Cyclops: Automated detection of interictal epileptiform discharges with few EEG channels

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis
and management. New EEG devices with fewer electrodes are more accessible but
their ability to detect IEDs is uncertain. The aim of this study is to develop
and validate a machine learning model capable of detecting IEDs in reduced-
channel EEG data, enabling broader epilepsy diagnosis.

Using EEG samples from 3,378 patients and an external validation set of 51
patients, we trained Cyclops, a deep neural network designed to function
across various channel configurations.

Performance was evaluated using AUROC and other clinically relevant metrics,
including IED source location sensitivity. Cyclops demonstrated strong
performance even with minimal channels. AUROC for one channel: 0.876 [95% CI:
0.854-0.897]; best configuration based on a clinically available product:
0.950 [95% CI: 0.936-0.962]; for the detection of focal IEDs with two local
channels, AUROC values ranged from 0.701 [95% CI: 0.656-0.745] to 0.930 [95%
CI: 0.902-0.955] with a median AUROC of 0.809. On the external validation set,
performance ranged from 0.692 [95% CI: 0.593-0.782] to 0.949 [95% CI:
0.922-0.972] with a median AUROC of 0.846. Thus, Cyclops supports effective
IED detection with reduced EEG setups, enhancing accessibility and expanding
epilepsy diagnosis to broader patient populations.