A CMOS-based neuromorphic device for seizure detection from LFP signals. (6th October 2021)

• Record Type: Journal Article
• Authors: Ronchini, Margherita; Zamani, Milad; Huynh, Hai Au; Rezaeiyan, Yasser; Panuccio, Gabriella; Farkhani, Hooman; Moradi, Farshad
• Is Part Of: Journal of physics. Volume 55:Number 1(2022)
• Publication Date: 2021-10-06
• Subjects: neuromorphic -- seizure-detection -- LFP signals -- SNN; Physics -- Periodicals; 530
• DOI: 10.1088/1361-6463/ac28bb
• Languages: English
• ISSNs: 0022-3727

Abstract

Abstract: Recent research provides examples of neuromorphic systems applied to process biological signals or to interface biological tissues. Usually, in such contexts, the neuromorphic system is used for automatic anomaly detection. The automation of the long-term monitoring of biological signals holds promise for lightening the burden placed on clinicians. At the same time, the adoption of such devices potentially allows processing to be performed locally, without the need to transfer data to an external processor. In turn, on-site signal analysis makes closed-loop intervention feasible, to correct the source of the anomalies. So far, the common approach has been to deploy the network of spiking neurons either on multi-core neuromorphic platforms or on programmable units (field programmable gate array). However, if the aim is to develop wearable or even chronically implantable devices, it is imperative to move in the direction of embedded solutions, tailored and optimized for the specific application. To this end, the present study proposes a neuromorphic device implemented in CMOS technology for the detection of epileptic seizures (ictal events) from local field potential (LFP) signals. The LFP data have been acquired by a multi-electrode array in a slice of mouse hippocampus-cortex. The system includes an analog-to-event converter (AEC) encoding the recorded signals into trains of spikes, and a small spiking neural network (SNN) of 2 × 1 neurons with online biological-plausible learning. The AEC yields two spike-trains: UP spikes that account for the positive slope signal and DOWN spikes that correspond to the negative slope signal. The synapses among the input and output layers are plastic and follow the spike-timing-dependent plasticity rule. Early results show that the SNN module is able to detect ictal events with a delay of 64.98 ± 30.92 ms, consuming < 50 pW. The layout of the entire system occupies 528 µ m × 278 µ m.