# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 4 Discussion  
### Comparison with Related Work  
Among related work, [PERSON] et al. [16] exploited feature extraction methods on the different brain waves and obtained an accuracy of 62.2% on the delta frequency band. [PERSON] et al. [14] also explored feature extraction methods on beta brain waves and obtained an accuracy between 91.5% and 93.9%. [PERSON] et al. [19] analyzed five brain waves using a software program and employed statistical analysis and feature selection. Two SVM models were then implemented for classification, with accuracies of 84% and 87% yielded for models 1 and 2, respectively. [PERSON] et al. [21] extracted features by employing feature extraction methods and selected features via linear  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fold** & **Accuracy (\%)** & **PPV (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline
1 & 96.52 & 98.36 & 95.24 & 98.08 \\
2 & 99.13 & 100.00 & 98.41 & 100.00 \\
3 & 99.13 & 100.00 & 98.41 & 100.00 \\
4 & 96.52 & 95.38 & 98.41 & 94.23 \\
5 & 100.00 & 100.00 & 100.00 & 100.00 \\
6 & 98.26 & 100.00 & 96.83 & 100.00 \\
7 & 94.69 & 95.16 & 95.16 & 94.12 \\
8 & 98.23 & 98.39 & 98.39 & 98.04 \\
9 & 99.12 & 100.00 & 98.39 & 100.00 \\
10 & 99.12 & 100.00 & 98.39 & 100.00 \\ \hline \end{tabular}
\end{table}
Table 4: Classification result per fold for non-subject based testing.  
Figure 4: Accuracy versus different epoch plot for (**a**) non-subject based testing and (**b**) subject based testing.  
Figure 5: Confusion matrix of (**a**) non-subject based testing and (**b**) subject based testing.  
discriminant analyses. Classification accuracies of 93.42% and 92.23% were achieved with the J5 MLP and J5 SVM classifiers, respectively. [PERSON] et al. [21] used the moving window method to compute Multiscale LZC to analyze brain signals. The study revealed that higher complexity values were present in right frontal regions of patients who were at rest. [PERSON] et al. [23] employed the Multi-domain connectome CNN model to classify extracted features with an accuracy of 93.06%. It can be noted from Table 5 that the current state-of-the-art techniques can be employed to classify SZ accurately. Comparing the different techniques discussed, it is evident that the highest accuracy is yielded for the classification of SZ using the CNN deep learning algorithm. In non-subject based testing, the segments used for training and testing are split randomly, wherein the subjects are not truly separated, resulting in higher accuracy, as compared to subject based testing, wherein the segments are not randomly split. Hence, using 10-fold validation [40; 41] for non-subject based testing generated more accurate results as compared to 14-fold validation for subject based testing. The model developed in our study and described herein could potentially also be used to diagnose other neurological disorders such as Alzheimer's, Parkinson's disease, and epilepsy. Apart from the CNN model, other deep learning methods such as long short-term memory (LSTM) and autoencoders could also be explored in the diagnosis of SZ.