Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine,
Depression is a common mood disorder, which might cause persistent feeling of sadness, loss of interest, and impairment of memory and concentration. Depressed patients normally experience cognitive impairment and suffer long and severe emotional depression. In severe cases, some patients will experience paranoia and illusion [
Presently, the study on the human cerebral is currently under intensive focus in order to understand the mechanism underlying persistent negative emotion and depression. Therefore, the most commonly used diagnosis of depression is a scale-based interview conducted by a psychologist or psychiatrist. The current international standard mostly used is “In Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition)” (DSM-IV) [
The current methods of depression detection are human-intensive, and the results are dependent on the doctor’s experience. Furthermore, depressed individuals are less likely to seek help due to fear of stigma and the nature of the disorder. As a result, a large number of depressed patients, not diagnosed accurately, do not receive optimal treatment and adequate recovery period. Therefore, finding convenient and effective methods for the detection of depression is an emerging topic for research. With the latest advances in the sensor and mobile technology, the exploration using physiological data for the diagnosis of mental disorder opens a new avenue for an objective and accurate tool for depression detection. Among all kinds of physiological data, electroencephalogram (EEG) reflects emotional human brain activity in real time [
The EEG signal is a recording of the spontaneous, rhythmic, electrical activity of brain neurons from the scalp surface. Since the earliest discovery from the rabbit and monkey brain and the first recording of the human EEG signal by German psychiatrist Hans Berger in 1926, studies on the analytical method of EEG and the interpretation of the association between the brain function and mental disorders have been continued for over a century [
The studies on EEG could be used to understand the mechanism underlying brain activity, human cognitive process, and diagnosis of brain disease, as well as the field of the Brain Computer Interface (BCI), which has attracted much attention in recent years [
The frequency of the EEG signal can be divided into 5 wave-bands: delta wave (<4 Hz), which normally appears in an adult’s slow-wave sleep; theta wave (4–8 Hz), which is usually found when someone is sleepy; alpha wave (8–14 Hz), which is normally detected when someone is relaxed; beta wave (14–30 Hz), which commonly appears when someone is actively thinking; and gamma wave (30–50 Hz), which could appear during meditation. The EEG signals undergo changes in the amplitude as well as frequency, while different mental tasks are performed [
Presently, for research purposes, the most commonly used are 128-electrode and 256-electrode EEG systems [
In the present study, the pervasive three-electrode EEG acquisition system, developed independently by the Ubiquitous Awareness and Intelligent Solutions Lab (UAIS) of Lanzhou University [ A pervasive three-electrode EEG acquisition system has been introduced (Section A psychophysiological experiment has been conducted, in which EEG of 213 participants has been recorded. These physiological data provided a comprehensive database for further analysis, construction, and evaluation of a pervasive EEG-based depression detection system (Sections Several EEG preprocessing steps and methods were applied on the raw EEG data (Section 270 features were identified and extracted from the recoded database. By employing a feature selection technique, an optimum feature matrix was constructed for the depression classification process (Section Four classification algorithms, including
The 10-20 system, proposed by Jasper in 1958, defined the name of the electrode and later became the international standard EEG placement system [
As shown in Figure
The international 10-20 system.
Prefrontal cortex is the center of consciousness; thus, the better the control of the forehead cortex, the better the emotional control. Jasper studied the resting-state EEG of severe depression patients showing that when the body suffered from severe depression, the activity of the cerebral cortex was altered [
Pervasive three-electrode EEG acquisition system using Fp1, Fp2, and Fpz positions.
Compared to the normal controls, depressed patients responded differently to outside stimulus [
Therefore, recording and analysis of the EEG signal in different stimuli may help in the identification of patients with depression. This study was designed to record the participants’ EEG in four different cases: in resting state, under negative stimulus, under neutral stimulus, and under positive stimulus. The source of stimulus is soundtracks from the International Affective Digitized Sounds (IADS-2) [
The experiment was performed in a quiet room. Firstly, the experiment objective and procedures were described to the participants. Then, the pervasive three-electrode EEG acquisition system was placed on the participants’ foreheads and checked for reception. After one minute of relaxation, the experiment begins again. At first stage, 90 s of resting-state EEG was recorded. Then, the participants were asked to remain seated with eyes closed with as little body movements as possible, followed by another minute of rest. In the second stage, stimulation soundtracks will be played to participants. Each soundtrack was 6 s long with a 6 s break between each soundtrack. The process would continue until the experiment is completed. The process of EEG acquisition is shown in Figure
Process of EEG acquisition.
A total of 6 stimulation soundtracks (according to IADS-2) existed, including 2 neutral stimulation soundtracks, 2 negative stimulation soundtracks, and 2 positive stimulation soundtracks. Table
Audio stimulation profile.
