Background
Depression, a common psychiatric disorder with a lifetime prevalence of ~ 20 % in the general population, is associated with high rates of disability, impaired psychosocial functioning and decreased life satisfaction [
1]. Early recognition and accurate diagnosis of depression are essential criteria for optimizing treatment selection and improving outcomes, thus reducing the economic and psychosocial burdens resulting from hospitalization, lost work productivity and suicide [
2‐
4]. Guided by established classification criteria (DSM-5) [
5], the diagnosis of psychiatric disorders including depression relies solely on inferences based on self-reported information and observed behaviour. Identifying people with established depression does not usually present as a clinical challenge with standard clinical instruments but the potential for ambiguity, bias and low reliability of a diagnosis of depression based on clinical descriptions can be compounded by the heterogeneous nature of the disorder. There are a number of DSM-5 defined depressive disorders (e.g. major depressive disorder [MDD], dysthymia, depressive disorder not otherwise specified [NOS]) and, for unipolar MDD, there are symptom based subtypes (e.g. melancholic, psychotic and atypical depression); symptoms can also vary by gender, age and even race [
6].
Defined as objective biological measures indicating the state of a normal biologic process, pathogenic process, or pharmacological response to a therapeutic intervention [
7], biomarker use for diagnostics has become standard in day-to-day practice in medicine (e.g. cardiology, oncology) but there are no accepted biomarkers for MDD or other psychiatric disorders. Recent progress has provided evidence that psychiatric disorders are brain disorders characterized by abnormalities in the structure, function and neurochemistry in distributed neural networks [
8]. Neuroimaging, which allows for
in vivo access to these brain circuits, has increased our understanding of the pathophysiology of these disorders [
9,
10] and is a leading candidate for the development of clinical biomarkers with potential use for diagnosis, prognosis and treatment of depression [
11‐
16].
For biomarkers to be diagnostically useful, they need to be reliable and reproducible, providing sufficiently high levels of sensitivity and specificity in the detection and correct classification of distinct disorders [
17]. Furthermore, for routine use in clinical practice, they should be inexpensive, noninvasive and easily accessible [
17]. Compared to some other proposed brain imaging biomarkers derived from functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and magnetic resonance spectroscopy (MRS), quantitative measurement of brain electrical signals taken from the scalp-recorded electroencephalogram (EEG) is a neuroimaging technique with clear practical advantages as it does not involve invasive procedures, is widely available, easy to administer, well tolerated, and has a relatively low cost [
18]. In addition to its growing potential as a biomarker in the therapeutic drug development process [
19,
20] and in predicting antidepressant treatment response [
21‐
25], power spectral measures of resting state EEG oscillatory activity in different frequency bands (delta [<4 Hz], theta [~4–8 Hz], alpha [~8–12 Hz], beta [~12–30 Hz) have been shown to distinguish between depressed patients and healthy controls [
26-
28]. However, EEG biomarkers/biosignatures characterizing brain abnormalities in depressed patients tend to be limited to group-level comparisons. Although they are informative in elucidating the neuropathophysiology of depression, investigations have not systematically examined whether or not these EEG measurements can be useful, at the individual level, in diagnosing whether a given subject is or is not depressed.
Studies focusing on individual-level neuroimaging data analyses are necessary if this approach is to be clinically useful [
16] but the inherent complexity of the data and its analyses continues to be an obstacle [
10]. Recent advances in EEG acquisition (high density systems) and processing has added to this complexity but this growth has been paralleled by the increased availability of machine learning methods. Unlike conventional analyses, machine learning classifiers are designed to deal with multivariate inputs — treating the EEG measures as patterns rather than considering each measure in isolation [
16,
29,
30]. To date, the limited number of machine learning studies on resting state EEG in depression have used varying classification algorithms and have been found to classify MDD patients and healthy controls with an overall accuracy ranging between 60–90 % [
31‐
35].
Despite their promise as a supplementary, computer-aided diagnostic approach, these analytic methods have not clearly delineated the contributing role of oscillatory activity in each frequency band and/or brain region to the machine learning classifiers. Further, they have not yet examined the role of vigilance states (e.g. eyes open vs. eyes closed) or recording montages (e.g. unipolar vs. bipolar EEG recordings). It is also unclear from the existing machine learning EEG studies if classification accuracy is different when analyzing data from each frequency band compared to when data from all bands are analyzed together.
