indian, Japanese, North-American and other worldwide
standardization activities in the area of Information and Communication Technology (ICT) and related application areas. The University of
Rome “Tor Vergata”, Italy as a Distinguished Professor of the Department of Clinical Sciences and Translational Medicine honored him on
March 15, 2016. He is Honorary Professor of University of Cape
Town, South Africa, and University of KwaZulu-Natal, South Africa.
He received Ridderkorset af Dannebrogordenen (Knight of the Dannenberg) in 2010 from the Danish Queen for the internationalization of
top-class telecommunication research and education. He has received
several international awards such as IEEE Communications Society
Wireless Communications Technical Committee Recognition Award in 2003 for making contribution in the
field of "Personal, Wireless and Mobile Systems and Networks", Telenor’s Research Award in 2005 for
impressive merits, both academic and organizational within the area of wireless and personal communication, 2014 IEEE AESS Outstanding Organizational Leadership Award for: “Organizational Leadership in
developing and globalizing the CTIF (Center for TeleInFrastruktur) Research Network”, and so on. He has
been Project Coordinator of several EC projects, namely, MAGNET, MAGNET Beyond, eWALL, and so
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A Machine Learning Framework for Major Depressive Disorder…
on. He has published more than 30 books, 1000 plus journal and conference publications, more than 15 patents, over 140 Ph.D. Graduates and a more significant number of Masters (over 250). Several of his students
are today worldwide telecommunication leaders themselves. Under his leadership, magnitudes of close collaborations are being established among premier universities across the globe. The collaborations are regulated by guidelines of the Memorandum of Understanding (MoU) between the collaborating universities.
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