Number | Name | Property |
---|---|---|
(1) | Cattle | Neutral |
(2) | Painting | Neutral |
(3) | Babies cry | Negative |
(4) | Dentist drill | Negative |
(5) | Baby | Positive |
(6) | Crowd | Positive |
Of the total 250 participants, 213 (92 depressed patients and 121 normal controls) completed the experiment, successfully. The raw EEG data from all the electrodes were recorded. Depressed participants were selected by professional psychiatrists using MMSE [ The Patient Health Questionnaire (PHQ-9) [ Life Event Scale (LES) [ Pittsburgh Sleep Quality Index (PSQI) [ Generalized Anxiety Disorder Scale-7 (GAD-7) [
In this study, all preprocessing, and data analyses have been implemented using MATLAB software (version R2014a).
EEG is a noninvasive method of capturing the physiological signal of brainwave activity. However, EEG data recorded are normally mixed with interferences from surrounding environment, such as close-by power line. Furthermore, other physiological signals, including electrocardiogram (ECG), electrooculogram (EOG), and electromyograph (EMG), could also be detected and recorded by EEG sensors [
ECG is a smooth signal among the physiological electrical signals, with a large amplitude. As the heart is located distally from the head, the ECG signal will be greatly attenuated when spread to the scalp. EMG is produced by muscle contraction, with an amplitude of 10
No overlap occurred between the frequency of EEG signal and power-line interferences, EMG and ECG; thus, Finite Impulse Response (FIR) filter based on the Blackman time window was used to remove these interference signals. The adequate linearity of the FIR filter is widely used in modern electronic communication. It can guarantee any amplitude frequency characteristics simultaneously, with strict linear phase-frequency characteristics. In addition, the unit sampling response is finite, which stabilized the filter. In order to reduce the energy leakage of the spectrum, the signal can be truncated by different interception functions. This truncation function is known as the window function. The time domain representation of the Blackman time window is
The resulting EEG signal is retained only between frequencies in the range of 0.5–50 Hz. However, the frequency of EOG overlaps within this range. Although all participants were asked to remain seated with eyes closed, their EOG was recorded inevitably while using the prefrontal-lobe EEG sites, such as Fp1, Fp2, and Fpz. A general model for EOG contamination can be described by
Kalman filter is an optimal recursive data processing algorithm, which has been widely utilized in several applications, such as industrial control systems, radar target tracking, communications and signal processing, aeroengine diagnosis, and intelligent robots. Kalman filter is based on the previous estimated value and the observed value of the current time to estimate the current value of the stated variable. Thus, the frequency of the EOG artifact would not exceed 15 Hz, and the approximate EOG signal and the amplitude of the brain in the low frequency band are small. As a result, the Kalman derivation formula combines the Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF) to estimate the pure EOG artifact.
The denoising model proposed in the present study involves the following steps: (1) signal decomposition, (2) ocular artifacts (OA) zones detection, (3) signal prediction, and (4) signal reconstruction. Herein, DWT was used to decompose the EEG signals and detect the OZ zones. The frequency range of the EEG signal was 0–64 Hz, while the OA occurred in 0–16 Hz. The multiscale DWT decomposition was used to extract the low frequency components and nonstationary time series, which were then divided into several approximate stationary time series. Thus, the conventional forecasting methods, such as Kalman filter, can predict the shape of the true wave of decomposition signals accurately. Subsequently, the Adaptive Auto Regressive (AAR) models and an Adaptive Predictor Filter (APF) were applied to improve the prediction. The APF uses an adaptive filter to estimate the future values of signals based on their past values. Finally, the EOG artifacts were removed from the raw EEG signal, and the data were ready for further processing.
The features matrix consists of
(1) Identify and extract all the efficient features for each set of EEG data, such that each row represents a feature vector.
(2) Each row of the features matrix is selected by feature selection; that is, the most suitable feature is selected from all the extracted features to form a final feature vector.
(3) Each row of the feature vectors is tagged by depression or nondepression.
