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arXiv Machine Learning By Hassan Mehdi, Riku Klen, Ayse Kosal Bulbul, Suzanne Timmons, Abdulhamit Subasi, Wei Chen, Zou Zhu, Muhammad Irfan

Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction

Read the original on arXiv Machine Learning →

The study compares six state‑of‑the‑art sleep‑staging foundation models on the MESA polysomnography dataset under three signal conditions: EEG only, ECG only, and EEG+ECG. Results show that EEG alone yields the highest accuracy (BIOT macro‑F1 = 0.7237), while switching to ECG alone incurs a consistent accuracy loss of about 0.35 macro‑F1 and reduces data rate to one‑third. Adding ECG to EEG offers little benefit for most models, indicating that EEG carries most of the sleep‑staging signal and that wearable‑compatible ECG alone is a viable but less accurate alternative.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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