SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features

by   Irfan Al-Hussaini, et al.

The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we propose a generalizable method to combine clinical interpretability with high accuracy derived from black-box deep learning. Clinician-determined sleep stages from polysomnogram (PSG) remain the gold standard for evaluating sleep quality. However, PSG manual annotation by experts is expensive and time-prohibitive. We propose SERF, interpretable Sleep staging using Embeddings, Rules, and Features to read PSG. SERF provides interpretation of classified sleep stages through meaningful features derived from the AASM Manual for the Scoring of Sleep and Associated Events. In SERF, the embeddings obtained from a hybrid of convolutional and recurrent neural networks are transposed to the interpretable feature space. These representative interpretable features are used to train simple models like a shallow decision tree for classification. Model results are validated on two publicly available datasets. SERF surpasses the current state-of-the-art for interpretable sleep staging by 2 classifier, SERF obtains 0.766 κ and 0.870 AUC-ROC, within 2 current state-of-the-art black-box models.


page 1

page 2

page 3

page 4


SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules

Sleep staging is a crucial task for diagnosing sleep disorders. It is te...

Performance and utility trade-off in interpretable sleep staging

Recent advances in deep learning have led to the development of models a...

A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture

In recent years, deep learning has shown potential and efficiency in a w...

Learning Optimized Or's of And's

Or's of And's (OA) models are comprised of a small number of disjunction...

The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy

We used neural networks in 3,000 sleep recordings from over 10 location...

Optimizing Rescoring Rules with Interpretable Representations of Long-Term Information

Analyzing temporal data (e.g., wearable device data) requires a decision...

Methods and Models for Interpretable Linear Classification

We present an integer programming framework to build accurate and interp...

Please sign up or login with your details

Forgot password? Click here to reset