The EEG signal presents weak, nonlinear, and time-sensitive characteristic, which exhibits typically complex dynamics. The feature of EEG will change with the emotional state transformation. The analysis of EEG data displayed different linear features such as peak, variance, and skewness that were used in recent literature [
The The fast Fourier transform (FFT) of the signal is as follows: The mean amplitude of the power spectrum The inverse FFT (IFFT) of The power of stochastic part Kolmogorov Entropy was used to measure the rate of loss of information per unit of time. Positive and finite entropy represents that the time series and the dynamic underlying phenomenon are chaotic. Zero entropy indicates a regular phenomenon in the space phase. Infinite entropy refers to a stochastic and nondeterministic phenomenon. Kolmogorov Entropy is defined as the average rate of loss of information as follows: Shannon Entropy was introduced by Shannon in 1948 in an article entitled “A Mathematical Theory of Communication” [ where Correlation Dimension indicates the dynamic features of the EEG signal. The greater the Correlation Dimension number, the complicated the EEG time series. The Correlation Dimension is a fractal dimension, often computed from the time series illustration. It is a simplified phase space diagram constructed from a single data vector. The fundamental Correlation Dimension algorithm was introduced by Grassberger and Procacia in 1983 [ where Power-Spectral Entropy is a sequence of power density with the frequency distribution obtained by Fourier transform. The calculated entropy of the power spectrum (referred to as Power-Spectral Entropy) can be implemented easily. The Power-Spectral Entropy is used to analyze the timing signals in EEG data. The entropy can be used as a physical indicator to estimate the quality and intensity of brain activity. The larger the entropy, the more active the brain.
All linear and nonlinear features (Table
Features used in the feature extraction process.
Name | Property |
---|---|
Centroid frequency | Linear features |
Relative centroid frequency | |
Absolute centroid frequency | |
Relative power | |
Absolute power | |
Peak | |
Variance | |
Skewness | |
Kurtosis | |
Hjorth | |
|
|
Power-Spectrum Entropy | Nonlinear features |
Shannon Entropy | |
Correlation Dimension | |
C0-complexity | |
Kolmogorov Entropy |
Feature Selection is used to select a relevant subset of all available features, which not only yields a small dimensionality of the classification problem but also reduces the noise (irrelevant features). We further deduced the types of features suitable for suppressing the EEG signal recognition by inspecting the features selected by the applied algorithm.
The feature evaluation function focuses on the relation between the features and the target class, which tends to involve redundant features, influencing the learning accuracy and results. In order to achieve these results, we applied the minimal-redundancy-maximal-relevance (MRMR) technique to perform the feature selection. The MRMR feature selection criterion was proposed by Peng et al. [
Feature redundancy is defined based on the pairwise feature dependence. If two relevant features highly depend on each other, the class-discrimination power would not change dramatically if one of the features was removed. Min-redundancy,
Each feature vector (each row of the feature matrix) has to be marked with a specific emotional tag. In this study, we divided the experimental population into two categories: depressed patients and normal controls. All eigenvectors are tagged as depressed and nondepressed.
SVM, KNN, and CT are the widely used classification algorithms in the majority of the EEG-related studies. In the present study, we evaluated the performance of these classifiers (SVM, KNN, and CT) plus the Artificial Neural Network (ANN) classifier in the depression detection process. All classifications and 10-fold cross-validations have been implemented using the MATLAB software (version R2014a).
SVM, proposed by Cortes and Vapnik [
KNN algorithm is a nonparametric supervised machine learning method for classification and regression. It was introduced by Dasarathy [
CT, also known as decision tree, is a tree structure-based supervised classification model [
ANN is a classification method that mimics the structure and function of the biological neural network and consists of an information processing network with wide parallel interconnection of simple units. This network exhibits learning and memory ability, knowledge generalization, and input information feature extraction ability similar to that of the human brain [
10-fold cross-validation results of the most optimal performance feature combination sets of each classifier and their accuracy in the detection of depression are shown below: results of resting-state data, neutral audio stimulation data, positive audio stimulation data, and negative audio stimulation data are summarized Tables
Classification results in resting state data.
Classifier | Feature sets | Accuracy |
---|---|---|
SVM | Absolute power of theta wave (Fp1), relative power of theta wave (Fp1), relative power of alpha wave (Fp1), absolute center frequency of gamma wave (Fp1) | 72.56% |
KNN | Absolute power of gamma wave (Fp1), absolute power of theta wave (Fp2), absolute power of beta wave (Fp2), absolute center frequency of beta wave (Fp2) | 76.83% |
CT | Peak (Fp1), Power-Spectral Entropy of full band EEG (Fp1), absolute power of beta wave (Fp2) | 68.29% |
ANN | Absolute center frequency of beta wave (Fp1), absolute power of gamma wave (Fp1), Kurtosis of full band EEG (Fp1), absolute power of alpha wave (Fp2), relative center frequency of beta wave (Fp2) | 72.56% |
Classification results in neutral audio stimulation data.
Classifier | Feature sets | Accuracy |
---|---|---|
SVM | Absolute center frequency of theta wave (Fp1), center frequency of full band EEG (Fp2), peak of full band EEG (Fp2) | 70.12% |
KNN | Absolute power of theta (Fp1), center frequency of full band EEG (Fp2), peak of full band EEG (Fp2) | 74.39% |
CT | Absolute center frequency of alpha wave (Fp1), Power-Spectral Entropy of alpha wave (Fp1) | 67.70% |
ANN | Relative center frequency of theta wave (Fp2), Hjorth of full band EEG (Fp2) | 73.78% |
Classification results in positive audio stimulation data.
