# Ecg Classification Eval

> Evaluates the ability of deep learning architectures to accurately classify electrocardiogram (ECG) recordings into predefined physiological or pathological categories. The benchmark probes joint time-frequency feature extraction capabilities by comparing models that embed Fourier analysis directly into convolutional layers against traditional signal processing and baseline CNN approaches. Use when the user wants to benchmark on MIT-BIH, ECG-ID, Apnea-ECG, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/ecg-classification-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ecg-classification-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ecg-classification-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/ecg-classification-eval

---


# ecg-classification-eval

> Convolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification — Jeong et al. (2025) (arXiv:2502.00497, 2025)

## What this evaluates

Evaluates the ability of deep learning architectures to accurately classify electrocardiogram (ECG) recordings into predefined physiological or pathological categories. The benchmark probes joint time-frequency feature extraction capabilities by comparing models that embed Fourier analysis directly into convolutional layers against traditional signal processing and baseline CNN approaches.

## Datasets

- **MIT-BIH** — total ?; splits: train (-1), val (-1), test (-1)
- **ECG-ID** — total ?; splits: train (-1), val (-1), test (-1)
- **Apnea-ECG** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: percent
  - The proportion of correctly classified ECG signals out of the total number of signals in the evaluation set. Calculated as (number of correct predictions) / (total number of predictions) and reported as a percentage.

## Input / output format

**Input**: 1D ECG time-series signals (typically preprocessed, normalized, and segmented into fixed-length windows for the CNN architecture).

**Output**: Predicted class label for each ECG recording.

## Scoring recipe

```python
def calculate_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / len(gold_labels)) * 100
```

## Common pitfalls

- The provided text does not specify the exact train/val/test split ratios or cross-validation fold configuration, only mentioning 'per fold' timing in the results table.
- Statistical significance testing (p ≤ 0.02) is reported to validate improvements over baselines, but the specific statistical test (e.g., paired t-test, Wilcoxon signed-rank) is not detailed in the snippet.

## Evidence (verbatim from paper)

> CFAN outperforms state-of-the-art methods (SPECT, CNN1D, FFT1D, CNN1D-FAN) across ECG classification tasks—achieving 98.95% accuracy on MIT-BIH, 96.83% on ECG-ID, and 95.01% on Apnea-ECG—with statistically significant improvements (p ≤ 0.02) over the second-best method in two tasks.

## Citation

```bibtex
@misc{jeong2025cfan,
  title={Convolutional Fourier Analysis Network (CFAN): A Unified Time-Frequency Approach for ECG Classification},
  author={Jeong et al. (2025)},
  year={2025},
  note={arXiv:2502.00497}
}
```

- arXiv: 2502.00497