Unattended generalized tonic-clonic seizures carry the highest risk for Sudden Unexpected Death in Epilepsy (SUDEP). A need for automated at-home detection of these seizures has led to the development of many devices. Currently, all the available SDAS use proprietary hardware that reveals the diagnosis of the user and are often too expensive.
Consumer smartwatches, such as the Apple watch, have sensors that can perform activity recognition. Development of an alert system that can be run on consumer smart watches could provide a discreet and cost-effective solution. Therefore, a pipeline is needed to collect data, develop models and test the ability of consumer smart watches to act as SDAS. This pipeline’s open-source and scalable nature is designed for a multi-center data collection effort to develop reproducible and publicly validated machine learning models.