EEG is sensitive to a continuum of states ranging from stress states, alertness to resting state, and sleep, and various regions of the brain do not emit the same oscillatory activity simultaneously. During the normal state of wakefulness with eyes open fast frequency (beta) oscillations are dominant in central-frontal scalp areas. During relaxation recorded in an eyes-closed resting condition, alpha activity in the EEG is dominant in posterior scalp regions and is markedly diminished when individuals open their eyes, perhaps reflecting widespread communication of cortical and thalamo-cortical interactions to aid information processing of visual input [
36,
37].
Several difference recording reference electrode placements are mentioned in the literature. The choice of reference may produce topographic distortion in oscillatory signals if a relatively electrically neutral site is not employed. Referencing to linked mastoids/earlobes and vertex scalp (Cz) are predominant in the depression EEG literature and may account for differences across studies as each technique has its own set of advantages and disadvantages. Linking reference electrodes from two earlobes or mastoids reduces the likelihood of artificially inflating activity in one hemisphere but this method may drift away “effective” reference from the midline plane if electoral resistance at each reference electrode differs [
38]. Cz reference is advantageous when it is located in the middle among active electrodes, however for closer points it makes poor resolution.
In this study, multi-feature data mining methodologies were used to classify MDD patients and non-depressed individuals using EEG data in six frequency bands derived from 28 scalp sites during both eyes-open and eyes-closed resting states, and computed with mastoid-based unipolar (measuring the difference between EEG signals at the scalp and a neutral non-scalp signal) and Cz-based bipolar (measuring the EEG difference between pairs of EEG scalp signals) referenced recordings. The aim was to assess whether these analytical approaches to EEG may provide an objective complementary tool to MDD diagnosis.
Results
The datasets of the four bands (alpha, beta, delta and theta) during the EC and EO conditions were analyzed based on two views to determine the most accurate approach which might be: 1) Each band was analyzed individually during each condition EC and EO for each reference (Mastoid and Cz); and 2) The four bands (alpha, beta, delta and theta) were grouped together to be analyzed as one dataset in each condition EC and EO for Mastoid and Cz references.
Then, the testing dataset was used to validate the predictive model before and after applying GA and LDA. After that, the results were evaluated based on the Sensitivity, Specificity, Positive and Negative Likelihood Rates (LR+ and LR-), Positive and Negative Predictive Values (PPV and NPV), accuracy, and Error rate for depressed and healthy individuals, see sections
Mastoid reference—bands analyzed individually,
Cz reference—bands analyzed individually,
Mastoid reference—bands analyzed together, and
Cz reference—bands analyzed together for more details of the analysis. In addition, Section
Model evaluation presents the results of the new obtained datasets, and it is used to evaluate the model that consists of the whole dataset that is used through Sections
Mastoid reference—bands analyzed individually,
Cz reference—bands analyzed individually,
Mastoid reference—bands analyzed together, and
Cz reference—bands analyzed together.
Mastoid reference—bands analyzed individually
The results of analyzing each band separately during the EO and EC conditions using a mastoid reference are presented in Table
2. Apart from the delta band, classification error rates were relatively high as evidenced by accuracies ranging from 40–66 %. Low specificities were also noted in non-delta bands (range: 0–54 %). With more than half of the classifiers, sensitivity, specificity, Positive Likelihood Ratio (LR+) and NPV (Negative Predictive Value) rates increased following GA and LDA application but this was not necessarily associated with higher accuracy. Delta was the exceptional individual band classifier when analysed during EO. Although showing less than chance accuracy when analyzed with all candidate features, feature reduction with GA and LDA markedly increased sensitivity, specificity and accuracy rates > 80 %. These results were accompanied by increases and decreases in LR+ and LR-, respectively. As displayed in “Additional file
1: Table S1” the specific scalp sites contributing to these findings were distributed over frontal, central and posterior regions of both hemispheres.
