Detecting Depression in Brain Activity with EEG Frequency Bands
Introduction
According to Steiger and Kimura, sleep and waking electroencephalogram (EEG) can act as anti-depressive therapy and depression biomarkers. The complex EEG signals are challenging to interpret and derive the associated figures hence a need for computer-aided diagnosis (CAD). For this reason, non-linear methods and chaos theory are often used in diagnosis. Specification of the value is essential to allow depression detection. Several responses can be used to evaluate the significance of using the EEG signals in depression detection. What if the neurons oscillated in unison and at a specific frequency on a scale detectable by EEG? How would the modeling help in depression detection?
Clinical Relevance
The EEG value that specifies the existence of depression in the brain can be defined to help act as the reference point. The divergence from the threshold value can determine the depth and severity of depression in the brain. Clinical detections guide these values. The detected value can determine the type of therapy effective for the depressed patient. According to Acharya et al., the EEG modeling for signals in the left side of the brain are sufficient to detect depression. The retrieved signals are compared with the normal clinical levels. These models are shown below.
The collected data is captured and processed, a model developed, and the output classified to determine if the signals depict any forms of depression. The extracted data's ranking uses differential figures such as fractal dimension, high-order spectra, and detrended fluctuation analysis with the CAD system. It is up to the clinicians to determine the most effective form of interpretation based on existing medical data. Non-linear methods are easier to use because the EEG outcome is used as a confirmation tool of the diagnosis. Besides, EEG signal processing can be used in early depression detection.
Methods
Newson and Thiagarajan explain that deep learning algorithms are used. The fusing of interhemispheric asymmetry is done and correlated with EEG values. Suppose we have 32 subjects, 16 with major depressive disorders and 16 who act as controls. The theta, alpha, and beta frequency bands are extracted. The connecting features are examined and segmented to fit the EEG signals. Structural matrices...
…point to determine the intensity of brain activities that contribute to depression. In the simulations, as shown in the MatLab code in the appendix, the tests are restricted to a small portion of varying health conditions. According to clinical predictions, there is a general pattern of increasing and decreasing values of theta and delta bands. There is dominance in the decrease of the alpha values. Beta band values fluctuate depending on the conditions. Since the objective of this lab is to answer the what-if question of using EEG to detect depression, the focus is on the registered signals and what they tell about brain activities (Newson and Thiagarajan). The goal is to check if the brain responses of the depressed can be monitored using signal levels by comparing the values with that of the normal brain. Equally, when the EEG signal and simulations are used on one participant, the effectiveness of therapy programs can be achieved by comparing EEG responses over some time. This way, medical practitioners can decide the best approach to run on individual patients. The reliability of the…
Works Cited
Acharya, U. Rajendra, et al. "Computer-Aided Diagnosis of Depression Using EEG Signals." European Neurology, vol. 73, no. 5-6, 2015, pp. 329–336., doi:10.1159/000381950.
Newson, Jennifer J., and Tara C. Thiagarajan. "EEG Frequency Bands in Psychiatric Disorders: A Review of Resting-State Studies." Frontiers in Human Neuroscience, vol. 12, 2019, doi:10.3389/fnhum.2018.00521.
Steiger, Axel, and Mayumi Kimura. "Wake and Sleep EEG Provide Biomarkers in Depression." Journal of Psychiatric Research, vol. 44, no. 4, 2010, pp. 242–252., doi:10.1016/j.jpsychires.2009.08.013.
AppendixBasic MatLab Code to define and initiate simulation using the EEG valuesSignal frequency definitionstheta = 4 – 7.9 HzAlpha = 8 – 12.9 HzLower Beta = 13 - 40 HzImport EEG data from subjectsSet intervals 1, 2,3 for x-axis with time as the independent variablePlotting1. plot(amp, x); 2. ylabel('Amplitude'); 3. xlabel('Time [s]'); 4. title('EEG Simulation for Depression Detection'); 5. subplot(1, 2, 3); 6. grid on; 7. plot(amp,distortion); 8. xlabel('Time [s]'); 9. title('Noise_reduce'); 10. ylabel('Amplitude'); 11. subplot(3, 1,2); 12. grid on; 13. plot(amplitude, noisy_captured EEGsignaval); 14. ylabel('Amplitude'); 15. xlabel('time [s]'); 16. title('Original signal overlapping noise reduction'); 17. subplot(1, 2, 3)to runencapsulate the main.m command with run as run(filename)
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