Real-Time Seizure Detection in Microelectrode Array based on Z-Test Spike Detection for Hardware Implementation

Epilepsy, affecting more than 50 million individuals globally, represents a significant clinical challenge, as numerous cases exhibit resistance to pharmacological treatment. Neural implants with advanced algorithms for on-device processing are transforming epilepsy management, enabling precise seizure detection and control. On-device algorithms must optimize computation to lower power use and minimize transmission bandwidth, ensuring long-term viability. We propose a new algorithm that combines a Z-test-based outlier detection approach with a heuristic decision tree to differentiate between interictal and ictal events. This algorithm operates in real-time to segment local field potentials (LFP) recorded via microelectrode arrays (MEA), detecting seizure onsets with minimal latency and low power requirements. Tested on an MEA dataset from brain slices, our algorithm achieved 98% sensitivity, 93% precision, 92% accuracy, and a 6% false detection rate (FDR), achieving a seizure detection latency of 0.5 ± 0.6 seconds. This work shows a robust, low-computation, low-latency approach resilient to noise requiring minimal configuration, advancing brain implantable devices for epilepsy treatment.