File size: 4,355 Bytes
16a6294 d08f5d9 468ddfd 16a6294 468ddfd 5fc8125 468ddfd 16a6294 8bab1ee 16a6294 468ddfd bea3f8e 468ddfd 5fc8125 468ddfd 35b1cb2 16a6294 7aa7a1f 5ef76b7 35b1cb2 468ddfd 35b1cb2 7aa7a1f 35b1cb2 468ddfd 7aa7a1f 696fc5c 35b1cb2 468ddfd 35b1cb2 696fc5c 5ef76b7 696fc5c 16a6294 35b1cb2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
---
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
```
@article{tang2024electrocardiogram,
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},
journal={arXiv preprint arXiv:2410.14464},
year={2024}
}
```
|