Event-based seizure detection in human iEEG with neuromorphic hardware

Abstract Background Epilepsy is a neurological disorder that affects approximately 1% of the global population. The current method for seizure monitoring, seizure diaries, is often inaccurate, making precise monitoring challenging. A monotonic descending “chirp” pattern in intracranial EEG (iEEG) is a specific marker of seizure onset and can be used for automatic detection. Objective To determine whether a spiking neural network (SNN) implemented on the DYNAP-SE1 neuromorphic chip can detect chirps/seizures. Methods We analysed 48 h of continuous bipolar iEEG from one patient (40 seizures). The signal was filtered into six 10 Hz sub-bands between 0 and 40 Hz, then encoded into UP/DOWN events with software asynchronous delta modulation (ADM). The encoded signal was used as input in the hardware-implemented SNN with 34 adaptive-exponential neurons (3.3 % of the 1024 neurons on one chip). Hierarchical inhibition enforced the required high-to-low band sequence, and a disinhibition unit suppressed isolated low-frequency bursts. We implemented the same 28-neuron SNN (without the dis-inhibition population) on software. Results Chirps appeared at the onset of all 40 seizures (100 %). The hardware SNN detected every seizure (sensitivity = 100 %) and produced one false alarm in 48 h (falsealarm rate = 0.021 h -1 ). Mean processing time was 4 h 55 s ± 42 s for each 4-h data block, showing real-time operation. In the software SNN implementation, we analysed the same 48-h iEEG bipolar channel recording, and detected 32/40 seizures (80 % sensitivity) with 9 false alarms (false-alarm rate = 0.19 h -1 ). Conclusion A 34-neuron SNN implemented on DYNAP-SE1 detects seizures from single-channel iEEG in real time with 100% sensitivity and a low false-alarm rate while using minimal hardware resources.