ABC Farma - Artificial Intelligence Doctor

How to Label 100 Complex ECG Strips (Clinical + AI Workflow)

Labeling 100 complex ECG strips requires a workflow designed for precision, speed, and consistency—especially if your goal is diagnostic accuracy, pacemaker evaluation, or training an AI model. Below is the most efficient method used in modern AI-assisted cardiology datasets.

1. Define the Labels You Will Use

The most common and clinically meaningful ECG labels are:

• NSR – Normal sinus rhythm
• SB – Sinus bradycardia
• ST – Sinus tachycardia
• AF – Atrial fibrillation
• AFL – Atrial flutter
• PVC – Ventricular ectopy / frequent PVCs
• Paced – Pacemaker rhythm
• AV Block – 1º / 2º / 3º
• Noise / Artifact
• Unknown rhythm (if insufficient clarity)

For pacemaker datasets (Aveir VR, Micra), it is valuable to label pacing behavior separately.

2. Use a Standardized ECG Labeling Template

This template ensures consistency and is suitable for training machine-learning models:

ECG_ID:
Sampling rate:
Lead(s):
Duration:

Primary Rhythm Label:
Secondary Rhythm Labels:
Beat-by-beat anomalies:
P-wave description:
PR interval:
QRS duration:
QT/QTc:
Pacing spikes? (Y/N):
Capture failure? (Y/N):
Reasoning (concise):
Uncertainty level (0–5):

Pacing artifact morphology:
True vs pseudo-loss-of-capture indicators:
Fusion beats? (Y/N):

3. The 4-Step AI-Accelerated Workflow

Step 1 — AI Pre-Labeling

Use an LLM or ECG classifier to produce:

• Primary rhythm label
• Secondary findings
• Pacing behavior
• Beat anomalies
• Short reasoning
• Confidence score

Step 2 — Sort by Confidence

• High (≥80%) → Accept with brief review
• Medium (50–79%) → Human correction
• Low (<50%) → Manual labeling required

This typically reduces human work by 60–80%.

Step 3 — Human Correction

Correct errors such as:

• Misdetection of P waves
• Confusion between AF vs sinus arrhythmia
• PVC vs fusion vs pseudofusion beats
• Pacing spike misinterpretation
• Non-capture vs undersensing vs artifact

Step 4 — Consistency Check

Every 20 strips, review labels and ensure definitions remain consistent across the whole dataset.

4. How to Label Pacemaker-Specific ECG Strips

Leadless pacemakers produce characteristic patterns, especially Aveir VR:

Look for:

• Tiny pacing spikes, sometimes blending into baseline noise
• Fusion beats (pacing + intrinsic beat)
• Pseudoloss-of-capture (artifact)
• True loss-of-capture (spike → no QRS + pause)

Recommended labels:

• Spike + Capture
• Spike + True Non-Capture
• Spike + Artifact Mimicking Non-Capture
• Intrinsic conduction with intermittent pacing

This makes your dataset extremely valuable for both clinical and AI applications.

5. What I Can Generate for You

If you want, I can create:

• A complete labeling guideline PDF
• An annotation spreadsheet (Excel / Google Sheets)
• A master AI prompt optimized for ECG labeling
• A confidence-based sorting workflow
• A pacemaker-specific labeling subset

6. Next Step

Would you like me to produce:

• A) A labeling template in table format
• B) The AI pre-labeling prompt
• C) A worked example with one ECG strip
• D) An annotation spreadsheet
• E) All of the above

Just tell me A, B, C, D or E.