Sequential-Model-CNN-with-Schizophrenia-Dataset

This repository contains the python notebook, in which 1D CNN (Sequential Model) has been implemented on a publicly available schizophrenia dataset. The dataset can be found in the RepOD website.

Dataset Description

The dataset comprised 14 patients with paranoid schizophrenia and 14 healthy controls. Data were acquired with the sampling frequency of 250 Hz using the standard 10-20 EEG montage with 19 EEG channels: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2. The reference electrode was placed between electrodes Fz and Cz.

Work Description

• All the required libraries have been imported. (numpy, pandas, matplotlib, glob, scipy, tensorflow-keras, scikit-learn)
• Used Python MNE to read .edf files.
• labels and group has been created.
• Extracted features. (12 features used)
• Applied sequential model (9 layers) & used LeakyReLU activation function.
• Trained model with 15 epochs and 400 batch size.
• Achieved 71.5 % accuracy.