This week, our BEATNET team dedicated efforts to building a solid foundation for our ECG deep learning project. We dived into the required documentation, mapping out the project’s scope, stakeholders, and benefits, while drafting weekly status reports and project diagrams to guide our process. A key focus was clarifying our data situation through a detailed session with our company liaison. We learned that Aventusoft’s proprietary ECG data is largely unlabeled, with annotations and clinical info available for only a fraction of patients—and with columns varying across different datasets. Most of the annotated data supports seismocardiogram (SCG) research rather than the ECG deep learning tasks we’re pursuing.

Given these findings, our immediate priorities are twofold: designing robust ECG landmark detection algorithms resilient to noise and diverse conditions, and developing models to identify arrhythmias and conduction abnormalities. To accomplish this, we’ll use well-annotated public datasets like PTB-XL and MIT-BIH as our starting point. Next week, we’ll shift into hands-on exploration of these public datasets, refining our preprocessing pipelines, and aligning our workflow with industry best practices. With the planning phase wrapping up, we’re eager to start coding and build out the core baseline models for BEATNET.