Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling. - Nathaniel Diamant; Erik Reinertsen; Steven Song; Aaron D. Aguirre; Collin M. Stultz; Puneet Batra (CoRR, 2021)

Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling. - Nathaniel Diamant; Erik Reinertsen; Steven Song; Aaron D. Aguirre; Collin M. Stultz; Puneet Batra (PLoS Comput. Biol., 2022)

Machine Learning for Arterial Blood Pressure Prediction. - Jessica Zheng; Hanrui Wang 002; Anand Chandrasekhar; Aaron D. Aguirre; Song Han 003; Hae-Seung Lee; Charles G. Sodini (CHIL, 2023)

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG. - Ridwan Alam; Aaron D. Aguirre; Collin M. Stultz (EMBC, 2023)

Detecting QT prolongation From a Single-lead ECG With Deep Learning. - Ridwan Alam; Aaron D. Aguirre; Collin M. Stultz (CoRR, 2024)

Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring. - Thomas Kite; Uzair Tahamid Siam; Brian Ayers; Nicholas Houstis; Aaron D. Aguirre (CoRR, 2024)