Table 2
Results- analysis of the individual bands- mastoid reference
Alpha 8–10.5 Hz | EC | 28 Raw Features | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
12 (GA + LDA) | 71 | 55 | 1.29 | 0.65 | 67 | 50 | 61 | 6 | 5 |
EO | 28 Raw Features | 75 | 63 | 1.2 | 0.67 | 17 | 90 | 43 | 15 | 1 |
15 (GA + LDA) | 56 | 80 | 0.69 | 2.22 | 56 | 20 | 43 | 8 | 8 |
Alpha 10.5–13 Hz | EC | 28 Raw Features | 67 | 63 | 1.06 | 0.9 | 33 | 70 | 46 | 12 | 3 |
11 (GA + LDA) | 67 | 62 | 1.08 | 0.87 | 56 | 50 | 54 | 8 | 5 |
EO | 28 Raw Features | 77 | 53 | 1.44 | 0.49 | 56 | 70 | 61 | 8 | 3 |
10 (GA + LDA) | 82 | 36 | 2.26 | 0.28 | 78 | 70 | 75 | 4 | 3 |
Alpha 8–13 Hz | EC | 28 Raw Features | 64 | 65 | 0.98 | 1.03 | 39 | 60 | 46 | 11 | 4 |
5 (GA + LDA) | 73 | 59 | 1.24 | 0.66 | 44 | 70 | 54 | 10 | 3 |
EO | 28 Raw Features | 77 | 53 | 1.44 | 0.49 | 56 | 70 | 61 | 8 | 3 |
14 (GA + LDA) | 100 | 38 | 2.67 | 0 | 67 | 100 | 79 | 6 | 0 |
Beta | EC | 28 Raw Features | 57 | 71 | 0.8 | 1.5 | 44 | 40 | 43 | 6 | 10 |
10 (GA + LDA) | 64 | 64 | 1 | 1 | 50 | 50 | 50 | 9 | 5 |
EO | 28 Raw Features | 59 | 73 | 0.81 | 1.51 | 56 | 30 | 46 | 7 | 8 |
11 (GA + LDA) | 67 | 63 | 1.07 | 0.89 | 44 | 60 | 50 | 10 | 4 |
Delta | EC | 28 Raw Features | 67 | 64 | 1.04 | 0.93 | 11 | 90 | 39 | 16 | 1 |
18 (GA + LDA) | 56 | 75 | 0.75 | 1.75 | 50 | 30 | 43 | 9 | 7 |
EO | 28 Raw Features | 58 | 69 | 0.85 | 1.33 | 39 | 50 | 43 | 11 | 5 |
10 (GA + LDA) | 88 | 27 | 3.24 | 0.16 | 83 | 80 | 82 | 2 | 3 |
Theta | EC | 28 Raw Features | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
18 (GA + LDA) | 79 | 50 | 1.57 | 0.43 | 61 | 70 | 64 | 7 | 3 |
EO | 28 Raw Features | 69 | 58 | 1.18 | 0.75 | 61 | 50 | 57 | 7 | 5 |
10 (GA + LDA) | 75 | 50 | 1.5 | 0.5 | 67 | 60 | 64 | 6 | 4 |
Cz reference—bands analyzed individually
The results of analyzing each band separately during the EC and EO conditions using a Cz reference are presented in Table
3. Although sensitivity, PPV and NPV rates for some of the classifiers reached 100 %, with the exception of the alpha (8–10.5 Hz) band, accuracy rates associated with delta, theta and beta were relatively low (43–64 % over EO/EC conditions). Similarly modest accuracy and sensitivity rates (64 %) were observed with EC and EO alpha band analysis using all candidate features. However, GA and LDA feature extraction processes increased the EC alpha classifier’s sensitivity, PPV, NPV and accuracy rates to 94, 83, 90 and 86 %, respectively. Scalp recordings contributing to these classifications were spread diffusely over frontal, central and parieto-occipital regions (“Additional file
1: Table S2”).
