## 5 Discussion  
The results above showed an effective performance of the adaptive classification with EEG data stream regarding accuracy and response time in the cloud environment. TPR (or sensitivity) is a popular metric for evaluating the performance of seizure detectors, as in this work. However, depending only on the high value of the True Negative Rate (or specificity) may lead to an unreliable evaluation. Epilepsy patients often have normal EEGs; consequently, if the classifier mostly receives normal EEG signals without seizures, then TNR tends to be very high. This metric does not necessarily reflect the actual performance in actual clinical use. The False Positive Rate is a more critical metric because false alarms about seizures waste the neurologists' time. However, the results in Table 3 show that ARFC has a low FPR equal to 0.0017. This means, on average, from 1000 detections, only 1.7 can be detected falsely as a seizure. Most of the previous work dealt with data as a batch file (i.e., not a stream) and focused on improving its classification accuracy regardless of its efficiency. Some of these works suggested a distributed classification environment, but the training process was carried out at both the edge and the master nodes. In our work, the results confirm the possibility of centralizing the training process of the adaptive model in a node with high computational resources and using the resulting model each time to perform the inference in the edge nodes close to the patient. This facility produces a lightweight and constantly adaptive classifier that can be implemented in low-resources devices such as smartphones, tablets, and even smartwatches. The fast response of the proposed classifier can provide efficient seizure onset detection. This kind of detection focuses on starting the warning procedures, diagnosis, and the required therapy. The instant notification for an epilepsy patient would save his life by preventing severe injuries caused by falling or car accidents through driving. The delayed alarm may cause worsening of seizure symptoms, thereby restricting the patient's ability to respond. The immediate seizure onset detection can also help the medical staff instantly give the patient the necessary anticonvulsant. Another essential characteristic of the proposed model is providing a resistant classification to the problem of connection loss, which is common on the user's side. The inference process continues in the edge node using the newest classifier regardless of a connection with the master node; the new data are sent after the connection returns. Regarding reliability, handling the delayed labeling as missing values can make the proposed detector more applicable for patients and medical staff because we do not expect high feedback, especially during seizure time. Despite that, the experiments were limited to a limited percentage of the values that did not exceed 3%. The missing rate could be more \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Authors** & **Technique** & **Accuracy** \\ \hline
2011 & [PERSON] et al. (2018) & On-line Retraining & 52.2 \\
2012 & [PERSON] et al. (2019) & LDA & 91.8 \\
2014 & [PERSON] et al. (2014) & Linear classifier & 84.2 \\
2016 & [PERSON] et al. (2014) & SVM & 98.68 \\
2017 & [PERSON] et al. (2012) & CNN & 82.2 \\
2018 & [PERSON] et al. (2013) & CNN & 97.5 \\
2019 & [PERSON] et al. (2015) & CNN & 92.2 \\
2020 & [PERSON] et al. (2017) & Recurrent CNN & 74.5 \\
2020 & [PERSON] et al. (2019) & SVM & 99.17 \\
2020 & [PERSON] et al. (2016) & CNN & 99.3 \\
2020 & [PERSON] et al. (2016) & CNN & 96.74 \\
2020 & [PERSON] et al. (2016) & SVM & 94 \\
2021 & [PERSON] et al. (2016) & CNN & 99.54 \\
2021 & [PERSON] et al. (2016) & CNN with LSTM & 97.51 \\
2021 & [PERSON] et al. (2015) & LGCN & 99.3 \\ This work & - & ARFC & 99.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance of ARFC compared to 15 related works. extensive in practical applications due to the lack of user response.