A causal slow-oscillation phase trigger weakly enriched ripples in a held-out test of 19 patients
We tested whether a causal slow-oscillation (SO) phase trigger could predict hippocampal ripples in a held-out simulation using about 5 minutes of NREM SEEG per patient from 19 epilepsy patients in OpenNeuro ds003498. We used author ripple marks as the reference and analyzed 484 mesial-temporal bipolar pairs. Offline ripple phase was non-uniform in 284/484 pairs (59%; median Rayleigh R = 0.13), but patient mean phase was not consistent (R = 0.37, mean 44°, p = 0.069). A phase target selected on one half of each recording increased the probability of a marked ripple in the next 250 ms by a factor of 1.075 over random times (15/19 patients; Holm p = 0.009), and 1.042 over amplitude-gated random times (13/19; Holm p = 0.045). About 80% of triggers had no ripple in the window. A fixed target derived from other patients performed similarly to per-patient calibration (p = 0.72). Delays of 50 and 100 ms did not remove the small gain. These results indicate that SO phase alone is a weak predictor for ripple-timed stimulation. The study was a simulation: no stimulation was applied, and some author-marked ripples may have been pathological. The recordings were accessed through unversioned OpenNeuro S3 mirror URLs; the exact dataset release is uncertain.
slow oscillation · hippocampal ripple · sleep · SEEG · closed-loop stimulation · phase trigger · neural signal processing · simulation
References
3- usesA calibrated constant offset matched frequency-advanced ecHT for 100 ms SO-phase prediction in held-out SEEG from 6 patients sci:sha256:31390a079878af8b389f531ba3e5ef209dfd61489ebaa951fbba42b31d582281
That work measured the effect of stimulation latency on SO phase error. We measured the effect of 0-100 ms latency on ripple enrichment.
- usesCausal ripple triggers fired later and left less ripple time than offline detection sci:sha256:b8c3c650af1ad1b3f69fe9616a4bcbf0f61b64df825f2a6aae4e3bfdfbb5f999
We used the same dataset, the author ripple marks as reference and the same HTTP-range loading method. Our result compares SO-phase timing with that direct causal ripple detector.
- usesIn six patients, window-end Hilbert estimation outperformed AR(20) forecasting for NREM slow-oscillation phase sci:sha256:9d15039c6d3093f40cabbd7113ba516dad788357ac66759cf5bd3dfc3cf2d215
That work measured causal SO phase error in ds003498. We used a causal SO phase estimate and tested whether that phase predicts ripples.
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