Table 3
Results- analysis of the individual bands- Cz reference
Alpha 8–10.5 Hz | EC | 28 Raw Features | 65 | 50 | 1.31 | 0.69 | 94 | 10 | 64 | 1 | 9 |
10 (GA + LDA) | 94 | 25 | 3.75 | 0.08 | 83 | 90 | 86 | 3 | 1 |
EO | 28 Raw Features | 65 | 50 | 1.31 | 0.69 | 94 | 10 | 64 | 1 | 9 |
15 (GA + LDA) | 73 | 54 | 1.36 | 0.58 | 61 | 60 | 61 | 7 | 4 |
Alpha 10.5–13 Hz | EC | 28 Raw Features | 67 | 63 | 1.07 | 0.89 | 44 | 60 | 50 | 10 | 4 |
4 (GA + LDA) | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
EO | 28 Raw Features | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
9 (GA + LDA) | 74 | 44 | 1.66 | 0.47 | 78 | 50 | 68 | 4 | 5 |
Alpha 8–13 Hz | EC | 28 Raw Features | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
7 (GA + LDA) | 100 | 57 | 1.77 | 0 | 28 | 100 | 54 | 13 | 0 |
EO | 28 Raw Features | 64 | Inf | Inf | Inf | 100 | 0 | 64 | 0 | 10 |
9 (GA + LDA) | 100 | 52 | 1.91 | 0 | 39 | 100 | 61 | 11 | 0 |
Beta 8–10.5 Hz | EC | 28 Raw Features | 58 | 69 | 0.85 | 1.33 | 39 | 50 | 43 | 10 | 5 |
8 (GA + LDA) | 62 | 67 | 0.92 | 1.15 | 44 | 50 | 46 | 11 | 5 |
EO | 28 Raw Features | 58 | 69 | 0.85 | 1.33 | 39 | 50 | 43 | 10 | 5 |
9 (GA + LDA) | 75 | 63 | 1.2 | 0.67 | 17 | 90 | 43 | 15 | 1 |
Beta 10.5–13 Hz | EC | 28 Raw Features | 59 | 73 | 0.81 | 1.51 | 56 | 30 | 46 | 8 | 7 |
8 (GA + LDA) | 100 | 44 | 2.25 | 0 | 56 | 100 | 71 | 0 | 8 |
EO | 28 Raw Features | 62 | 100 | 0.62 | Inf | 89 | 0 | 57 | 2 | 10 |
11 (GA + LDA) | 74 | 44 | 1.68 | 0.46 | 78 | 50 | 68 | 4 | 5 |
Beta 8–13 Hz | EC | 28 Raw Features | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
10 (GA + LDA) | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
EO | 28 Raw Features | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
10 (GA + LDA) | 83 | 70 | 2.77 | 0.24 | 83 | 70 | 79 | 3 | 2 |
Delta | EC | 28 Raw Features | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
9 (GA + LDA) | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
EO | 28 Raw Features | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
10 (GA + LDA) | 80 | 61 | 1.31 | 0.51 | 90 | 22 | 46 | 14 | 1 |
Theta | EC | 28 Raw Features | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
9 (GA + LDA) | 100 | 57 | 1.77 | 0 | 28 | 100 | 54 | 13 | 0 |
EO | 28 Raw Features | 77 | 53 | 1.44 | 0.49 | 56 | 70 | 61 | 8 | 3 |
12 (GA + LDA) | 64 | 0 | 0.64 | Inf | 100 | 0 | 64 | 0 | 10 |
Mastoid reference—bands analyzed together
Table
4 presents the results of analyzing all of the bands together using either the total (8–13 Hz), or by separating the low (8–10.5 Hz) or high (10.5–13 Hz) alpha band data during EO and EC conditions. Overall, accuracy rates relying on all candidate features were relatively low (56–64 %) but the classification accuracy of MDD patients and HVs significantly increased after feature selection with GA and LDA, regardless of whether alpha total (85–86 %), low (88–89 %) or high alpha band (80–86 %) features were used in the modeling. Of these, the best results were obtained when reduced EC low alpha features were used for classification yielding an accuracy rate approaching 90 %, high sensitivity and specificity rates (89 %), and LR+ (8.09) and LR- (0.12) values that strongly support this model for ruling-in and ruling-out depression. While the “Additional file
1: Tables S3-S5” show that multiple recording regions contributed to these classifiers, left and right parietal and occipital recording sites (where alpha power is typically maximal) did not contribute to these results.
