  Implantable devices that record neural activity and detect seizures have been
adopted to issue warnings or trigger neurostimulation to suppress epileptic
seizures. Typical seizure detection systems rely on high-accuracy
offline-trained machine learning classifiers that require manual retraining
when seizure patterns change over long periods of time. For an implantable
seizure detection system, a low power, at-the-edge, online learning algorithm
can be employed to dynamically adapt to the neural signal drifts, thereby
maintaining high accuracy without external intervention. This work proposes
SOUL: Stochastic-gradient-descent-based Online Unsupervised Logistic regression
classifier. After an initial offline training phase, continuous online
unsupervised classifier updates are applied in situ, which improves sensitivity
in patients with drifting seizure features. SOUL was tested on two human
electroencephalography (EEG) datasets: the CHB-MIT scalp EEG dataset, and a
long (>100 hours) NeuroVista intracranial EEG dataset. It was able to achieve
an average sensitivity of 97.5% and 97.9% for the two datasets respectively, at
>95% specificity. Sensitivity improved by at most 8.2% on long-term data when
compared to a typical seizure detection classifier. SOUL was fabricated in
TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification
energy efficiency, which is at least 24x more efficient than state-of-the-art.
