A medical device using deep learning to analyze long-term neural data could effectively predict seizures in patients with epilepsy and reduce their disease burden, according to a study published in eBioMedicine.
Developing accurate and reliable predictive analytics for seizures is a challenge, the team points out, given that the neurological signals related to seizures are highly specific to individuals and can be subject to change.
Deep learning, a machine learning technique that mimics the decision-making structure of the human brain, provides a potential solution to the uncertainty that can burden epilepsy patients.
The model also allows clinicians and patients to set individual preferences regarding sensitivity, as well as the duration and number of alarms, which is a feature that isn’t available in other seizure prediction models.
The researchers note, “This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.”