Comparison of Machine Learning Methods in Classification of Affective Disorders

I. Kinder*, K. Friganovic*, J. Vukojevic**, D. Mulc**, T. Slukan**, D. Vidovic**, P. Brecic** and M. Cifrek* * University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia

** University Psychiatric Hospital Vrapče, University of Zagreb, Zagreb, Croatia ivana.kinder@fer.hr; kresimir.friganovic@fer.hr

Abstract - Depression belongs to a group of psychiatric disorders called affective disorders. In medical practice, patients are diagnosed according to the criteria in standardized diagnostic manuals. The criteria for diagnosing such disorders focus on the symptoms presented by the patient as well as on disqualifying other potential causes of the symptoms. Electroencephalography (EEG) is a non-invasive brain imaging technique that measures the electrical activity of the brain across different sites on the surface of the scalp. In this paper, 15 EEGs of depression patients and 15 EEGs of healthy control subjects are observed. The depressed and healthy subjects are paired according to age and gender to achieve a dataset that is balanced across classes, gender, and age of subjects. 475 different features are extracted from each EEG and used in the evaluation of different binary classification methods. The best F1-score of 0.7586 is achieved with the K-nearest neighbor algorithm. Sequential feature selection is performed, and sequentially selected features are used to evaluate the former binary classification methods. The best F1-score of 0.8750 is achieved with the K-nearest neighbor algorithm. Classification results are compared across different methods, as well as before and after excluding features that were not deemed significant by the sequential selection algorithm.

Keywords - electroencephalography; affective disorders; depression; feature selection; binary classification

I. INTRODUCTION Affective disorders are psychiatric disorders

characterized by problems in mood regulation [1]. They are diagnosed and treated by psychiatrists. The diagnostic process relies on interviewing the patient in order to determine the symptoms which are present, as well as the intensity of those symptoms. A diagnosis is then determined according to a standardized diagnostic manual, e.g., ICD-10 (International Classification of Diseases – Tenth Edition) or DSM-5 (Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition).

Electroencephalography is a method of measuring brain activity. The prefix of the term regards the nature of the activity, which is electrical, as a result of the functioning of the cells of which the central nervous system is comprised. In the process of diagnosing affective disorders, visual inspection of electroencephalograms (EEGs) is done to rule out brain damage or epileptogenic activity. However, EEGs contain more information, which is not accessible by visual inspection of the signal in the time domain but can be extracted with methods of signal processing and analysis.

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [2]. It has been hypothesized that the characteristics of EEGs could serve as biomarkers of affective disorders such as major depressive disorder (MDD). The identification of biomarkers for psychiatric disorders then serves as a step toward identifying underlying mechanisms of dysfunction, as an alternative to the current descriptive, diagnostic systems [3].

For example, parameters of EEG recorded during sleep have been found to be of discriminative and predictive value in MDD [4]. Another example are the parameters of the loudness dependency auditory evoked potential as predictors of antidepressant treatment outcome [5]. The absolute and relative power of alpha rhythms have been observed to be higher in MDD patients compared to healthy controls [6], whilst comparatively slower alpha rhythms have been linked to the ineffectiveness of treatment with certain antidepressants [7].

The goal of this paper is to process and analyze resting-state EEGs of individuals with depressive disorders as well as healthy control subjects. EEG recordings are first preprocessed to remove artifacts. Then, feature extraction is performed in order to prepare the input for machine learning algorithms, which classify EEGs into one of two classes: depression or healthy. Different binary classification methods are compared according to accuracy and F1 scores. Sequential feature selection is used to identify the most significant features, and cross-validation of different models is repeated using said features.

II. METHODS

A. Data acquisition and description EEG dataset is obtained from the University Psychiatric

Hospital Vrapče, Zagreb. Subjects are patients diagnosed with various affective disorders and healthy controls. The EEGs of 149 affective disorder patients (107 female, 42 male, age 53, +/- 12) were procured from the hospital archive. Patient EEGs are recorded as a part of the standardized admission procedure. EEGs of 15 healthy volunteers (8 female, 7 male, age 31 +/- 7) were recorded using the same protocol. The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the ethics committee of the University of Zagreb, University Psychiatric Hospital Vrapče.

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EEGs were recorded using a 19-channel EEG amplifier, with standard 10-20 electrode placement, as shown on Fig. 1. Non-reference EEG electrodes are Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2, with Oz as the reference electrode. The sampling frequency is 256 Hz.