Classifier | Feature sets | Accuracy |
---|---|---|
SVM | Absolute power of theta wave (Fp1), Kurtosis of full band EEG (Fp1) | 68.29% |
|
||
KNN | Absolute power of theta wave (Fp1), absolute power of beta wave (Fp1) | 79.27% |
|
||
CT | Absolute power of gamma wave (Fp1), absolute power of gamma wave (Fp2) | 60.37% |
|
||
ANN | Absolute power of theta wave (Fp1), power spectral entropy of gamma wave (Fp1), |
74.39% |
Classification results in negative audio stimulation data.
Classifier | Feature sets | Accuracy |
---|---|---|
SVM | Hjorth of full band EEG (Fp2), correlation dimension of full band EEG (Fp2) | 67.07% |
KNN | Absolute power of theta wave (Fp1), correlation dimension of full band EEG (Fp1), absolute center frequency of theta wave (Fp2), absolute power of gamma wave (Fp2) | 77.44% |
CT | Absolute power of theta wave (Fp1), power spectral entropy of full band EEG (Fp1), relative power of beta wave (Fp2), peak of full band EEG (Fp2) | 71.34% |
ANN | Absolute power of theta wave (Fp1), absolute power of beta wave (Fp1), center frequency of full band EEG (Fp1) | 71.34% |
For resting-state EEG data, KNN achieved the best accuracy of 76.83% using feature combination of absolute power of gamma wave on Fp1, absolute power of theta wave on Fp2, absolute power of beta wave on Fp2, and absolute center frequency of beta wave on Fp2 (Table
For EEG data of participants under neutral audio stimulation, KNN achieved the best accuracy of 74.39% using the feature combination of absolute power of theta on Fp1, center frequency of full-band EEG on Fp2, and peak of full-band EEG on Fp2 (Table
For EEG data of participants under positive audio stimulation, KNN achieved the best accuracy of 79.27% using the feature combination of absolute power of theta wave on Fp1 and absolute power of beta wave on Fp1 (Table
For EEG data of participants under negative audio stimulation, KNN achieved the best accuracy of 77.44% using feature combination of absolute power of theta wave on Fp1, correlation dimension of full-band EEG on Fp1, absolute center frequency of theta wave on Fp2, and absolute power of gamma wave on Fp2 (Table
The results showed that, among all the four classifiers of SVM, KNN, CT, and ANN, KNN performed the best with an average classification accuracy of 76.98% (Figure
Average accuracy of classification on selected features.
Depression is a major health concern in millions of individuals. Thus, diagnosing depression in the early curable stages is critical for the treatment in order to save the life of a patient. However, current methods of depression detection are human-intensive, and their results are dependent on the experience of the doctor. Therefore, a pervasive and objective method of diagnosing or even screening would be useful. The present study explores a novel method of depression detection using pervasive prefrontal-lobe three-electrode EEG system, which chooses Fp1, Fp2, and Fpz for electrode sites, according to the international 10-20 system.
Several widely employed psychological scales were used to select the optimal experimental candidates, which encompassed 213 participants (92 depressed patients and 121 normal controls). Their EEG data of resting state, as well as under sound stimulation, were recorded. The soundtracks were selected from the IADS-2 database, comprising positive, neutral, and negative stimuli.
The FIR filter combining the Kalman derivation formula, DWT, and an APF were applied on the raw EEG data to remove the interference from environment, ECG, EMG, and EOG. Subsequently, 270 linear and nonlinear features were extracted from the preprocessed EEG. Then, the MRMR technique was applied to perform the feature selection. Four classification algorithms, KNN, SVM, CT, and ANN, have been evaluated and compared, using a 10-fold cross-validation. The results exhibited KNN as the best performance classification method in all datasets, with the highest accuracy of 79.27%. The results also demonstrated the feature “absolute power of theta wave” in all the best performance features of the four datasets, thereby suggesting a robust connection between the power of theta wave and depression; this could be used as a valid characteristic feature in the detection of depression.
The current study postulated that a novel and pervasive system for screening depression is feasible. With a carefully designed model, the pervasive system could reach accuracy similar to the current scale-based screening method; for instance, the accuracy of BDI is reported to be 79–86% in different studies [
EEG and depression have been under intensive focus of research. In comparison to the study by Knott et al., who collected EEG recordings from 21 scalp sites and conducted univariate analyses for group comparisons and correctly classified 91.3% of the patients and controls [
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported by the National Basic Research Program of China (973 Program) (no. 2014CB744600), the National Natural Science Foundation of China (Grants nos. 61210010 and 61632014), and the Program of International S&T Cooperation of MOST (no. 2013DFA11140).