Hardware Implementation of a Real-Time Adaptive Time-Series Segmentation Algorithm for Intracortical Implants

Epilepsy affects over 50 million people worldwide, posing a significant clinical challenge, particularly for patients unresponsive to conventional treatments. Advances in neural implants with on-device algorithms are revolutionizing epilepsy management by enabling precise, real-time seizure detection and reducing the technical and financial burden of data transmission. The current trend advances towards the integration of a larger number of electrodes in neural implants, enhancing spatial resolution and broadening brain coverage. Consequently, the increasing data demands necessitate highly efficient processing to minimize transmission bandwidth and power consumption, ensuring the long-term viability of implantable systems. This work presents a novel approach using time-series segmentation (TSS) to extract labeled information from raw recordings. The algorithm explores multiple outlier detection methods with a heuristic low-complexity event classifier, and employs a multichannel consensus strategy to improve detection accuracy through multichannel agreement. This system enables high-performance seizure detection and segments local field potentials (LFP) into clinically relevant labels for interpretation and post-processing. Tested on microelectrode array (MEA) recordings from mouse hippocampus-cortex slices treated with 4-aminopyridine, the system demonstrated robust reliability. Implemented on a Pynq-Z2 board with a Zynq 7020 System-on-Chip, the algorithm requires minimal calibration, achieving 95% accuracy, 94% sensitivity, and a 0.03% FPR with a low power consumption of 128 mW for the best-performing outlier detector. By demonstrating the application of TSS to implantable device algorithms for on-device processing, this work advances towards more effective, personalized epilepsy treatments.