Epilepsy is one of the most common neurological disorders in the world, affecting around 50 million people worldwide. The diagnosis of epilepsy can be confirmed in-hospital via video-electroencephalography (vEEG). Currently, long-term monitoring is limited to self-reporting seizure occurrences by the patients. In recent years, the development of wearable sensors has allowed monitoring patients outside of specialized environments. However, it is unfeasible to manually analyze the large amounts of data recorded by the devices. In our work, we focus on the development of automated seizure detection frameworks for identifying seizures in the wearable data. The main objective is to use such technologies to aid clinicians in monitoring patients long-term, providing a better diagnosis and tracking the progression of the disease over time.