--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - epilepsy - seizures - neurology - ilae - sanad - aed - drug-resistant-epilepsy - eeg - ied - hfo - qolie-31 - sudep - vagus-nerve-stimulation - ketogenic-diet - epilepsy-surgery - hippocampal-sclerosis - photosensitive-epilepsy - levetiracetam - lamotrigine - valproate pretty_name: "HC-NEU-003 — Epilepsy Seizure Event Dataset (Sample)" size_categories: - 1K **Built by** XpertSystems.ai — Synthetic Data Platform > **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai) > **License** CC-BY-NC-4.0 (sample); commercial license available for the full product. --- ## What HC-NEU-003 does — and how it positions the Healthcare/Neurology vertical HC-NEU-003 is the **third Healthcare / Neurology SKU** in the XpertSystems catalog (HC-NEU-001 = Alzheimer's, HC-NEU-002 = Parkinson's). Together, these three SKUs cover the **three most heavily studied neurological diseases by patient count and pharma R&D spend**: | SKU | Disease | US Patients | Pharma R&D | Cadence | |---|---|---|---|---| | HC-NEU-001 | Alzheimer's | ~6.9M | ~$8B | Longitudinal, 20 visits, semi-annual | | HC-NEU-002 | Parkinson's | ~1.0M | ~$5B | Longitudinal, 32 visits, quarterly | | HC-NEU-003 | **Epilepsy** | **~3.4M** | **~$3B** | **Cross-sectional, 5-year summary** | Epilepsy is a fundamentally different disease shape than AD/PD — most clinical decisions hinge on **5-year medication trials and event counts** rather than continuous decline. HC-NEU-003 reflects this by shipping a **cross-sectional dataset** (one row per patient, ~89 columns) where each row summarizes a 5-year follow-up window: seizure characterization, trigger patterns, EEG findings, AED treatment + response, neuroimaging, quality of life, and SUDEP risk. This is the substrate **epilepsy pharma teams, neuro-stimulation device vendors, EEG analytics platforms, seizure prediction ML researchers, and ILAE epidemiology teams** have been waiting for: a coherent dataset where seizure type × etiology × AED × EEG biomarkers × QoL × SUDEP risk all interact with **SANAD-trial-grade calibration**. | Buyer Persona | Use Case | |---|---| | Epilepsy Pharma R&D | AED efficacy comparator modeling, DRE patient stratification | | Neuro-Stim Device Vendor | VNS/RNS/DBS candidate identification ML | | EEG Analytics Platform | IED detection ML, ictal pattern classification | | Seizure Prediction ML | Trigger-pattern + sensor + EEG ML training | | ILAE Epidemiology Team | Population-level epilepsy statistics modeling | | SUDEP Risk Modeling | Multi-factor SUDEP-7 risk stratification | | Surgical Candidate Selection | MRI + PET + EEG concordance ML for epilepsy surgery | --- ## What's inside **Single cross-sectional dataframe**, one row per patient. | Output | Rows (sample) | Columns | Size | |---|---:|---:|---| | `HC_NEU_003_dataset.csv` | 3,000 | 89 | ~1.4 MB | Schema is provided in `HC_NEU_003_schema.json`. The schema spans 7 modality blocks (demographics + seizure events + triggers + EEG + treatment + neuroimaging + QoL/comorbidities) covering ILAE-aligned seizure characterization, AED pharmacology, EEG biomarkers, and patient-reported outcomes. --- ## Calibration sources Every distribution is anchored to **named clinical references**. The headline anchors are **SANAD I** (Marson 2007 Lancet) and **SANAD II** (Marson 2021 Lancet) for AED-specific seizure-free rates, **Kwan & Brodie 2010** ILAE Task Force for drug-resistant epilepsy, and **WHO GBD Epilepsy Atlas** for etiology distribution. Other anchors: - **SANAD I trial (Marson 2007 Lancet)** — Levetiracetam vs Lamotrigine vs Carbamazepine in focal epilepsy. - **SANAD II trial (Marson 2021 Lancet)** — Valproate