SPIRIT: Low Power Seizure Prediction using Unsupervised Online-Learning and Zoom Analog Frontends

TL;DR

SPIRIT is a low-power, edge-device system-on-a-chip that uses unsupervised online learning with integrated retraining to predict seizures with high accuracy, early warning, and minimal maintenance.

Contribution

It introduces SPIRIT, a novel low-power SoC integrating unsupervised online learning and Zoom Analog Frontends for seizure prediction on edge devices.

Findings

Achieves 97.5% sensitivity and 96.2% specificity.

Predicts seizures 8.4 minutes in advance on average.

Consumes only 17.2 uW, the lowest reported for such classifiers.

Abstract

Early prediction of seizures and timely interventions are vital for improving patients' quality of life. While seizure prediction has been shown in software-based implementations, to enable timely warnings of upcoming seizures, prediction must be done on an edge device to reduce latency. Ideally, such devices must also be low-power and track long-term drifts to minimize maintenance from the user. This work presents SPIRIT: Stochastic-gradient-descent-based Predictor with Integrated Retraining and In situ accuracy Tuning. SPIRIT is a complete system-on-a-chip (SoC) integrating an unsupervised online-learning seizure prediction classifier with eight 14.4 uW, 0.057 mm2, 90.5 dB dynamic range, Zoom Analog Frontends. SPIRIT achieves, on average, 97.5%/96.2% sensitivity/specificity respectively, predicting seizures an average of 8.4 minutes before they occur. Through its online learning algorithm, prediction accuracy improves by up to 15%, and prediction times extend by up to 7x, without any external intervention. Its classifier consumes 17.2 uW and occupies 0.14 mm2, the lowest reported for a prediction classifier by >134x in power and >5x in area. SPIRIT is also at least 5.6x more energy efficient than the state-of-the-art.