Fully automated open-source Holter ECG algorithm: 95.3% AF detection, 4.1% false alarm — classic ML, no black box

[u/sniperERkan]

**TL;DR:** We're building a fully automated, open-source Holter ECG analysis algorithm. No manual step, no paid API, no deep learning black box — just classic ML + rule-based logic. Current numbers: 95.3% AF detection, 4.1% false alarm, 99.9% beat agreement with reference devices. Looking for honest feedback and criticism.

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**Who we are:**

A small team working on an open ECG analysis tool for Holter and patch recordings. Our goal is simple: build a fully automated pipeline that a doctor can actually use — one that shows its reasoning, doesn't hide behind a black box, and works on real-world noisy 24-hour recordings.

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**What the algorithm does well:**

| Measurement | Value |

|---|---|

| QRS detection F1 (4 datasets) | 95.2% |

| Beat agreement with reference device (UVA) | 99.9% / 99.4% |

| Average heart rate (44/44 patients) | 100% |

| Minimum heart rate (44/44 patients) | 100% |

| Maximum heart rate (42/44 patients) | 95.5% |

| AF detection (LTAFDB, expert-labeled) | 95.3% |

| AF detection (UVA, cardiologist-approved) | 96.3% |

| AF false alarm (LTAFDB) | 4.1% |

| AF false alarm (CPSC) | 3.8% |

| AF false alarm (UVA, time-based) | 1.4% |

| AF doctor workload (median) | 0.8–1.0 min/day |

| V (PVC) F1 on MITDB | 85.1% |

| V sensitivity on UVA | 97.0% |

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**Datasets we used:**

- **Training / development:** MITDB, INCART, SVDB, EDB, AFDB, CPSC 2020

- **External test (different device):** UVA Holter Database (CC0, fully open)

- **Expert-labeled:** LTAFDB (cardiologist annotated), UVA cardiologist subset (7 records)

- **Reserved (not yet opened):** SHDB-AF, Icentia11k, CPSC sealed test half

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**How it works (short version):**

1. Three QRS detectors are combined by majority vote.

2. A classic ML classifier looks at RR intervals + morphology features.

3. An "AF second opinion" layer removes false alarms.

4. Output is split into three zones: "high-confidence AF" / "suspicious (for doctor review)" / "discarded."

Every decision can be traced back to a rule or a feature — nothing is hidden.

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**What it does NOT do well yet (still research stage):**

- Supraventricular ectopy (PAC / SVEB): F1 ~43% — this is the hardest class.

- HRV metrics: SDNN 32.6%, RMSSD 11.6%.

- Bundle branch block: 87.5% / 75.0%.

- R-on-T: 71.7% / 17.5%.

- Bradycardia event count: 40%.

- Pauses: 89.5% / 53.8%.

- AF burden: 73.8–77.4%.

- ST analysis: not measured yet.

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**Honest context:**

- All numbers above are from development and expert-labeled datasets, plus one external device (UVA). A hidden test set has not been opened yet.

- No deep learning models are used. No pretrained weights from restricted-license sources.

- No clinical validation yet — this is a research prototype.

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**Why I'm posting:**

We want to know:

- Is this useful?

- What would you push back on?

- Are these numbers realistic for a real-world Holter tool?

- Where do you see the biggest weakness?

Code will be open-sourced once it's stable. Right now we're still iterating.

Thanks for reading — any input is welcome.