This week, our team officially kicked off Project BEATNET with a meeting attended by Aventusoft’s liaisons, Dr. Keider Hoyos and Dr. Diego Pava, along with our faculty advisor, Prof. Kejun Huang. The meeting established the foundation for our collaboration by clearly defining the project’s objectives and expectations. Aventusoft highlighted three key focus areas for our work: landmark detection, disease state classification, and system deployment. Our task is to develop AI models that can accurately identify PQRS-T waveforms in both normal and abnormal ECG signals, as well as classify various conditions, including arrhythmias, conduction abnormalities, pacemaker types, and signs of electrolyte imbalance. A distinctive feature of the project is to create both comprehensive models for cloud-based inference and streamlined models optimized for mobile device performance.

At the beginning of the meeting, the liaisons highlighted the importance of data. Ultimately, we will receive single-lead, 30-second ECG recordings in periodic batches, provided in MATLAB or NumPy formats, along with limited demographic details. Meanwhile, our team is expected to actively search for and use publicly available datasets, such as those from PhysioNet and MIT-BIH, to start initial model development while Aventusoft finalizes data sharing agreements. We are fully responsible for cleaning and preprocessing all the data.

From a technical standpoint, Aventusoft instructed us to use Python as our main programming language and PyTorch for all deep learning tasks, explicitly advising against TensorFlow due to previous compatibility issues. Our approach will begin with an extensive review of the latest research on single-lead, short-duration ECG analysis, adapting and fine-tuning existing models to fit the specific requirements of our project. We are also encouraged to apply semi-supervised learning methods to utilize unlabeled data for improved results.

A particularly important topic discussed was documentation. Aventusoft requires FDA-style documentation from all team members, including specifications of requirements, descriptions of system and algorithm designs, risk assessments, and verification and validation procedures. They emphasized that the quality of documentation is just as critical as the quality of the code for the project’s success and regulatory compliance.

Looking ahead, the immediate priorities for the upcoming week are to identify and evaluate suitable public ECG datasets, start an in-depth literature review of methodologies relevant to our project, brainstorm and document preprocessing strategies, and thoughtfully assign sub-tasks among team members. With clear direction from our liaisons and defined next steps, our team is excited to move from the planning phase into concrete research and development. We look forward to sharing our progress in the coming weeks!