vs Levetiracetam in generalised epilepsy. - **ILAE 2017 Seizure Classification (Fisher 2017)** — 9-class seizure type taxonomy. - **ILAE 2017 Etiology Framework** — 6-class etiology taxonomy. - **Kwan & Brodie 2010 + ILAE Task Force** — drug-resistant epilepsy definition (failure of ≥2 appropriate AED trials). - **WHO GBD Epilepsy Atlas** — global epilepsy prevalence and etiology distribution. - **Rochester Epilepsy Study (Cramer 1998)** — QOLIE-31 normative data. - **Sperling 2003 + Bernasconi 2019** — MRI lesion detection in focal epilepsy. - **Fisher 2014** — photosensitive epilepsy prevalence. - **Faught 2009 Neurology** — real-world AED adherence. - **Devinsky 2016 + DeGiorgio 2010 SUDEP-7** — SUDEP risk stratification. - **Trinka 2015 + Sperling 1990** — status epilepticus and EEG IED detection. --- ## Validation scorecard The wrapper ships a 10-metric clinical-trial-anchored scorecard (`validation_scorecard.json`) that re-scores the dataset on every generation. Default seed 42 result: | ID | Metric | Target | Observed | Source | |---|---|---|---:|---| | M01 | Levetiracetam Seizure-Free Rate | 0.27–0.43 | **0.389** | **SANAD I** | | M02 | Lamotrigine Seizure-Free Rate | 0.36–0.52 | **0.442** | **SANAD I** | | M03 | Valproate Seizure-Free Rate | 0.43–0.59 | **0.487** | **SANAD II** | | M04 | Drug-Resistant Epilepsy Share | 0.26–0.46 | **0.417** | Kwan & Brodie 2010 | | M05 | Structural Etiology Share | 0.25–0.35 | **0.306** | WHO GBD / ILAE 2017 | | M06 | Focal Seizure Share | 0.45–0.65 | **0.532** | ILAE 2017 (Fisher 2017) | | M07 | QOLIE-31 Score Mean | 52–68 | **55.48** | Rochester Epilepsy Study | | M08 | Photosensitivity Share | 0.010–0.060 | **0.047** | Fisher 2014 | | M09 | MRI Lesion Detection Share | 0.22–0.38 | **0.298** | Sperling 2003 | | M10 | AED Adherence Mean (%) | 68–88 | **80.54** | Faught 2009 | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** **The standout achievement here is M01-M03: AED-by-AED seizure-free rates land within 4% of their SANAD I/II trial published rates.** - Levetiracetam observed 38.9% vs SANAD I published 35% (4 pp deviation) - **Lamotrigine observed 44.2% vs SANAD I published 44%** (0.2 pp — exact) - Valproate observed 48.7% vs SANAD II published 51% (2 pp deviation) This is **SANAD-trial-grade calibration** — the synthetic data reproduces the *exact* relative efficacy ranking of the AEDs from the two pivotal UK NIHR-funded trials that have defined epilepsy first-line therapy guidelines for the last 20 years. --- ## Suggested use cases - **AED comparator modeling** — calibrated AED-specific seizure-free rates × adherence × side effect burden support treatment-effect modeling for new AED candidates. - **Drug-resistant epilepsy (DRE) prediction** — pre-built `drug_resistant_epilepsy_flag` (Kwan-Brodie ILAE definition) with patient features enables DRE risk ML. - **Surgical candidate identification** — `surgical_candidate_flag` + MRI lesion + PET hypometabolism + EEG concordance supports epilepsy surgery patient selection ML. - **EEG biomarker analysis** — spectral power (delta/theta/alpha/beta/ gamma %) + IED frequency + HFO rate + ictal pattern × seizure type enables EEG ML training. - **SUDEP risk stratification** — `sudep_7_risk_score` + clinical features support SUDEP-7-aligned risk modeling for high-risk patient identification. - **Quality of life modeling** — QOLIE-31 + NDDI-E + GAD-7 + MoCA multimodal QoL ML for epilepsy outcomes research. - **Trigger pattern ML** — 9-trigger binary indicators (sleep, missed dose, stress, alcohol, photic, hormonal, fever, exertion, drug interaction) for seizure prediction model training. - **VNS / RNS / DBS candidate selection** — for neuro-stim device vendor ML training on patient eligibility scoring. --- ## Loading ```python from datasets import load_dataset ds = load_dataset( "xpertsystems/hcneu003-sample", data_files="HC_NEU_003_dataset.csv", split="train", ) ``` Or with pandas directly: ```python import pandas as pd from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="xpertsystems/hcneu003-sample", filename="HC_NEU_003_dataset.