# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 3 Deep Learning
### Results  
The CNN network employed in this study was designed using Two Intel Xeon 2.40 GHz (E5620) processors with 24 GB RAM and the Intel(R) Xeon(R) CPU E5-2650 v4 2.20 GHz (2 processors), 384 GB RAM and NVIDIA Quadro K4200. Accuracy, sensitivity, positive predictive value, and specificity were utilized as the assessment parameters. Tables 3 and 4 show the classification result per fold for subject based testing and non-subject based testing, respectively. The best diagnostic performance for the subject based testing is achieved with a learning rate of 0.001 while that of the non-subject based testing it is 0.0001. Figure 4a,b indicate the performance of the network with dropout layers. It is notable that the accurateness of the training set does not deviate substantially from that of the validation set in Figure 4a, when dropout is added to layers 9 and 10 during training for non-subject base testing. However, in Figure 4b, the accuracy of the training set is far better than that of the validation set, when dropout is added to layers 4 and 6 during training for the subject base testing. The proposed architecture generated high accuracy, sensitivity, specificity, and positive predictive values of 98.07%, 97.32%, 98.17%, 98.45% and 81.26%, 75.42%, 87.59%, 87.59%, for the non-subject based testing and subject based testing, respectively. It is apparent that non-subject based testing using 10-fold yields results of higher accuracy compared to subject based testing using 14-fold. Figure 5 shows the confusion matrix result. Based on Figure 5a, it is evident that 13.18% of healthy subjects are miscategorized as SZ patients and 23.32% of healthy subjects are incorrectly classified as SZ patients. In Figure 5b, 1.56% of healthy subjects are miscategorized as SZ patients and of 2.24% healthy subjects are wrongly classified as SZ patients.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Layers** & **Type of Layer** & **No. of Neurons (Output Layer)** & **Kernel Size** & **Stride** \\ \hline
1 & Convolution & \(6241\times 5\) & 10 & 1 \\
2 & Max pooling & \(3120\times 5\) & 2 & 2 \\
3 & Convolution & \(3111\times 10\) & 10 & 1 \\
4 & Max pooling & \(1555\times 10\) & 2 & 2 \\
5 & Convolution & \(1546\times 10\) & 10 & 1 \\
6 & Max pooling & \(773\times 10\) & 2 & 2 \\
7 & Convolution & \(769\times 15\) & 5 & 1 \\
8 & Max pooling & \(384\times 15\) & 2 & 2 \\
9 & Fully connected & 20 & - & - \\
10 & Fully connected & 10 & - & - \\
11 & Fully connected & 2 & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Parameter details of each layer of the developed CNN model in non-subject based testing.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fold** & **Accuracy (\%)** & **PPV (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline
1 & 87.14 & 100.00 & 72.73 & 100.00 \\
2 & 100.00 & 100.00 & 100.00 & 100.00 \\
3 & 51.35 & 100.00 & 5.26 & 100.00 \\
4 & 100.00 & 100.00 & 100.00 & 100.00 \\
5 & 94.44 & 100.00 & 88.57 & 100.00 \\
6 & 65.15 & 100.00 & 20.69 & 100.00 \\
7 & 69.66 & 66.67 & 98.11 & 27.78 \\
8 & 77.78 & 95.45 & 58.33 & 97.22 \\
9 & 55.42 & 56.10 & 97.87 & 0.00 \\
10 & 55.13 & 0.00 & 0.00 & 97.73 \\
11 & 98.89 & 100.00 & 98.15 & 100.00 \\
12 & 72.15 & 100.00 & 48.84 & 100.00 \\
13 & 100.00 & 100.00 & 100.00 & 100.00 \\
14 & 96.67 & 95.56 & 100.00 & 88.24 \\ \hline \end{tabular}
\end{table}
Table 3: Classification result per fold for subject based testing.