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Your guideDr. Kongdaka puppy in a doctor's coat
Intermediate · ECGby KNOWVERSE
ECG Deep Learning Hands-on — Building Biosignal AI Models with the MIT-BIH Arrhythmia Dataset
A practical, ethics-first path from WFDB signal processing to 1D CNN classification and patient-level evaluation.
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M0–M2 are free after sign-in
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Foundation track (M0–M2)
M0
Research Ethics and Licensing
Distinguish open from credentialed data, preserve attribution, and follow a practical credentialing checklist.
Distinguish open from credentialed data, preserve attribution, and follow a practical credentialing checklist.
M1
Reading and Visualizing Signals with WFDB
Understand the WFDB format and safely download, inspect, and visualize MIT-BIH records.
Understand the WFDB format and safely download, inspect, and visualize MIT-BIH records.
M2
ECG Preprocessing
Understand baseline wander, band-pass filtering, resampling, and common evaluation traps.
Understand baseline wander, band-pass filtering, resampling, and common evaluation traps.
C0
Getting Started with Colab
From the Google Colab interface to your first ECG plot, with slides and English narration.
From the Google Colab interface to your first ECG plot, with slides and English narration.
Deep-dive track (M3–M8)
M3
R-peak Detection (Pan–Tompkins)
Build the R-peak detection pipeline and validate it against reference annotations.
Build the R-peak detection pipeline and validate it against reference annotations.
M4
AAMI Five-class Classification (1D CNN)
Design a 1D CNN experiment that accounts for severe class imbalance.
Design a 1D CNN experiment that accounts for severe class imbalance.
M5
The Inter-patient Split Trap
Reproduce the misleading performance caused by patient overlap.
Reproduce the misleading performance caused by patient overlap.
M6
RR-interval Rhythm Classification
Design an RR-interval rhythm-classification workflow with AFDB.
Design an RR-interval rhythm-classification workflow with AFDB.
M7
Signal Quality Assessment
Check signal quality and missingness before analysis.
Check signal quality and missingness before analysis.
M8
Reproducibility and Experiment Management
Record data versions, splits, parameters, and results for reproducible experiments.
Record data versions, splits, parameters, and results for reproducible experiments.