## 6 Mobile Application Development  
We developed an Android-based mobile application that could monitor the behavior of an epileptic user. Our mobile application operated based on the processing and classification of the EEG signal and provided features such as sending an immediate notification once an epileptic seizure was detected. Furthermore, the application displayed the EEG spectrum of the user's state and provided an alarm notification sent to the user as well as to family members and caretaxhers during the occurrence of an epileptic seizure. The application was connected to the EEG classification process via a wireless internet connection and displayed instant results. We tested the application using real-time EEG signals, as collecting epileptic signals would require clinical trials, which was beyond the scope of this paper.  
The application development consisted of three modules, namely a login/registration, EEG signal, and visual and alarm notification. As shown in Figures 14 and 15, the communication between the application and the server was established using socket events, and the three components were notified through an \"HTTP POST\" request notification to a DigitalOcean (DO) server. Then, the HTTP POST response interface was utilized by the DO server to obtain the notification from both the client and the device, as well as to relay the HTTP GET request between the application and the server [73].  
Once a connection was established between the application and the server, the client was required to register and log in to the application. Then, the application informed the server that it was ready for an update event. If an epileptic signal was detected, the server would immediately send an update event to the application. After that, the application would send an HTTP GET request to the server. Furthermore, an alarm notification would be triggered as soon as an epileptic signal had been detec  
Figure 14: Architecture Diagram.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**No** & **Title** & **Author** & **Features** & **Classifier** & **Accuracy** \\ \hline \multirow{4}{*}{17} & Automatic Identification of & & mean of spectral magnitude for PSD, & \multirow{2}{*}{Gaussian mixture} & \multirow{2}{*}{- (PSD) 88.78\%} \\  & epilepsy by HOS and power & & mean of spectral magnitude for & & \\  & spectrum parameters using & EEG signals: a comparative & & HOS, & model (GMM) & - (HOS) 93.11\% \\  & study (2008) & & entropy & & \\ \hline \multirow{2}{*}{18} & \multirow{2}{*}{Proposed method: (2023)} & \multirow{2}{*}{[PERSON] et al.} & amplitude range, & ANN, & \multirow{2}{*}{96\% 95\% 98\%} \\  & & band power, & SVM, & & \\ \cline{1-1}  & & crest range & KNN & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._classification was complete, the application would receive a notification from the server and show the results on a smart device. It would also trigger an alarm.  
The application required the user to initially register and then activate their account using a verification link. This enabled immediate notification and alarms to be sent to all registered clients via email ID notification. The login/registration screen is shown in Figure 16.  
Figure 16: Login/Registration Screen.  
Figure 15: Application, Server, and Device Connection.  
During the registration process, the application would track users who had requested to be notified by an alarm when an epileptic seizure had been detected. The alarm notification is shown in Figure 17.  
There has not been a significant amount of published research that has involved the use of a mobile application for epileptic seizure detection. Therefore, we compared our proposed application to the most relevant methods available. As shown in Table 10, our proposed method outperformed the existing methods due to its use of EEG signals as well as the features that it provided.