Table 4
Mastoid reference results (For around 28 records)
Alpha (8–10.5 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 69 | 43 | 1.21 | 0.72 | 53 | 60 | 56 | 8 | 4 |
60 GA + LDA | 89 | 89 | 8.09 | 0.12 | 94 | 80 | 89 | 1 | 2 |
EO | 112 Raw Features | 68 | 50 | 1.36 | 0.64 | 83 | 30 | 64 | 3 | 7 |
58 GA + LDA | 94 | 75 | 3.76 | 0.08 | 83 | 90 | 88 | 3 | 1 |
Alpha (10.5–13 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 73 | 78 | 3.32 | 0.35 | 80 | 70 | 75 | 2 | 3 |
46 GA + LDA | 77 | 100 | Inf | 0.23 | 100 | 70 | 86 | 0 | 3 |
EO | 112 Raw Features | 62 | 71 | 2.14 | 0.54 | 80 | 50 | 65 | 2 | 5 |
42 GA + LDA | 80 | 80 | 4 | 0.24 | 80 | 80 | 80 | 2 | 2 |
Alpha (8–13 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 62 | 33 | 0.93 | 1.15 | 76 | 20 | 56 | 4 | 8 |
60 GA + LDA | 88 | 80 | 4.4 | 0.15 | 88 | 80 | 85 | 2 | 2 |
EO | 112 Raw Features | 67 | 40 | 1.12 | 0.83 | 67 | 40 | 57 | 6 | 6 |
58 GA + LDA | 94 | 75 | 3.76 | 0.08 | 83 | 90 | 86 | 3 | 1 |
Cz reference—bands analyzed together
The results of the analysis of all the bands together using either the total (8–13 Hz), or by separating the low (8–10.5 Hz) and high (10.5–13 Hz) alpha band data during EO and EC conditions are presented in Table
5.
Table 5
Cz reference results
Alpha (8–10.5 Hz), Beta (8–10.5 Hz), Delta & Theta |
EC | 109 Raw Features | 55 | 28 | 0.76 | 1.61 | 32 | 50 | 38 | 13 | 5 |
59 GA + LDA | 100 | 77 | 4.35 | 0 | 84 | 100 | 90 | 3 | 0 |
EO | 109 Raw Features | 55 | 72 | 1.96 | 0.63 | 32 | 50 | 34 | 13 | 5 |
55 GA + LDA | 85 | 78 | 3.86 | 0.19 | 89 | 70 | 83 | 2 | 3 |
Alpha (10.5–13 Hz), Beta (10.5–13 Hz), Delta & Theta |
EC | 109 Raw Features | 88 | 53 | 1.87 | 0.23 | 47 | 90 | 64 | 8 | 1 |
50 GA + LDA | 100 | 77 | 4.35 | 0 | 80 | 100 | 88 | 3 | 0 |
EO | 109 Raw Features | 88 | 53 | 1.87 | 0.23 | 47 | 90 | 64 | 8 | 1 |
57 GA + LDA | 90 | 60 | 2.25 | 0.16 | 60 | 90 | 72 | 6 | 1 |
Alpha (8–13 Hz), Beta (8–13 Hz), Delta & Theta |
EC | 109 Raw Features | 47 | 14 | 0.55 | 3.79 | 37 | 20 | 31 | 12 | 8 |
59 GA + LDA | 94 | 75 | 3.76 | 0.08 | 84 | 90 | 86 | 3 | 1 |
EO | 109 Raw Features | 75 | 38 | 1.21 | 0.66 | 32 | 80 | 48 | 13 | 2 |
64 GA + LDA | 100 | 66 | 2.94 | 0 | 74 | 100 | 83 | 5 | 0 |
As shown with mastoid-referenced analyses, analyzing all bands together using total raw features of Cz referenced EEG yielded low diagnostic accuracy rates between 31–64 % across EO and EC behavioural states. Similarly, feature extraction with GA and LDA elevated accuracies with total alpha (EC/EO: 86, 83 %), low alpha (EC/EO: 90, 83 %), and high alpha (EC: 88 %) analyses. Further, the more robust classifications were seen in the analysis conducted with reduced low alpha features under the EC condition which also resulted in sensitivity and NPV rates of 100 %, along with moderate rates of specificity (77 %), LR+ (4.35) and LR- (0). The scalp regions contributing to the low alpha classifier were relatively widespread (Additional file
1: Tables S3-S5”).
