**2. Materials and methods**

#### **2.1 Measurement protocol**

The real time data was recorded from 30 medication free outpatients under the age group of 20–50 years comprising of 16 female and 14 male patients from the Psychiatry department of Medical College, Calicut, Kerala, India (female mean age: 33; male mean age: 35). The measurement was done on unipolar depression patients who did not have any history of substance abuse and no significant medical illness.

*Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation… DOI: http://dx.doi.org/10.5772/intechopen.94254*

Similarly 30 age and sex matched healthy controls also participated in the study who were free of medical illness. None of them reported of a history of any central nervous system disorder. Bipolar EEG recordings using a 24-channel EEG measuring instrument was carried out at locations FP1-T3 (left half) and FP2-T4 (right half) of the brain. The electrodes were placed, based on the International 10–20 electrode placement system. An ear clip electrode attached to the right earlobe served as an isoground connection. The EEG recordings were done by placing the electrodes on the frontopolar-temporal regions both on the left and right half of the brain for duration of 5 minutes each, under eyes closed and eyes open condition in a resting state. The sampling frequency of the signal is 256 Hz and is notch filtered at 50 Hz to remove the power line interference. Statistical analysis was performed by One-way ANOVA to test for differences among the two classes of EEG signals recorded. Informed written consent was obtained from all the subjects who participated in the study and medical ethical committee approval was taken prior to the study. **Figures 1** and **2** show a typical EEG signal of normal and depression patient respectively.

#### **2.2 Preprocessing**

Artefacts such as eye movements, eye blinks, head movements, cardiac and muscle activation artefacts, tongue movements and power line noise pose a problem for the proper EEG interpretation and analysis. Other artefacts that disrupt the EEG signal include instrument artefacts (faulty electrodes), sweat artefacts, impedance fluctuations, cable movements, pulse artefacts etc. Power line interferences are removed from the EEG signal by using a 50 Hz notch filter. Eye movement and muscle movement artefacts are manually removed from the signal with the help of an expert by visual inspection. In this work, the high frequency components present in the acquired EEG signals are denoised using Total Variation Filtering (TVF) [2].

The TVF employed in this work is based on the algorithm developed by Chambolle [3]. A dual formulation approach is used to minimise the objective

**Figure 1.** *EEG signal of a normal control.*

**Figure 2.** *EEG signal of a depression patient.*

function of the Total Variation (TV) denoising problem. So the TV denoising problem amounts to minimising the following discrete function [4]:

$$J(\mathbf{x}) = \|\mathbf{y} - \mathbf{x}\|\_2^2 + \lambda \|A\mathbf{x}\|\_1 \tag{1}$$

where *A* is a matrix of size M � N. Smoothing of the signal is controlled by *λ*, which is known as the regularisation parameter. Since the amount of high frequency noise present in the EEG signal recorded from depression patients using the 24 channel equipment was already low, the optimal value of *λ* for denoising was found to be 0.9.