Figure 1. 10 - 20 electrode montage with Oz as the reference

electrode

The recording protocol consists of three main parts, as visualized in Fig. 2: the first consisting of resting-state EEG with eyes opened followed by eyes closed, the second being EEG during photo-stimulation, and the third being EEG during and after induced hyperventilation. The subject is in a lying down position, with their body positioned to alleviate muscle tension. The room is kept quiet and peaceful. The protocol starts with the subject being asked to keep their eyes open. After 8 to 10 seconds, the subject is asked to keep their eyes closed until told otherwise. After 45 seconds to a minute, the subject is asked to open their eyes. This is repeated 5 to 7 times. Next, photo-stimulation is performed with 5 different flash frequencies (4 Hz, 8 Hz, 16 Hz, 24 Hz, and 30 Hz), lasting 15 seconds each. Lastly, state hyperventilation is induced; 1 to 2 eyes-opened eyes-closed segments are recorded, after which the subject is instructed to breathe steadily for the rest of the recording. During the recording, the technician marks the onset of each recording event: instructions given to subject as well as non-protocol events that cause EEG artifacts, such as coughing, movement, or speaking.

Figure 2. EEG recording protocol

Diagnosing of subjects is carried out by appointed physicians according to the International Classification of Diseases, 10th Edition (ICD-10). Due to the much greater number of subjects diagnosed with affective disorders than

that of healthy controls, 15 subjects with severe depression are selected for the depression class, to be compared to the 15 in the healthy control class. This is done to achieve a balanced dataset for the classification problem at hand. Depression subjects are chosen to individually match each healthy subject by gender, as well as to be of similar age. This results in having the gender-balanced classes, as well as an approximately equal age distribution across classes, as shown in Tab. 1.

TABLE 1. AGE AND GENDER OF SUBJECTS IN DEPRESSION AND HEALTHY CLASSES

Class Female subjects

Male subjects

Mean of age

Standard deviation of

age

Depression 7 8 40 10

Healthy 7 8 39 10

Of the 15 subjects in the depression class, 6 were diagnosed with F32.2, 1 with F32.3, 7 with F33.2 and 7 with F33.3, at the time of the recording of their EEGs, as shown in Tab. 2.

TABLE 2. DIAGNOSIS OF SUBJECTS IN DEPRESSION CLASS

ICD-10 diagnosis Description

Number of

subjects

F32.2 Major depressive disorder, single episode, severe without psychotic features

6

F32.3 Major depressive disorder, single episode, severe with psychotic features

1

F33.2 Major depressive disorder, recurrent severe without psychotic features 7

F33.3 Major depressive disorder, recurrent severe with psychotic features 1

B. Preprocessing Raw EEG data is contaminated with artifacts such as

line noise at 50 Hz and its harmonics as well as its aliased higher harmonics, muscle artifacts, blink artifacts, and artifacts from the shifting of the EEG electrodes across the surface of the scalp.

Therefore first, the raw EEG data is filtered with a passband filter from 0.1 to 40 Hz. The lower frequency edge is chosen to eliminate the slow drifts in the signal [8], while the high-frequency edge is chosen to retain the frequency components of interest, whilst removing the 50 Hz line noise.

After passband filtering, channels are re-referenced to the average of all 19 channels.

Finally, independent component analysis (ICA) is performed using the runica() function implemented in

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eeglab, a framework within MATLAB. Components are then labeled using ICLabel, a pre-trained independent component classifier. Each component is classified according to its proposed signal source, the sources being either brain activity, muscle activity, cardiac activity, blinking, or other, along with the certainty of said classification expressed as a percentage of certainty. The output of ICLabel for one of the EEGs is shown in Fig 3.

Figure 3. Example of ICA component labeling

Labeled independent components are then manually inspected, and their subsets are then subsequently removed to find a minimal subset that removes the prominent artifacts while retaining the components that make up the useful part of the signal.

C. Feature extraction There are five main characteristic brain rhythms that are

recognizable in EEGs. They can be visually recognized by their characteristic amplitudes and frequencies. They occur and dissipate depending on the state of the subject. These states include eyes opened, eyes closed, concentration, relaxation, and sleep, as well as frustration, intellectual strain, and stress [8].

The main characteristic of each brain rhythm is the frequency band it belongs to. From the lowest to the highest frequencies there is the: delta (𝛿) band, theta (θ) band, alpha (α) band, beta (β) band, and gamma (γ) band, as shown in Tab. 3.

