Update README.md

README.md

@@ -13,25 +13,7 @@ license: mit
  
 ## Source Datasets
  
-The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as PTB-XL and MIMIC-IV-ECG, and organizes them into diverse task sets based on question types (e.g., binary, multiple-choice, and query-based) and clinical attributes (e.g., SCP codes, noise type, axis deviation). This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
- 
-- **ICBHI Respiratory Sound Dataset**  
-  download from the official ICBHI challenge website: [ICBHI 2017 Respiratory Sound Dataset](https://bhichallenge.med.auth.gr)  
- 
- 
-- **KAUH Respiratory Dataset**  
-  download from [Mendeley Data](https://data.mendeley.com/datasets/jwyy9np4gv/3)  
-  Place the audio files in the `Audiofiles` folder under this directory.
- 
-- **CirCor Pediatric Heart Sound Dataset**  
-  download from [Kaggle](https://www.kaggle.com/datasets/bjoernjostein/the-circor-digiscope-phonocardiogram-dataset-v2)
- 
-- **SPRSound Pediatric Respiratory Dataset**  
-  download from [GitHub](https://github.com/SJTU-YONGFU-RESEARCH-GRP/SPRSound)
- 
-- **ZCHSound Pediatric Heart Sound Dataset**  
-  download from [ZCHSound](http://zchsound.ncrcch.org.cn)
- 
+  The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/), and [ECG-QA dataset](https://github.com/Jwoo5/ecg-qa?tab=readme-ov-file) organizes them into diverse task sets based on question types including verify(yes/no), choice(Condition_A/Condition_B), and query(open-ended) question. and clinical attributes (e.g., SCP codes, noise type, axis deviation) used to describing the ECG. This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
 
 To utilize this dataset, the authors propose a novel multimodal meta-learning framework that integrates a frozen ECG encoder, a frozen language model (e.g., LLaMA or Gemma), and a trainable cross-modal fusion module. This setup effectively aligns ECG signals with natural language queries to enable accurate clinical question answering.