Spiking Seizure Classification Dataset

Dataset for event encoded analog EEG signals for detection of Epileptic seizures 

This dataset contains events that are encoded from the analog signals recorded during pre-surgical evaluations of patients at the Sleep-Wake-Epilepsy-Center (SWEC) of the University Department of Neurology at the Inselspital Bern. The analog signals are sourced from the SWEC-ETHZ iEEG Database

This database contains event streams for 10 seizures recorded from 5 patients and generated by the DYnamic Neuromorphic Asynchronous Processor (DYNAP-SE2) to demonstrate a proof-of-concept of encoding seizures with network synchronization. The pipeline consists of two parts (I) an Analog Front End (AFE) and (II) an SNN termed as"Non-Local Non-Global" (NLNG) network. 

In the first part of the pipeline, the digitally recorded signals from SWEC-ETHZ iEEG Database are converted to analog signals via an 18-bit Digital-to-Analog converter (DAC) and then amplified and encoded into events by an Asynchronous Delta Modulator (ADM). Then in the second part, the encoded event streams are fed into the SNN that extracts the features of the epileptic seizure by extracting the partial synchronous patterns intrinsic to the seizure dynamics. 

Details about the neuromorphic processing pipeline and the encoding process are included in a manuscript under review.  The preprint is available in bioRxiv

Installation
The installation requires Python>=3.x and conda (or py-venv) package. Users can then install the requirements inside a conda environment using 

conda env create -f requirements.txt -n sez

Once created the conda environment can be activated with conda activate sez

Long-term monitoring of biomedical signals is crucial for modern clinical treatment of neurological disorders such as epilepsy. Encoding epileptic seizures with partial synchronization using a spiking neural network (SNN) offers a promising avenue as such networks can be implemented on ultra-low-power neuromorphic processors. Indeed, such bio-inspired neuromorphic systems containing mixed-signal asynchronous electronic circuits can perform always-on monitoring of biomedical signals for extended periods of time, without having to employ traditional clocked analog-to-digital conversion or cloud-based processing platforms.

Here, we present a novel SNN architecture, co-designed and implemented with a mixed-signal neuromorphic chip, for monitoring epileptic seizures. Our hardware-aware SNN captures the phenomenon of partial synchronization within brain activity during seizures. We validate the network on a full-custom mixed-signal neuromorphic hardware using real-time analog signals converted from an Electroencephalographic (EEG) seizure data-set, and encoded as streams of events by an asynchronous delta modulation (ADM) circuit, directly integrated, together with its analog front-end (AFE) signal conditioning circuits, on the same die of the neuromorphic SNN chip.

We demonstrate the ability of the hardware SNN to extract local synchronization patterns from the event streams and show that such patterns can facilitate seizure detection using a simple linear classifier. This research represents a significant advancement toward developing embedded intelligent “wear and forget” units for resource-constrained environments. These units could autonomously detect and log relevant EEG events of interest in out-of-hospital environments, offering new possibilities for patient care and management of neurological disorders.