TABLE 3. CHARACTERISTIC BRAIN RHYTHMS

Rhythm symbol Rhythm name Frequency band [Hz]

𝛿 delta 0.5 – 4 θ theta 4 – 8 α alpha 8 – 13 β beta 13 – 30 γ gamma 30 - 45

For each of characteristic EEG waves (theta, delta, alpha, beta, and gamma) 5 features were selected and extracted from each of the 19 available EEG channels.

Absolute band power for each characteristic brain rhythm is calculated from power spectral density approximated by expression (1):

𝑃𝑆𝐷 =

1

𝑁|𝐹𝐹𝑇(𝑥𝑐𝑜𝑟𝑟(𝑥(𝑛)))| (1)

Where 𝑥𝑐𝑜𝑟𝑟(𝑥(𝑛)) is the autocorrelation function of the discrete-time EEG signal, 𝐹𝐹𝑇() the Fast Fourier Transform, and 𝑁 the number of data samples. Absolute band power is then equal to the sum of the PSD values within each frequency band, as shown in expression (2):

𝑎𝑏𝑠𝑃𝑜𝑤𝑟𝑦𝑡ℎ𝑚 = ∑ 𝑃𝑆𝐷(𝑓)

𝑓 ∈ 𝑟ℎ𝑦𝑡ℎ𝑚 𝑓𝑟𝑒𝑞.𝑏𝑎𝑛𝑑

(2)

Relative band power for each frequency band of interest is calculated, dividing the absolute power of each frequency band by the total absolute power of the EEG signal, as shown in expression (3):

𝑟𝑒𝑙𝑃𝑜𝑤𝑟𝑦𝑡ℎ𝑚 =

𝑎𝑏𝑠𝑃𝑜𝑤𝑟𝑦𝑡ℎ𝑚

𝑎𝑏𝑠𝑃𝑜𝑤𝑠𝑖𝑔𝑛𝑎𝑙

(3)

Spectral centroids describe the shape of EEG spectra in defined frequency bands. Spectral centroids for each of the frequency bands corresponding to their respective characteristic brain rhythm are calculated using expression (4):

𝑆𝐶𝑟ℎ𝑦𝑡ℎ𝑚 =

∑ 𝑓𝑘𝑠𝑘𝑏2𝑘=𝑏1

∑ 𝑠𝑘𝑏2𝑘=𝑏1

(4)

where 𝑓𝑘 is the frequency in Hz corresponding to frequency bin k of the discrete Fourier transform (DFT) of a discrete signal 𝑥(𝑛), 𝑠𝑘 the spectral value of bin k and b1 and b2 the bins corresponding to the band edges of the characteristic brain rhythms.

The final two features are calculated based on the detail coefficients of signal components obtained by decomposing the signals using the one-dimensional wavelet transform implemented in MATLAB function wavedec(), resulting in details and coefficients corresponding to frequency bands which approximate that of the characteristic brain rhythms, as shown in Tab. 4.

TABLE 4. WAVELET DECOMPOSITION

Coefficients Frequency band [Hz]

d1 6 - 128 d2 32 – 64 d2 16 – 32 d4 8 – 16 d5 4 – 8 d6 2 – 4 d7 1 – 2 d8 0.5 – 1 a8 0 – 0.5 a8 0 – 0.5

Wavelet energy is defined by expression (5):

𝐸𝑛 = ∑|𝑑𝑛,𝑗|2

𝑗

; 𝑛 = 1, … , 𝑁 (5)

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where 𝑑𝑛,𝑗 is the ith detail coefficient at level n of the discrete wavelet decomposition of the signal.

Total wavelet energy is the sum of the wavelet energy across all levels of decomposition and is calculated using expression (6):

𝐸𝑡𝑜𝑡𝑎𝑙 = ∑ 𝐸𝑛 ; 𝑛 = 1, … , 𝑁

𝑁

𝑛=1

(6)

where 𝐸𝑛 is the wavelet energy at decomposition level n [9].

Relative wavelet energy (RWE) is calculated using (7):

𝜌𝑛 =

𝐸𝑛

𝐸𝑡𝑜𝑡𝑎𝑙

; 𝑛 = 1, … , 𝑁 (7)

where 𝐸𝑛 is the wavelet energy at decomposition level n and 𝐸𝑡𝑜𝑡𝑎𝑙 the total wavelet energy across all decomposition levels [9].