Model evaluation
Tables
6 and
7 present the results when newly recorded (unseen) EEG recordings are analyzed using all the bands using mastoid or Cz references, and with low, high or total alpha band features.
Table 6
Mastoid reference results on the new unseen 44 records
Alpha (8–10.5 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 56 | 51 | 1.1 | 0.8 | 56 | 51 | 52 | 4 | 17 |
60 GA + LDA | 78 | 74 | 3.02 | 0.2 | 78 | 74 | 75 | 2 | 9 |
EO | 112 Raw Features | 22 | 71 | 0.7 | 1.08 | 22 | 71 | 61 | 7 | 10 |
58 GA + LDA | 78 | 80 | 3.8 | 0.2 | 78 | 80 | 80 | 2 | 7 |
Alpha (10.5–13 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 56 | 60 | 1.3 | 0.7 | 56 | 60 | 59 | 4 | 14 |
46 GA + LDA | 78 | 69 | 2.4 | 0.3 | 78 | 69 | 70 | 2 | 11 |
EO | 112 Raw Features | 44 | 66 | 1.2 | 0.8 | 44 | 66 | 61 | 5 | 12 |
42 GA + LDA | 67 | 74 | 2.5 | 0.4 | 67 | 74 | 73 | 3 | 9 |
Alpha (8–13 Hz), Beta, Delta & Theta |
EC | 112 Raw Features | 33 | 57 | 0.7 | 1.1 | 33 | 57 | 52 | 6 | 15 |
60 GA + LDA | 78 | 77 | 3.4 | 0.2 | 78 | 77 | 77 | 2 | 8 |
EO | 112 Raw Features | 56 | 43 | 0.9 | 1.03 | 56 | 43 | 45 | 4 | 20 |
58 GA + LDA | 67 | 74 | 2.5 | 0.4 | 67 | 74 | 73 | 3 | 9 |
Table 7
Cz Reference Results on the new unseen 44 records
Alpha (8–10.5 Hz), Beta (8–10.5 Hz), Delta & Theta |
EC | 109 Raw Features | 56 | 43 | 0.9 | 1.03 | 56 | 43 | 45 | 4 | 20 |
59 GA + LDA | 67 | 80 | 3.3 | 0.4 | 67 | 80 | 77 | 3 | 7 |
EO | 109 Raw Features | 11 | 54 | 0.2 | 1.6 | 11 | 54 | 45 | 8 | 16 |
55 GA + LDA | 67 | 77 | 2.9 | 0.4 | 67 | 77 | 75 | 3 | 8 |
Alpha (10.5–13 Hz), Beta (10.5–13 Hz), Delta & Theta |
EC | 109 Raw Features | 33 | 66 | 0.9 | 1.01 | 33 | 66 | 59 | 6 | 12 |
50 GA + LDA | 78 | 77 | 3.4 | 0.2 | 78 | 77 | 77 | 2 | 8 |
EO | 109 Raw Features | 56 | 60 | 1.3 | 0.7 | 56 | 60 | 59 | 4 | 14 |
57 GA + LDA | 67 | 74 | 2.5 | 0.4 | 67 | 74 | 73 | 3 | 9 |
Alpha (8–13 Hz), Beta (8–13 Hz), Delta & Theta |
EC | 109 Raw Features | 33 | 57 | 0.7 | 1.1 | 33 | 57 | 52 | 6 | 15 |
59 GA + LDA | 56 | 80 | 2.7 | 0.5 | 56 | 80 | 75 | 4 | 7 |
EO | 109 Raw Features | 22 | 54 | 0.4 | 1.4 | 22 | 54 | 48 | 7 | 16 |
64 GA + LDA | 67 | 80 | 3.3 | 0.4 | 67 | 80 | 77 | 3 | 7 |
Although accuracy was frequently below 60 % for mastoid and Cz referenced datasets using all candidate features, classification rates improved to > 70 % using the reduced features derived with GA and LDA. Accuracy reached 75–77 % levels with EC and EO Cz referenced EEG, but the maximal 80 % accuracy was evidenced with EO low alpha analysis, which also yielded sensitivity and specificity rates of 77 % and 80 %, respectively, together with relatively high PPV (78 %) and NPV (80 %) values.
