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ECG SQI Fusion

Reproducing and extending classical ECG signal-quality assessment

This project asks whether classical signal-quality-index (SQI) fusion can be functionally reproduced from public data, and what is lost when ECG quality is compressed into whole-record summary features. It compares interpretable SQI models with temporally local SQIs and waveform networks on 12-lead Set-A and single-lead BUT QDB.

The repository is an open-science companion to the final report. It contains the analysis code, four frozen inference models, tests, Docker definitions, and clean-room reproduction entry points.

flowchart LR
  A[Public ECG data] --> B[Preprocessing and QRS detection]
  B --> C[Seven SQI families across 12 leads]
  C --> D[Classical SQI fusion]
  B --> E[Local windows and waveform views]
  E --> F[ResNet and Conformer]
  D --> G[Reproduction and domain audit]
  F --> G

Main contribution

The classical fusion trend was recovered, but fixed synthetic class balancing created an easily learned source shortcut. Train-only support-aware candidate construction reduced that mismatch. Local SQIs improved the difficult Good-Medium boundary, and waveform models retained additional local evidence that fixed summaries discarded.

Evidence Result Interpretation
Reproduced five-SQI RBF-SVM accuracy 0.948 The strongest predefined paper subset was functionally reproduced.
Synthetic/native poor-source classifier AUC 0.974 Fixed synthetic poor records remained strongly distinguishable from native poor ECGs.
Poor recall at 95% acceptable specificity 0.0759 to 0.7321 Support-aware construction repaired most of the fixed-noise failure.
Set-A Full Conformer accuracy 0.9128 Best selected operating-point accuracy in the frozen Set-A comparison.
BUT Full Conformer macro-F1 0.9398 Strong graded single-lead performance, but statistically comparable with matched ResNet.

These values are taken from the submitted report. They are not claims of clinical readiness; see limitations.

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