L1 ⚛ L1-531 ⊙ Testnet

EEG Seizure Detection (PWDR)

Medical Imaging · Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout

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⬇ L1-531.md

# ⚛ L1 Principle — EEG Seizure Detection (PWDR) **ID:** `L1-531` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D seizure class per segment > **📡 Carrier:** biopotential · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41553387 --- ## 🧠 1. Introduction **EEG Seizure Detection (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D seizure class per segment** space, within the **Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout** sub-domain of **Medical Imaging**. Measurements consist of biopotential signals recorded at the body surface via a **eeg with seizure classifier** sensing mechanism. The forward operator applies, in order: L · eeg acquisition operator; L · bandpass filter operator; L · artifact rejection operator; L · spectral decomposition operator; L · evolution detection operator; L · seizure threshold classifier operator; int · temporal spatial operator. Observations are corrupted by additive Gaussian noise. Existence inherited from L1-068. Uniqueness conditional on adequate channel coverage + electrode impedance < 10 kohm. Stability dominated by muscle_artifact and drowsy_state_confounder (subclinical seizures). Joint Hadamard well-posedness established by Fisher 2017 (ILAE 2017 Operational Classification), Trinka 2015 (status epilepticus definition), Hirsch 2013 (ACNS critical-care EEG terminology), Roy 2019 (deep learning EEG seizure benchmark), Acharya 2018 (EEG seizure deep learning review). ## ⚙ 2. Forward Model Physical chain: **x** → L · eeg acquisition → L · bandpass filter → L · artifact rejection → L · spectral decomposition → L · evolution detection → L · seizure threshold classifier → int · temporal spatial → **y** (detector). ``` y = `int.temporal_spatial` `L.seizure_threshold_classifier` `L.evolution_detection` `L.spectral_decomposition` `L.artifact_rejection` `L.bandpass_filter` `L.eeg_acquisition` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.eeg_acquisition` | L · eeg acquisition operator | | `L.bandpass_filter` | L · bandpass filter operator | | `L.artifact_rejection` | L · artifact rejection operator | | `L.spectral_decomposition` | L · spectral decomposition operator | | `L.evolution_detection` | L · evolution detection operator | | `L.seizure_threshold_classifier` | L · seizure threshold classifier operator | | `int.temporal_spatial` | Int · temporal spatial operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout | | Carrier | biopotential | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_seizure_class_per_segment | | Noise model | gaussian | | Integration axis | temporal_spatial | | Difficulty delta | 5 | | L dag | 6 | ## 📡 4. Measurement Model Existence inherited from L1-068. Uniqueness conditional on adequate channel coverage + electrode impedance < 10 kohm. Stability dominated by muscle_artifact and drowsy_state_confounder (subclinical seizures). Joint Hadamard well-posedness established by Fisher 2017 (ILAE 2017 Operational Classification), Trinka 2015 (status epilepticus definition), Hirsch 2013 (ACNS critical-care EEG terminology), Roy 2019 (deep learning EEG seizure benchmark), Acharya 2018 (EEG seizure deep learning review). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | sensitivity_per_seizure_event | ## 📏 5. Operating Range (Ω) **Center problem class:** `eeg_seizure_pwdr` · **Forward operator:** `eeg_seizure_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | Snr db | dB | 25 | | N channels | — | 19 | | Muscle artifact | — | 0 | | Duration minutes | — | 60 | | Sampling rate hz | Hz | 256 | | Movement artifact | — | 0 | | Voltage resolution uv | — | 1 | | Drowsy state confounder | — | 0 | | Electrode impedance kohm | — | 5 | | Neonatal premature pattern | — | 0 | | Electrocardiac contamination | — | 0 | | Manual neurologist inter rater kappa | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N channels | — | 4 – 256 | | Duration minutes | — | 10 – 10080 | | Sampling rate hz | Hz | 128 – 5000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.85_accuracy | Metric | Range | |---|---| | Categorical accuracy | 0.5 – 0.99 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **categorical_accuracy**, with κ = `100` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xcb4e44696a6858b8c739ce767902a8dc80703a62319f0e2292d77c1cccd0c4fc` - **Chain tx hash:** `0xe9366efe9e1c72cb6abe8878d646a103ee63723935756f63697947ebdb66282d` - **Chain block:** `41553387` --- ## File Mapping This bundle consists of: `L1-531.md`, `L1-531.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-531.md` | Source of truth — edit this | Human or LLM | | `L1-531.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-531`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-531.md).
Regenerate L1-531.json so every field matches the Markdown.
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L2-531-001 EEG Seizure Detection (PWDR) — Spec Skeleton

