Signal Quality Screening and Automated Sleep Stage Agreement in Home EEG: A Systematic Comparison of Dreamento and YASA on the Wearanize+ Dataset

Wearable EEG devices such as the Zmax headband offer scalable alternatives to laboratory polysomnography (PSG) for sleep monitoring, but their real-world performance in home settings remains poorly characterised. This study presents a systematic validation of automated sleep staging on the Wearanize+ dataset; a unique multimodal resource providing synchronised full PSG, bilateral Zmax EEG (F7-Fpz/F8-Fpz), and psychiatric phenotyping from 100 participants recorded at home. We first developed and applied an automated signal quality screening framework, revealing that 10% of recordings failed completely due to signal dropout and a further 16% showed partial degradation. We then evaluated two automated staging algorithms; Dreamento and YASA against PSG manual scoring, stratified by signal quality. In technically adequate recordings (N=74), YASA achieved significantly higher agreement than Dreamento (mean κ=0.450 vs 0.371; Δκ=+0.079, p=0.0005), primarily through substantially improved N2 detection (recall: 0.64 vs 0.36). Both algorithms showed a systematic N2/N3 boundary confusion, however in opposite directions: Dreamento over-called N3 (37% of N2 epochs mis-staged as N3), while YASA over-called N2 (35% of N3 epochs mis-staged as N2). Critically, Dreamento showed greater robustness than YASA in degraded-quality recordings (WARN group: κ=0.414 vs 0.330), consistent with its training on Zmax-specific data. Signal quality metrics did not predict staging performance within adequate recordings, indicating that channel topology is the primary limiting factor for frontal single-channel staging. These findings establish the Wearanize+ dataset as a benchmark for wearable sleep staging and motivate the use of PSG manual stage labels for downstream physiological analyses.