Sepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine. - Matthieu Scherpf; Miriam Goldammer; Hagen Malberg; Felix Gräßer (CinC, 2019)

Predicting sepsis with a recurrent neural network using the MIMIC III database. - Matthieu Scherpf; Felix Gräßer; Hagen Malberg; Sebastian Zaunseder (Comput. Biol. Medicine, 2019)

DeepPerfusion: Camera-based Blood Volume Pulse Extraction Using a 3D Convolutional Neural Network. - Matthieu Scherpf; Hannes Ernst; Hagen Malberg; Martin Schmidt 004 (CinC, 2020)

Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction. - Alexander Hammer; Matthieu Scherpf; Hannes Ernst; Jonas Weiß; Daniel Schwensow; Martin Schmidt 004 (CinC, 2021)

Skin Segmentation for Imaging Photoplethysmography Using a Specialized Deep Learning Approach. - Matthieu Scherpf; Hannes Ernst; Leo Misera; Hagen Malberg; Martin Schmidt 004 (CinC, 2021)

Optimal color channel combination across skin tones for remote heart rate measurement in camera-based photoplethysmography. - Hannes Ernst; Matthieu Scherpf; Hagen Malberg; Martin Schmidt 004 (Biomed. Signal Process. Control., 2021)

Improved Pulse Pressure Estimation Based on Imaging Photoplethysmographic Signals. - Matthieu Scherpf; Hagen Malberg; Martin Schmidt 004 (CinC, 2022)

Assessing Brain Dynamics for Predicting Postanoxic Coma Recovery: An EEG Based Approach. - Marc Goettling; Richard Hohmuth; Franz Ehrlich; Hannes Ernst; Alexander Hammer; Matthieu Scherpf; Martin Schmidt 004 (CinC, 2023)

DeepPerfusion: A comprehensible two-branched deep learning architecture for high-precision blood volume pulse extraction based on imaging photoplethysmography. - Matthieu Scherpf; Hannes Ernst; Hagen Malberg; Martin Schmidt 004 (Comput. Biol. Medicine, 2025)