Wavelet entropy is calculated using expression (8):

𝑊𝐸𝑛 = − ∑ 𝜌𝑛 𝑙𝑛(𝜌𝑛)

𝑁

𝑛=1

(8)

where 𝜌𝑛is the RWE at decomposition level n [9].

Features are extracted only for the signal samples of the first part of the recording protocol, the resting state with eyes opened and eyes closed. For 19 channels, 5 characteristic brain rhythms, and 5 different feature types, the total number of features per subject amounts to a total of 475 features.

D. Classification methods and feature selection For evaluating different classification methods, 5-fold

cross-validation is done using different classification methods. Cross-validation is performed using all 475 features to rank the various methods according to accuracy and F1-score.

Sequential feature selection was performed using MATLAB function sequetialfs() to identify the most significant subset of features for separating the two classes. Cross-validation is then done again, and classification methods are ranked by accuracy and F1-score.

III. RESULTS 5-fold cross-validation results for binary classification

of classes depression and healthy are shown in Tab. 5. All 475 available features are used. The support vector machine model shows the highest classification accuracy, followed by the K-nearest neighbor algorithm.

Sequential feature selection results in features Fp1_RelPow_Beta (relative power of beta rhythm on channel location Fp1), Fp2_SpecCent_Alpha (spectral centroid of alpha rhythm on channel location Fp2), Cz_SpecCent_Delta (spectral centroid of delta rhythm on channel location Cz) and Pz_SpecCent_Alpha (spectral centroid of alpha rhythm on channel location Pz) chosen as most significant.

TABLE 5. RESULTS FOR ALL 475 FEATURES

Method Acc. F1 score

SVM 0.7667 0.7586 KNN 0.7667 0.7586

Naïve Bayes 0.7000 0.7097 Linear Discriminant 0.7000 0.6400

Tree 0.5333 0.5625 Logistic Regression 0.5333 0.5333

Fig. 4 shows the scatter plot of points defined by feature Cz SpecCent_Delta on the x-axis and Pz_SpecCent_Alpha on the y-axis.

Figure 4. Scatter plot of features Cz_SpecCent_Delta and

Pz_SpecCent_Alpha

Fig. 5 shows the scatter plot of points defined by feature F1_RelPow_Beta on the x-axis and Fp2_SpecCent_Alpha on the y-axis.

Figure 5. Scatter plot of features F1_RelPow_Beta and

Fp2_SpecCent_Alpha

5-fold cross-validation results for binary classification of classes depression and healthy are shown in Tab. 6. Of the 475 available features, only 4 are used:

• Fp1_RelPow_Beta

• Fp2_SpecCent_Alpha

• Cz_SpecCent_Delta

• Pz_SpecCent_Alpha

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TABLE 6. RESULTS FOR ALL SEQUENTIALLY SELECTED FEATURES

Method Acc. F1 score

KNN 0.8667 0.8750 SVM 0.8667 0.8571

Naïve Bayes 0.8667 0.8571 Tree 0.8333 0.8276

Linear Discriminant 0.8000 0.7857 Logistic Regression 0.7667 0.7407

The K-Nearest Neighbor (KNN) algorithm shows the highest classification accuracy and F1 score, followed by the Support Vector Machine (SVM) and the Naive Bayes.

IV. CONCLUSION AND FUTURE WORK EEGs of 15 depression patients and 15 healthy controls

are filtered, re-referenced to average reference, decomposed using independent component analysis, and then cleaned of blink, muscle, and cardiac artifacts by removing artifact components. Resting-state EEG segments are extracted from the individual EEG recordings, and feature extraction is performed on said segments.

For 5-fold cross-validation for binary classification of complete feature sets (475) labeled with labels depression or healthy SVM and KNN show the highest accuracy and F1 scores amongst the evaluated methods.

Sequential feature selection identifies the relative power of beta rhythm on Fp1, the spectral centroid of the alpha rhythm on Fp2, the spectral centroid of the delta rhythm on Cz, and the spectral centroid of the alpha rhythm on channel location Pz as the most significant features.

5-fold cross-validation with sequentially selected features shows higher accuracy and F1 scores across all evaluated methods, with the Linear Discriminant Analysis and Logistic Regression showing the biggest rise in accuracy and F1 score.

To determine whether these findings can be generalized to fit EEGs outside the observed dataset, trained models should be tested with feature – label pairs belonging to new EEGs. Moreover, the models should be trained with a bigger dataset to see whether an improvement in evaluation metrics can be achieved. Feature selection should be

repeated on a larger dataset. The inclusion of other features should be examined as well as the use of other classification methods.

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