# Kahlus-STF Public Dataset Review This is a source-selection note for the first public-data STF smoke. It is not a clinical validation claim and does not permit A100 training by itself. ## First Target: CHB-MIT Scalp EEG Database Source: Reason for first use: - public scalp EEG epilepsy dataset on PhysioNet - record-level lists are available through `RECORDS` and `RECORDS-WITH-SEIZURES` - seizure annotation sidecars use `.edf.seizures` - common benchmark target in seizure detection/prediction papers Local audit command: ```bash PYTHONPATH=src python3 scripts/fetch_chb_mit_smoke_subset.py \ --dataset chb_mit_physionet \ --out-root /tmp/kahlus_chbmit_smoke_subset \ --patients 2 \ --records-per-patient 2 ``` The fetch command materializes a small CHB-MIT subset outside the repository. It downloads only selected EDFs, `RECORDS`, `RECORDS-WITH-SEIZURES`, patient summary text files, and required `.edf.seizures` sidecars. ```bash PYTHONPATH=src python3 scripts/run_stf_public_data_audit.py \ --dataset chb_mit_physionet \ --data-root /tmp/kahlus_chbmit_smoke_subset \ --out-dir /tmp/kahlus_stf_chbmit_audit ``` The audit checks metadata shape only. It does not parse EDF, download data, copy raw signals, or launch A100 jobs. Local EDF smoke command after the audit passes: ```bash PYTHONPATH=src python3 scripts/run_stf_chb_mit_smoke.py \ --dataset chb_mit_physionet \ --data-root /tmp/kahlus_chbmit_smoke_subset \ --out-dir /tmp/kahlus_stf_chbmit_smoke \ --max-records 4 \ --max-samples-per-record 900000 \ --max-channels 8 ``` The smoke reads capped local EDF records with `edfio`, preserves variable-length record windows, builds patient-held-out plus time-held-out tasks, and reports persistence, ridge-AR, TinySSM, shuffled-target control, cycle/time-of-day, event-frequency, logistic-ridge, and time-shifted-label rows when CHB-MIT summary text files are present. Binary `.edf.seizures` parsing remains out of scope; summary text is the event-interval source for this smoke. ## Literature Stop The research risk is not lack of papers; it is over-optimistic validation. Shafiezadeh et al. report that patient-independent validation is rare in EEG seizure prediction, and Wong et al. emphasize that public EEG datasets differ in structure enough to hurt reproducibility. Therefore, Kahlus-STF starts with patient-held-out and time-held-out audits before any neural architecture upgrade. ## Blocked - committing raw EDF or annotation files - diagnosis, treatment, medication, stimulation, seizure-prevention, or vEEG/PSG replacement claims - A100 runs before local synthetic and public-data smokes pass