Update README.md

README.md

@@ -6,4 +6,15 @@ license: apache-2.0
 
 Code repository: https://github.com/alexmschubert/ACS-BenchWork
 
+## Project description
+In this project, we benchmarked various machine learning (ML) approaches for detecting acute coronary syndrome (ACS) - commonly known as 'heart attack' - from 12-lead ECG waveforms. Our findings reveal that ML models can successfully identify large groups of high-risk patients who show no classical ECG features (e.g., ST-elevation or depression) typically associated with ACS by cardiologists. We are releasing the weights of our best-performing models to facilitate further development in data-driven ACS detection. The training dataset, ACS-BenchWork, is available on [Nightingale Open Science](https://docs.ngsci.org/datasets/ed-bwh-ecg/), and we invite researchers to build on these resources to improve the accuracy and utility of ECG-based ACS screening tools.
+
+## Models
+
+
+
+## Usage
+
+
+## Citation