# Online EEG Seizure Detection and Localization
## 4 Discussion
On average, these networks needed to be calculated three times per hour (FPH of 2.9), which requires approximately 25 ms per hour. [PERSON] et al., [44] implemented their proposed method on the same \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Methods, Classifier1** & **Available** & **\#Patients,** **\#Seizures** & **TPR\%** & **FPH (hour1)** & **PS/NPS 3** \\ \hline [PERSON] et al. [41]5  & SAE & CHB-MIT & 639 & 100 & 7.96 & PS \\ \hline [PERSON] et al. [42] (2014) & CNBC, PSO & CHB-MIT & 21,132 & 89.01 & — & PS \\ \hline [PERSON] et al. [43] (2015) & GLCM, SVM & CHB-MIT & 23,153 & 70.19 & — & PS \\ \hline [PERSON] et al. [44] (2016) & PCA, LDA & CHB-MIT & 23,165 & 88.27 & 4.86 & PS \\ \hline [PERSON] [45] (1996) & ANN & — & 5034 & 76 & 1 & NPS \\ \hline [PERSON] [46] (1998) & SOM, ANN & — & 65,181 & 92.8 & 1.35 & NPS \\ \hline [PERSON] [47] (2000) & ANN & — & 426,670 & 76 & 0.11 & NPS \\ \hline Saab and Gotman [48] (2005) & CSP & — & 44,195 & 76 & 0.34 & NPS \\ \hline [PERSON] et al. [20] (2006) & LC, ANN & — & 634 & 91 & 1.17 & NPS \\ \hline [PERSON] et al. [50] (2009) & CSP & — & 2188 & 81 & 0.60 & NPS \\ \hline [PERSON] et al. [51] (2015) & EpiScan & CHB-MIT & 24,197 & 67 & 0.32 & NPS \\ \hline Proposed method & PBI, DN, CN & CHB-MIT & 18,123 & \
\begin{tabular}{c} Mean= 72, \\ Median= 83 \\ \end{tabular} &
\begin{tabular}{c} Mean= 2.82 \\ Median= 2.96 \\ \end{tabular} & NPS \\ \hline \hline \end{tabular}  
* 1 wavelet decomposition (WD), support vector machine (SVM), linear discriminant analysis (LDA), time and frequency domain features (TFEP), nuclear norm learning (NNL), improved particle swarm optimization (PSO), artificial neural network (ANN), stacked autoencoder (SAE), collective network of binary classifiers (CNBC), particle swarm optimization (PSO), gray level co-occurrence matrix (GLCM), principal component analysis (PCA), self-organizing map (SOM), conditional seizure probability (CSP), linear correlation (LC); 2 available online datasets; 3 patient-specific (PS) or non-patient-specific (NPS) algorithms; 4 average of median performance with one seizure training; 5 reported results with channel threshold = 3.  
\end{table} 
Table 6: _Cont._dataset using MATLAB version R2014b with a 3.4 GHz processor and 16 GB RAM. The time required for feature extraction for one second of one EEG channel was reported as 2.6 ms. For an hour of EEG analysis on one channel, the time required for feature extraction with linear discriminant analysis (LDA) classification is about 9.6 s. [PERSON] et al. (2017) implemented their algorithm in MATLAB with a 3.4 GHz machine with 16.0 GB RAM. The reported training time varied from 2 to 5 h, depending on the amount of training for each patient, and the computation time for seizure detection was reported as \(\sim\)10 ms for each 1-second EEG segment. This translates to about 36 s for an hour of EEG activity without considering the amount of time devoted to training. In contrast, for the proposed algorithm, the average computation time for analysis of an hour worth of EEG data for all channels is about 2.2 s.  
In addition to detecting a seizure occurrence, this study proposes a simultaneous localization of the seizure. EEG localization methods have lower spatial resolution compared to the other methods, such as MEG or fMRI, but the portability of EEG devices makes them a popular approach for some cases such as long-term monitoring (LTM) with wearable EEG devices for post-surgery seizure monitoring or monitoring the effect of antiepileptic drugs (AEDs) on a patient. Table 5 contains the localization performance of 175 EEG seizure sequences from the Karuniya dataset. The underlying hypothesis for locating the origin of a seizure using the proposed algorithm is to capture the seizure characteristic at the beginning of a seizure and localize it. The Karuniya EEG records are 10 s segments of a seizure, which could be at the beginning, middle, or last ten seconds of an ictal period.