L1 ⚛ L1-528 ⊙ Testnet

ECG Arrhythmia Classification (PWDR)

Signal Processing · Cardiac electrical signal feature recovery with arrhythmia categorical readout

⬇ Download bundle 🚀 Submit to mainnet — earn 5% royalty

🎁 Contribute & earn PWM

Improve L1-528 — submit with or without uploading. Accepted submissions earn the 1/16 modify share of A(t).

✨ Submit without uploading free check · 1×

One click — no files, no typing. We submit a check of L1-528 for you; it goes through review and earns the 1/16 share once. One free no-upload Principle per account.

You'll be asked to log in first — then it's one click, no files.

⬆ Or submit by uploading your files

Upload your .md + .json as a modification of L1-528. Unlimited.

🛠 Helper: build your files in ChatGPT / Gemini / Claude

Layer template

⬇ principle.md ⬇ principle.json

Fork L1-528

⬇ L1-528.md ⬇ L1-528.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-528 (ECG Arrhythmia Classification (PWDR)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-528.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-528.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫_t dt `L.aami_threshold_classifier` `L.qrs_feature_extraction` `L.r_peak_detection` `L.blind_source_separation` `L.bandpass_filter` `L.ecg_acquisition` x + n,    n ~ 𝒩(0, σ²)

world state x (1D arrhythmia class per beat)

→

L · ecg acquisition `L.ecg_acquisition`

→

L · bandpass filter `L.bandpass_filter`

→

L · blind source separation `L.blind_source_separation`

→

L · r peak detection `L.r_peak_detection`

→

L · qrs feature extraction `L.qrs_feature_extraction`

→

L · aami threshold classifier `L.aami_threshold_classifier`

→

Temporal integration ∫_t dt

→

observation y (biopotential detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-528.md

# ⚛ L1 Principle — ECG Arrhythmia Classification (PWDR) **ID:** `L1-528` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Signal Processing — *Cardiac electrical signal feature recovery with arrhythmia categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D arrhythmia class per beat > **📡 Carrier:** biopotential · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41555318 --- ## 🧠 1. Introduction **ECG Arrhythmia Classification (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D arrhythmia class per beat** space, within the **Cardiac electrical signal feature recovery with arrhythmia categorical readout** sub-domain of **Signal Processing**. Measurements consist of biopotential signals recorded at the body surface via a **ecg with aami classifier** sensing mechanism. The forward operator applies, in order: L · ecg acquisition operator; L · bandpass filter operator; L · blind source separation operator; L · r peak detection operator; L · qrs feature extraction operator; L · aami threshold classifier operator; detector accumulates flux over the exposure window. Observations are corrupted by additive Gaussian noise. Existence inherited from L1-388. Uniqueness conditional on adequate SNR (typical 20-30 dB) and clean lead placement. Stability dominated by motion_artifact_wearable for consumer ECG. Joint Hadamard well-posedness established by Pan-Tompkins 1985 (foundational R-peak detection), AAMI 1998 EC57 standard, Hannun 2019 (deep-learning ECG arrhythmia detection at cardiologist level), Attia 2019 (Apple Heart Study), Perez 2019 (smartwatch AF screening). ## ⚙ 2. Forward Model Physical chain: **x** → L · ecg acquisition → L · bandpass filter → L · blind source separation → L · r peak detection → L · qrs feature extraction → L · aami threshold classifier → Temporal integration → **y** (detector). ``` y = ∫_t dt `L.aami_threshold_classifier` `L.qrs_feature_extraction` `L.r_peak_detection` `L.blind_source_separation` `L.bandpass_filter` `L.ecg_acquisition` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.ecg_acquisition` | L · ecg acquisition operator | | `L.bandpass_filter` | L · bandpass filter operator | | `L.blind_source_separation` | L · blind source separation operator | | `L.r_peak_detection` | L · r peak detection operator | | `L.qrs_feature_extraction` | L · qrs feature extraction operator | | `L.aami_threshold_classifier` | L · aami threshold classifier operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Signal Processing | | Sub domain | Cardiac electrical signal feature recovery with arrhythmia categorical readout | | Carrier | biopotential | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_arrhythmia_class_per_beat | | Noise model | gaussian | | Integration axis | temporal | | Difficulty delta | 3 | | L dag | 5.9 | ## 📡 4. Measurement Model Existence inherited from L1-388. Uniqueness conditional on adequate SNR (typical 20-30 dB) and clean lead placement. Stability dominated by motion_artifact_wearable for consumer ECG. Joint Hadamard well-posedness established by Pan-Tompkins 1985 (foundational R-peak detection), AAMI 1998 EC57 standard, Hannun 2019 (deep-learning ECG arrhythmia detection at cardiologist level), Attia 2019 (Apple Heart Study), Perez 2019 (smartwatch AF screening). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | F1_per_AAMI_class | ## 📏 5. Operating Range (Ω) **Center problem class:** `ecg_aami_pwdr` · **Forward operator:** `ecg_aami_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | Snr db | dB | 25 | | N leads | — | 12 | | Baseline wander | — | 0 | | Muscle artifact | — | 0 | | Duration seconds | — | 10 | | Sampling rate hz | Hz | 500 | | Lead placement error | — | 0 | | Voltage resolution uv | — | 5 | | Motion artifact wearable | — | 0 | | Powerline interference 60hz | — | 0 | | Manual cardiologist disagreement | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N leads | — | 1 – 12 | | Duration seconds | — | 5 – 86400 | | Sampling rate hz | Hz | 100 – 1000 | ## 🎯 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 = `80` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x369ae3694ab4576b30e9f7bc59c096d848afea584fc1266052cf08c41a746bbd` - **Chain tx hash:** `0xf548171affb46d1d2dedd4cc0b77fdb4f50b6d681b4f20d3f2d6fd3d8eca73c4` - **Chain block:** `41555318` --- ## File Mapping This bundle consists of: `L1-528.md`, `L1-528.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-528.md` | Source of truth — edit this | Human or LLM | | `L1-528.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 `L1-528`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