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` The dataset ships with `HC_NEU_003_schema.json` providing per-column dtypes for pipeline integration: ```python import json schema = json.load(open("HC_NEU_003_schema.json")) # {"patient_id": "object", "seizure_type": "object", "qolie_31_score": "float64", ...} ``` Unlike HC-NEU-001 (longitudinal AD) and HC-NEU-002 (longitudinal PD), HC-NEU-003 is **cross-sectional** — one row per patient summarizing a 5-year follow-up. There are no `visit_number` or `visit_date` columns beyond a single most-recent visit reference. --- ## Schema highlights **Demographics** — `patient_id`, `age_at_onset_years`, `age_at_visit_years`, `epilepsy_duration_years`, `sex`, `etiology` (6-class: Structural, Genetic, Infectious, Metabolic, Immune, Unknown), `early_onset_flag`. **Seizure characterization (ILAE 2017)** — `seizure_type` (9-class: Focal_Aware, Focal_Impaired_Awareness, Focal_to_Bilateral_Tonic_Clonic, Absence, Myoclonic, Tonic_Clonic, Atonic, Tonic, Epileptic_Spasm), `seizure_onset_zone`, `seizure_frequency_per_month`, `seizure_duration_sec`, `postictal_duration_min`, `status_epilepticus_flag`, `seizure_cluster_flag`, `nocturnal_flag`, `provoked_flag`, `annualized_seizure_rate`, `seizure_free_days`, `seizure_diary_compliance_pct`, `seizure_semiology_code`. **Triggers** — `trigger_sleep_deprivation`, `trigger_missed_aed_dose`, `trigger_emotional_stress`, `trigger_alcohol_use`, `trigger_photosensitivity`, `trigger_hormonal`, `trigger_fever_infection`, `trigger_physical_exertion`, `trigger_medication_interaction`, `trigger_count`, `trigger_identified_flag`, `catamenial_pattern_flag`. **EEG** — `eeg_type` ∈ {Scalp_Routine, Scalp_Ambulatory, sEEG, ECoG}, `eeg_duration_hours`, `eeg_background_activity`, `eeg_interictal_discharge_flag`, `eeg_ied_morphology`, `eeg_ied_frequency_per_hour`, `eeg_ictal_pattern`, `eeg_ictal_onset_frequency_hz`, spectral power % (delta/theta/alpha/beta/ gamma), `eeg_hfo_rate_per_min`, `eeg_spike_wave_hz`, `eeg_seizure_captured_flag`. **Treatment** — `treatment_arm` ∈ {Monotherapy, Polytherapy, Surgical, VNS_Therapy, Ketogenic_Diet, No_AED}, `aed_name` (13 agents), `aed_dose_mg_day`, `aed_mechanism`, `aed_polytherapy_count`, `aed_duration_months`, `aed_adherence_pct`, `seizure_reduction_pct`, `seizure_free_flag`, `treatment_response_class` ∈ {Seizure_Free, Responder, Non_Responder}, `drug_resistant_epilepsy_flag`, `aed_side_effect_score` (SIDAED 0-20), `aed_serum_level_ug_ml`, `surgical_candidate_flag`, `vns_therapy_flag`, `ketogenic_diet_flag`. **Neuroimaging** — `mri_lesion_type` ∈ {Hippocampal_Sclerosis, FCD, Vascular, Tumor, Cortical_Malformation, NaN}, `mri_lesion_flag`, `hippocampal_volume_ml`, `hippocampal_asymmetry_index`, `fcd_location`, `pet_hypometabolism_flag`, `spect_lateralization_index`, `mri_field_strength_T`. **QoL & comorbidities** — `qolie_31_score` (0-100), `qolie_seizure_worry`, `nddi_e_score` (0-30), `nddi_e_positive_flag`, `anxiety_gad7_score` (0-21), `cognitive_impairment_flag`, `moca_score`, `driving_restriction_flag`, `employment_status` ∈ {Employed, Unemployed, Disability, Student, Retired}, `sudep_7_risk_score`, `sudep_high_risk_flag`, `psychiatric_comorbidity` ∈ {Depression, Anxiety, PNES, ADHD, NaN}, `charlson_comorbidity_index`, `bmi`. --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Cross-sectional, not longitudinal.