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

arXiv: https://arxiv.org/abs/2409.04838 · HTML: https://mlsanity.com/paper/2409.04838/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC](https://mlsanity.com/paper/2205.06090/) (2022)
- [SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier](https://mlsanity.com/paper/2110.02169/) (2021)
- [Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks](https://mlsanity.com/paper/2206.09951/) (2022)

## Abstract (arXiv)

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.