Journal: Annals of General Psychiatry

Article Title: Analysis of EEG entropy during visual evocation of emotion in schizophrenia

doi: 10.1186/s12991-017-0157-z

Figure Lengend Snippet: Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using IIR filters (6-order Butterworth bandpass filters designed with the Matlab R2015b “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B

Article Snippet: Then, the signal is separated into five frequency bands using IIR filters (6-order Butterworth bandpass filters designed with the Matlab R2015b “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β (15–18 Hz), and β 3 (18–30 Hz).

Techniques: Biomarker Discovery