As indicated in Tables
6 and
7, the model shows an acceptable performance on the newly recorded data. The accuracy of the model fluctuates between 70 % and 80 %. For example, based on the Cz reference, EC dataset with all bands (Table
7), the accuracy of the model on 106 raw features is about 60 %, while by applying the proposed method, the accuracy reaches 72.7 % which is a noticeable improvement.
Discussion
The heterogeneity of symptom profiles and severity among patients with MDD is a major challenge for diagnostic classification. Further, given the reliability problems associated with subjective assessments of clinical phenomena there is an increasing effort to identify more brain-based, objective and reliable classifiers for various psychiatric disorders, including depression. In this paper, classification approaches were performed on EEG signal features (power density in different frequency bands) derived from multiple scalp recording sites, during two states (EO/EC) and analyzed using two reference montages. In Experiment 1, individual bands resulted in relatively high classification errors, regardless of whether or not the complexity and redundancy of signal features was reduced by the genetic algorithm. Exceptions were observed with EO mastoid-referenced delta and EC Cz-referenced total alpha, with the reduced extracted features of each band exhibiting > 80 % accuracy, sensitivity and PPVs. These latter findings are generally supportive of previous group-level comparison studies showing activity of alpha, and to a lesser extent delta oscillations to distinguish depressed and healthy volunteer samples [
26].
When analyzing each band separately, classification was similarly less than optimal in Experiment 2 when bands were analyzed together and with the total set of candidate features, but was markedly increased following feature reduction. Regardless of the type of reference (mastoid vs. Cz) or vigilance state (EC vs. EO), most models exhibited low classification errors, with high accuracy and sensitivity values. The electrode sites contributing to these classifiers and the above-mentioned single band delta and alpha classifiers were widely distributed across frontal, temporal and posterior regions in both hemispheres. These data are consistent with functional neuroimaging studies that tend to characterize depression as a dysfunction in a network(s) of discrete, but functionally integrated, cortico-limbic pathways [
53], which can be assessed by brain-based algorithms for diagnosis and optimized treatment [
54,
56].
In summary, the most accurate decision tree models (accuracies > 80 %) were evaluated with unseen data from 44 participants, including 35 HVs and 9 MDD patients. Correct diagnosis rates of the models were found to be quite accurate. These results generally support the notion that data mining techniques, and especially those involving feature extraction, may yield promising classifiers for the EEG signal processing applications, specifically in cases of MDD and control subjects classification. Improved classification accuracies may possibly be achieved with the addition of other candidate features besides EEG power including EEG coherence and cordance measures, which have been reported to distinguish depressed patients from healthy volunteers, and the EEG antidepressant response (ATR) index, which has predicted treatment response in depressed patients [
26,
27].
Competing interests
None of the authors have any financial or non-financial competing interests related to this paper.
Authors’ contributions
VK was one of the authors responsible for developing the research questions and overseeing the project, and helped to draft the manuscript, particularly the Introduction, Methods (Sections
Project participants,
EEG acquisition and
EEG processing), and Discussion. BR was also responsible for developing and overseeing the project, directing, overseeing and interpreting the data mining results and helped to interpret the data and edit the manuscript. PB, the psychiatrist on the project, screening, interviewed and diagnosed all patients and helped to edit the paper. GR was involved in the conception of the project, coordinated the communications/interactions between investigators and submitting the project for funding and was involved in editing the manuscript. MM and FA were responsible for performing the data mining procedures, writing results sections and editing the manuscript. NJ recruited and tested the patients and controls and processed all the EEG files and assisted in editing the manuscript. DS and SD prepared and assembled EEG and clinical data files and assisted in editing the manuscript. All authors have given approval for this manuscript to be published.