ArXiv Preprint
Recent advances in deep learning have led to the development of models
approaching human level of accuracy. However, healthcare remains an area
lacking in widespread adoption. The safety-critical nature of healthcare
results in a natural reticence to put these black-box deep learning models into
practice. In this paper, we explore interpretable methods for a clinical
decision support system, sleep staging, based on physiological signals such as
EEG, EOG, and EMG. A recent work has shown sleep staging using simple models
and an exhaustive set of features can perform nearly as well as deep learning
approaches but only for certain datasets. Moreover, the utility of these
features from a clinical standpoint is unclear. On the other hand, the proposed
framework, NormIntSleep shows that by representing deep learning embeddings
using normalized features, great performance can be obtained across different
datasets. NormIntSleep performs 4.5% better than the exhaustive feature-based
approach and 1.5% better than other representation learning approaches. An
empirical comparison between the utility of the interpretations of these models
highlights the improved alignment with clinical expectations when performance
is traded-off slightly.
Irfan Al-Hussaini, Cassie S. Mitchell
2022-11-07
