README.md

---
license: mit
---
# FSL ECG QA Dataset

**FSL ECG QA Dataset** is a **benchmark dataset** used in paper [*"Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning"*] (https://arxiv.org/abs/2410.14464). It supports research in combining **electrocardiogram (ECG) signals** with **natural language question answering (QA)**, particularly in **few-shot** and **meta-learning** scenarios.

## Dataset Highlights

- 🧠 **Task Diversification**: Restructured **ECG-QA tasks** promote rapid **few-shot adaptation**.
- 🧬 **Fusion Mapping**: A lightweight **multimodal mapper** bridges **ECG** and **language features**.
- 🚀 **Model Generalization**: **LLM-agnostic design** ensures broad **transferability** and **robustness**.

# 1. Datasets

## 1.1 Load the classes

```python
python load_class.py --base_path /your/actual/path/to/ecgqa/ptbxl/paraphrased --test_dataset ptb-xl
python load_class.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
(all tested mimic-iv-ecg dataset is listed in "data/processed_test_30k.json")
```
## 1.2 Load ECG QA FSL dataset

```python
python data_loader.py
--model_name /your/actual/path/to/model/download/from/hugging/face
--paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased
--test_dataset ptb-xl
```

## 1.3 Sample of ECG QA FSL dataset

```python
episode = {
    # --- Support Set ---
    "support_x": [  # ECG feature tensors or preprocessed arrays
        ecg_sample_1,  # typically a NumPy array or tensor
        ecg_sample_2,
        # ...
    ],
    "support_y_q": [  # Question token sequences (padded)
        [12, 45, 78, 0, 0, 0],
        [23, 67, 89, 90, 0, 0],
        # ...
    ],
    "support_y_a": [  # Answer token sequences (padded)
        [1, 0, 0, 0, 0, 0],
        [1, 1, 0, 0, 0, 0],
        # ...
    ],
    "support_y_q_mask": [  # Mask for question tokens (1 = valid, 0 = pad)
        [1, 1, 1, 0, 0, 0],
        [1, 1, 1, 1, 0, 0],
        # ...
    ],
    "support_y_a_mask": [  # Mask for answer tokens
        [1, 0, 0, 0, 0, 0],
        [1, 1, 0, 0, 0, 0],
        # ...
    ],
    "flatten_support_x": [  # File paths to raw ECG signals
        "/path/to/support/ecg_1.npy",
        "/path/to/support/ecg_2.npy",
        # ...
    ],

    # --- Query Set ---
    "query_x": [
        ecg_query_1,
        ecg_query_2,
        # ...
    ],
    "query_y_q": [
        [34, 78, 56, 0, 0, 0],
        [90, 12, 45, 76, 0, 0],
        # ...
    ],
    "query_y_a": [
        [0, 1, 1, 0, 0, 0],
        [1, 0, 0, 0, 0, 0],
        # ...
    ],
    "query_y_q_mask": [
        [1, 1, 1, 0, 0, 0],
        [1, 1, 1, 1, 0, 0],
        # ...
    ],
    "query_y_a_mask": [
        [1, 1, 1, 0, 0, 0],
        [1, 0, 0, 0, 0, 0],
        # ...
    ],
    "flatten_query_x": [
        "/path/to/query/ecg_1.npy",
        "/path/to/query/ecg_2.npy",
        # ...
    ],
}
```

### 1.4 Class distribution

| Question Type   | Attributes | Answers                          | Classes (train:test) | Samples | Example                                                                 |
|-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
| Single-Verify   | 94         | yes/no                           | 156 (124:32)          | 34,105  | Q: Does this ECG show 1st degree av block? <br> A: yes/no               |
| Single-Choose   | 165        | both/none/attr_1/attr_2          | 262 (209:53)          | 47,655  | Q: Which noise does this ECG show, baseline drift or static noise? <br> A:  baseline drift /static noise   |
| Single-Query    | 30         | attr_1/attr_2/.../attr_n         | 260 (208:52)          | 63,125  | Q: What direction is this ECG deviated to? <br> A: Normal axis/...     |
| All             | 206        | yes/no/both/none/.../attr_n      | 678 (541:137)         | 144,885 | ...                                                                     |

# 2. Citation

```
@InProceedings{pmlr-v287-tang25a,
  title = {Electrocardiogram--Language Model for Few-Shot Question Answering with Meta Learning},
  author = {Tang, Jialu and Xia, Tong and Lu, Yuan and Mascolo, Cecilia and Saeed, Aaqib},
  booktitle = {Proceedings of the Sixth Conference on Health, Inference, and Learning},
  pages = {89--104},
  year = {2025},
  editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene},
  volume = {287},
  series = {Proceedings of Machine Learning Research},
  month = {25--27 Jun},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v287/tang25a.html}
}
```