💬

Ask the PWM AI agent AI

Ask about this ECG Arrhythmia Classification (PWDR) — or how to improve it & earn PWM

Free to search; log in to chat with the agent & earn PWM. How it works ↗

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Digital Twin 1 child ▾

L2-528-001 ECG Arrhythmia Classification (PWDR) — Spec Skeleton

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-528",
  "chain_block": 41555318,
  "chain_hash": "0x369ae3694ab4576b30e9f7bc59c096d848afea584fc1266052cf08c41a746bbd",
  "chain_tx_hash": "0xf548171affb46d1d2dedd4cc0b77fdb4f50b6d681b4f20d3f2d6fd3d8eca73c4",
  "domain": "Signal Processing",
  "hardness_fn": {
    "delta": 3,
    "kappa": 80,
    "metric": "categorical_accuracy",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "categorical_accuracy",
    "regime": "Existence inherited from L1-388. Uniqueness conditional on adequate SNR (typical 20-30 dB) and clean lead placement. Stability dominated by motion_artifact_wearable for consumer ECG. Joint Hadamard well-posedness established by Pan-Tompkins 1985 (foundational R-peak detection), AAMI 1998 EC57 standard, Hannun 2019 (deep-learning ECG arrhythmia detection at cardiologist level), Attia 2019 (Apple Heart Study), Perez 2019 (smartwatch AF screening).",
    "secondary": "F1_per_AAMI_class"
  },
  "physics_fingerprint": {
    "L_DAG": 5.9,
    "carrier": "biopotential",
    "difficulty_delta": 3,
    "domain": "Signal Processing",
    "integration_axis": "temporal",
    "noise_model": "gaussian",
    "primitives": [
      "L.ecg_acquisition",
      "L.bandpass_filter",
      "L.blind_source_separation",
      "L.r_peak_detection",
      "L.qrs_feature_extraction",
      "L.aami_threshold_classifier",
      "int.temporal"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "ecg_with_aami_classifier",
    "solution_space": "1D_arrhythmia_class_per_beat",
    "sub_domain": "Cardiac electrical signal feature recovery with arrhythmia categorical readout",
    "title": "ECG Arrhythmia Classification (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "ecg_aami_pwdr_forward",
      "ecg_clinical_rhythm_pwdr_forward",
      "ecg_holter_pwdr_forward",
      "ecg_wearable_pwdr_forward"
    ],
    "allowed_omega_dimensions": [
      "N_leads",
      "sampling_rate_Hz",
      "duration_seconds",
      "voltage_resolution_uV",
      "SNR_dB",
      "lead_placement_error",
      "baseline_wander",
      "powerline_interference_60Hz",
      "muscle_artifact",
      "motion_artifact_wearable",
      "manual_cardiologist_disagreement"
    ],
    "allowed_problem_classes": [
      "ecg_aami_pwdr",
      "ecg_clinical_rhythm_pwdr",
      "ecg_arrhythmia_continuous_pwdr"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.85_accuracy",
      "forward_operator": "ecg_aami_pwdr_forward",
      "input_format": "ecg_signal_with_per_beat_label",
      "omega": {
        "N_leads": 12,
        "SNR_dB": 25,
        "baseline_wander": 0.0,
        "duration_seconds": 10,
        "lead_placement_error": 0.0,
        "manual_cardiologist_disagreement": 0.0,
        "motion_artifact_wearable": 0.0,
        "muscle_artifact": 0.0,
        "powerline_interference_60Hz": 0.0,
        "sampling_rate_Hz": 500,
        "voltage_resolution_uV": 5
      },
      "problem_class": "ecg_aami_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "N_leads": [
        1,
        12
      ],
      "duration_seconds": [
        5,
        86400
      ],
      "sampling_rate_Hz": [
        100,
        1000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Cardiac electrical signal feature recovery with arrhythmia categorical readout",
  "title": "ECG Arrhythmia Classification (PWDR)"
}

🎁

Earn PWM by promoting this to mainnet

• PWM emission on accept — your share of A(t) (1/16 modify · 3/16 new)
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Go to submit ↑

How: use the “Contribute & earn PWM” panel at the top — either Submit without uploading (one click) or upload your files. Then the review runs and, on accept, it’s promoted to mainnet.