SPIRIT: Low Power Seizure Prediction using Unsupervised Online-Learning and Zoom Analog Frontends
<|reference_start|>SPIRIT: Low Power Seizure Prediction using Unsupervised Online-Learning and Zoom Analog Frontends: 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.<|reference_end|>
@article{pandey2024spirit:,
    title={SPIRIT: Low Power Seizure Prediction using Unsupervised Online-Learning
  and Zoom Analog Frontends},
    author={Aviral Pandey, Adelson Chua, Ryan Kaveh, Justin Doong, Rikky Muller},
    journal={arXiv preprint arXiv:2409.04838},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04838},
    primaryClass={eess.SP cs.LG}
}