## 7 Conclusions  
In this study, we developed an efficient method for epileptic seizure detection that could be easily implemented in a microcontroller device as well as a mobile application for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Author** & **Method** & **Features** \\ \hline Android application for & & & \\ neonatal colonic seizures & & & \\ detection & & & \\ \hline Android application & [PERSON] et al. [75] & Fall detection & Low accuracy. \\ \hline Android application & [PERSON] et al. [76] & & \\  & & & \\  & & & \\ \hline Fitbit devices & [PERSON] et al. [77] & SMS and GPS alerts & Lack the compatibility. High cost. \\ \hline Android application & [PERSON] et al. [78] & Fall detection & GPS locations/SMS. \\ \hline Proposed android application & [PERSON] et al. & EEG signal processing & High accuracy. Immediate notification. Development for real-time processing. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Table of Comparison of Related Mobile Applications. Figure 17: Epilepsy Detection Notification. real-time detection. We used an EEG dataset, which was the best publicly available from the University of Bonn in Germany. The noise and artifacts of the signal were eliminated by applying three different filters, and in the next step, we decomposed the signal using enhanced wavelet analysis. Furthermore, out of the many features extracted, three features were selected for the classification procedure. One of these features was our newly designed feature, the crest range. In the final stage, three of the most widely used machine-learning techniques in epilepsy classification, namely ANN, KNN, and SVM, were used for training and testing. After random classification experiments with different testing percentages, including 20%, 30%, 40%, and 50%, and after calculating the average accuracy of at least ten classifications at each percentage level, we achieved the best accuracy when using the KNN classifier. We developed a mobile application that monitored the behavior of users based on EEG processing and classification. The application was connected to the classification procedure through a cloud server and updated the results instantly whenever a new signal was processed. An alarm would be triggered should an epileptic seizure be detected. Furthermore, we have also tested real-time seizure detection using a Neurosky Mindwave headset that utilizes Bluetooth to exchange data with other devices. The device reads the EEG signal from the forehead and sends it via Bluetooth for processing. However, at this stage, we could only test the normal EEG signal since using the device on epileptic patients is out of our scope. In future work, we will collect data from epileptic patients so that we can use and test the Mindwave device to collect epileptic EEG signals in real time. As well as trying to detect different types of seizures. **Author Contributions:** Participated in this research: [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Conducted and designed the experiments: [PERSON], [PERSON] and [PERSON]. Performed data analysis: [PERSON] and [PERSON]. The manuscript was written by [PERSON] and [PERSON]. [PERSON] and [PERSON] contributed to the intellectual merit of the new investigation. All authors have read and agreed to the published version of the manuscript. **Funding:** CT Next-UB grant number UB-18-102 funded this research. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.