Generalized Tonic Clonic Seizure (GTCS) detection has been an ongoing problem in the healthcare industry. Algorithms and devices for this problem do exist on the market, but they either have poor False Positive Rates, are expensive, or cannot be used as anything other than a seizure detector. There is currently a need to provide a portable seizure detection algorithm that can meets patient demands. In this thesis, we develop a two-stage end-to-end seizure detection algorithm that is implemented on an Apple Watch, and validated on Epilepsy Monitoring Unit (EMU) patients. 124 features are extracted from the collected dataset, after which 9 are empirically selected. We have provided mutual information based feature selection methods that cannot yet be implemented on the watch due to computational restrictions. In stage one we compare common anomaly detection methods of One Class SVM, SVDD, Isolation Forest and Extended Isolation Forest over a thorough cross-validation to determine which is ideal to use as an anomaly detector. Isolation Forest (Sensitivity: 0.9, FPR: 3.4/day, Latency: 69s) was chosen despite the good sensitivity and latency of SVDD (Sensitivity: 1.0, FPR: 17.28/day, Latency: 8.9s) due to better implementation characteristics. During in-vivo testing, we record a sensitivity of 100% over 24 recorded tonic seizures with FPR: 1.29/day. To further limit false positive detections, a second stage is incorporated to separate between true and false positives using deep learning methods. We compare a Deep-LSTM, CNN-LSTM and TCN network. CNN-LSTM (Sensitivity: 0.93, FPR: 0.047/day) was finally used on the watch due to its tractable implementation, though TCN (Sensitivity: 1.0, FPR: 0/day) performed significantly better during cross-validation. During in-vivo testing, the 2-stage algorithm showed sensitivity: 100%, FPR: 0.05/day over 2004 tracked hours and 12 seizures. The mean latency was 62 seconds, which is on the threshold of clinical acceptability for this task
