# Mit Bih Ecg Class Eval

> Evaluates deep learning architectures for multi-class ECG heartbeat classification, specifically testing their ability to handle severe class imbalance and capture temporal cardiac waveform features. Use when the user wants to benchmark on MIT-BIH Arrhythmia Database, or asks about evaluating this task. Reports F1-score.

- Skill: `qhjqhj00/mit-bih-ecg-class-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/mit-bih-ecg-class-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/mit-bih-ecg-class-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/mit-bih-ecg-class-eval

---


# mit-bih-ecg-class-eval

> Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation — Yun Song et al. (2026) (arXiv:2602.17701, 2026)

## What this evaluates

Evaluates deep learning architectures for multi-class ECG heartbeat classification, specifically testing their ability to handle severe class imbalance and capture temporal cardiac waveform features.

## Datasets

- **MIT-BIH Arrhythmia Database** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `F1-score` **(primary)** — range: [0, 1]
  - Harmonic mean of Precision and Recall: 2 * (Precision * Recall) / (Precision + Recall). Used as the headline metric to balance sensitivity and specificity under class imbalance.
- `Accuracy` — range: [0, 1]
  - Proportion of correctly classified samples out of the total number of samples.
- `Macro-average AUC` — range: [0, 1]
  - Average of the Area Under the Receiver Operating Characteristic Curve computed independently for each arrhythmia class, ensuring equal weighting across all categories.

## Input / output format

**Input**: Preprocessed ECG waveform segments (heartbeats) representing temporal cardiac signals.

**Output**: Predicted arrhythmia class label (e.g., Normal, Atrial Premature, Fusion of ventricular and normal).

## Scoring recipe

```python
def compute_f1(y_true, y_pred):
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == p)
    precision = tp / sum(1 for p in y_pred if p == 1)
    recall = tp / sum(1 for t in y_true if t == 1)
    return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
```

## Common pitfalls

- Class imbalance heavily skews Accuracy and Precision; F1-score or macro-averaged metrics must be prioritized for fair comparison.
- Macro-average AUC requires per-class ROC curves and probability outputs, not just hard predictions.
- Confusion matrix off-diagonal elements often reveal morphologically similar classes (e.g., Normal vs. Atrial Premature) that are frequently misclassified.

## Evidence (verbatim from paper)

> To quantify classification performance, the study adopted standard evaluation metrics, including Accuracy, Precision, Recall, and F1-score, along with Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) analyses. Accuracy measures the overall proportion of correctly classified samples, while Precision and Recall capture the trade-off between false positives and false negatives. The F1-score, defined as the harmonic mean of Precision and Recall, provides a balanced measure of sensitivity and specificity, making it particularly suitable for the imbalanced class distribution characteristic of ECG datasets.

## Citation

```bibtex
@misc{song2026deepneural,
  title={Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation},
  author={Yun Song et al. (2026)},
  year={2026},
  note={arXiv:2602.17701}
}
```

- arXiv: 2602.17701