** Unlike HC-NEU-001 (20 visits) and HC-NEU-002 (32 visits), HC-NEU-003 is one-row-per-patient summarizing a 5-year follow-up. This is the **clinically appropriate shape for epilepsy management** (medication trials measured in years, not weeks), but means time-series modeling on this sample requires different methodology than HC-NEU-001/002. 2. **EEG IED detection share ~68% — below clinical 80-90%.** Salinsky 1987 reports IED detection in 50% of single routine EEGs but 80-90% with repeat EEG or sleep-deprived EEG. The generator's `eeg_type` mix is dominated by routine EEG (49%), reflecting realistic clinical acquisition patterns. For higher-yield IED studies, filter by `eeg_type` ∈ {Scalp_Ambulatory, sEEG} to recover ≥85% IED detection. 3. **Status epilepticus rate ~2.4% — below epilepsy lifetime 5-10%.** Trinka 2015 reports lifetime SE risk 5-10% in epilepsy populations; the generator reports per-patient SE history rate ~2.4%. The full product calibrates SE history more aggressively for lifetime risk modeling. 4. **SUDEP high-risk flag ~2.5% — at low end of high-risk cohort range 5-10%.** The actual SUDEP incidence is ~1/1000 patient-years (Devinsky 2016); cohorts classified "high SUDEP risk" by SUDEP-7 typically span 5-10% of populations. The generator is conservative; use `sudep_7_risk_score > 75th percentile` for relative risk stratification. 5. **Sleep deprivation trigger ~29% — at low end of Sperling 30-40%.** Patient self-report varies widely; the generator's 29% rate reflects conservative trigger attribution. 6. **Catamenial pattern flag ~5.7% — well below Herzog 1997 30% of female PD patients.** This reflects the *strict definition* (perimenstrual + periovulatory + perimenstrual+anovulatory subtypes combined); broader self-reported catamenial epilepsy is higher. For broader catamenial filtering, intersect with `trigger_hormonal == 1`. 7. **Driving restriction flag 89% — reflects DRE-enriched cohort.** This sample includes 42% DRE patients (above general population 30-40%); driving restriction follows DMV/state-by-state rules. For non-DRE driving analysis, filter `drug_resistant_epilepsy_flag == 0`. 8. **Treatment arm "No_AED" subset (~4.5%)** represents seizure-free recently-tapered, single-unprovoked-seizure not yet started AED, and refractory-no-current-AED subgroups combined. For deployed-patient modeling, filter to `treatment_arm != "No_AED"`. 9. **Cross-sectional means no within-patient trajectory.** For medication-switching modeling (a common clinical scenario), use the full product which carries `aed_history` as a list-typed column with per-AED entries. 10. **Deterministic seeding.** Wrapper passes user-specified seed into `CONFIG["seed"]`, `np.random.seed()`, and `random.seed()`. Seed sweep verifies Grade A+ across {42, 7, 123, 2024, 99, 1}. --- ## Commercial / full product The full **HC-NEU-003** product covers 10,000 patients × 5-year follow-up with optional longitudinal sub-cohort (per-quarter seizure event diaries), AED-switching history with sequence modeling, refined SUDEP risk modeling including detailed mortality cohort, ethnic-population stratified AED metabolism modeling (CYP-genotype-stratified phenytoin/carbamazepine levels), and pediatric epilepsy cohort variants (Lennox-Gastaut, Dravet, West syndrome). Available under commercial license — contact [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai). XpertSystems.ai also publishes synthetic data products across Oil & Gas (17 SKUs), Cybersecurity, Insurance & Risk, and Materials & Energy. Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).