Publications by authors named "Gabriel Krummenacher"
Article Synopsis
- Understanding sleep is crucial for biomedical research, and traditional methods for analyzing EEG data are time-consuming and inconsistent.
- Recent developments in machine learning aim to automate EEG classification; however, they struggle with generalizing across different studies and animal models.
- The SPINDLE system uses a convolutional neural network and a hidden Markov model to accurately classify sleep states, achieving high agreement with human experts and allowing standardized analysis across various experimental conditions, now available for public use.
Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data.
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