xpertsystems/hcneu003-sample

HC-NEU-003 — Epilepsy Seizure Event Dataset (Sample)

A schema-identical preview of HC-NEU-003, the XpertSystems.ai synthetic epilepsy patient cohort dataset for ILAE 2017 seizure classification research, antiepileptic drug (AED) efficacy modeling, EEG biomarker analysis, SUDEP risk stratification, and seizure prediction machine learning. The full product covers 10,000 patients with 5-year follow-up summaries. This sample is HF-sized at 3,000 patients (one row per patient).

Built by XpertSystems.ai — Synthetic Data Platform Contact pradeep@xpertsystems.ai · 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

from datasets import load_dataset

ds = load_dataset(
    "xpertsystems/hcneu003-sample",
    data_files="HC_NEU_003_dataset.csv",
    split="train",
)

Or with pandas directly:

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:

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.

XpertSystems.ai also publishes synthetic data products across Oil & Gas (17 SKUs), Cybersecurity, Insurance & Risk, and Materials & Energy. Catalog: huggingface.co/xpertsystems.