MCLF: Montage consistent CNN–Liquid fusion for long-term scalp EEG seizure detection

Long-term scalp EEG monitoring yields hours of multi-channel recordings in which seizure-related patterns may appear only on a subset of derivations and can be obscured by transient artifacts. This work presents MCLF, a montage-consistent CNN–Liquid fusion method that implements cross-channel evidence integration as a state-based accumulation process within each epoch. Specifically, a shared 1D CNN encodes each channel into a common embedding space; embeddings are then arranged in a montage-consistent order and integrated by liquid state evolution to form an epoch-level representation for seizure scoring. A lightweight event-formation step converts the score sequence into clinically interpretable seizure events. Validation on the CHB-MIT dataset reports 100% event sensitivity with an FDR of 0.98/h and a mean latency of 2.33 s, while maintaining competitive segment-level performance relative to representative baselines. Key steps of the proposed method include: Apply per-epoch DWT reconstruction (Db4, 5 levels) followed by z-score normalization to standardize inputs for long-term recordings. Perform montage-consistent channel serialization and fuse the resulting channel stream via liquid state evolution for state-based evidence accumulation. Form seizure events from epoch-level scores using MAF smoothing, thresholding, collar expansion, and event merging with validation-based parameter calibration.