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📋 JSON metadata

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  "chain_tx_hash": "0xe9366efe9e1c72cb6abe8878d646a103ee63723935756f63697947ebdb66282d",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 5,
    "kappa": 100,
    "metric": "categorical_accuracy",
    "type": "epsilon_fn"
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      "name": "primary",
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  "layer": "L1",
  "observable_profile": {
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    "regime": "Existence inherited from L1-068. Uniqueness conditional on adequate channel coverage + electrode impedance \u003c 10 kohm. Stability dominated by muscle_artifact and drowsy_state_confounder (subclinical seizures). Joint Hadamard well-posedness established by Fisher 2017 (ILAE 2017 Operational Classification), Trinka 2015 (status epilepticus definition), Hirsch 2013 (ACNS critical-care EEG terminology), Roy 2019 (deep learning EEG seizure benchmark), Acharya 2018 (EEG seizure deep learning review).",
    "secondary": "sensitivity_per_seizure_event"
  },
  "physics_fingerprint": {
    "L_DAG": 6.0,
    "carrier": "biopotential",
    "difficulty_delta": 5,
    "domain": "Medical Imaging",
    "integration_axis": "temporal_spatial",
    "noise_model": "gaussian",
    "primitives": [
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      "L.bandpass_filter",
      "L.artifact_rejection",
      "L.spectral_decomposition",
      "L.evolution_detection",
      "L.seizure_threshold_classifier",
      "int.temporal_spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "eeg_with_seizure_classifier",
    "solution_space": "1D_seizure_class_per_segment",
    "sub_domain": "Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout",
    "title": "EEG Seizure Detection (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "eeg_seizure_pwdr_forward",
      "eeg_status_epilepticus_pwdr_forward",
      "eeg_neonatal_seizure_pwdr_forward",
      "ceeg_icu_pwdr_forward"
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    "allowed_omega_dimensions": [
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      "electrocardiac_contamination",
      "drowsy_state_confounder",
      "neonatal_premature_pattern",
      "manual_neurologist_inter_rater_kappa"
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      "eeg_status_epilepticus_pwdr",
      "eeg_continuous_icu_pwdr",
      "eeg_neonatal_pwdr"
    ],
    "center_spec": {
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      "forward_operator": "eeg_seizure_pwdr_forward",
      "input_format": "multichannel_eeg_with_seizure_label",
      "omega": {
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        "SNR_dB": 25,
        "drowsy_state_confounder": 0.0,
        "duration_minutes": 60,
        "electrocardiac_contamination": 0.0,
        "electrode_impedance_kohm": 5,
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        "movement_artifact": 0.0,
        "muscle_artifact": 0.0,
        "neonatal_premature_pattern": 0.0,
        "sampling_rate_Hz": 256,
        "voltage_resolution_uV": 1
      },
      "problem_class": "eeg_seizure_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
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        0.99
      ]
    },
    "omega_bounds": {
      "N_channels": [
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        256
      ],
      "duration_minutes": [
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        10080
      ],
      "sampling_rate_Hz": [
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        5000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Cortical electrical signal feature recovery from EEG with seizure / non-seizure categorical readout",
  "title": "EEG Seizure Detection (PWDR)"
}

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