Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor

TL;DR

This work aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN), providing an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.

Abstract

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirments than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems.Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 uW(IO power) + 287.9 uW(computational power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.

Figures (9)

• Figure 1: Equivalent circuit of the LIF neuron model.
• Figure 2: Block diagram of a Leaky integration and Fire spiking neuron.
• Figure 3: Xylo comprises a hidden population of 1000 LIF neurons and a readout population of 8 LIF neurons. Dense input and output weights are available, along with sparse recurrent weights that can target up to 32 hidden neurons per neuron. The inputs, which consist of 16 channels, and the outputs, which consist of 8 channels, are transmitted through asynchronous firing events.
• Figure 4: (A). Comparison of power spectrum before and after filtering and notching. (B). EEG comparison after removing the selected independent components. Red is the signal before ICA, and black is the signal after ICA. This operation can check the influence of a specific component on the overall signal after removal. (C). Spatial distribution of each component on the scalp surface, the brighter the area, the greater the contribution of the independent component to the area. (D). The energy distribution of this independent component at different frequencies. (E). The time series reconstructing the trails of the independent components and the time of each trail. (F). The total variance of the waveform of each component over time under different numbers of independent components over time.
• Figure 5: Original signal and corresponding spikes time series, the data in the red line is the data of the epileptic seizure period.