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

Authors:

Irfan Al-Hussaini,

Cassie S. MitchellAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 3791 - 3795

https://doi.org/10.1145/3511808.3557700

Published: 17 October 2022 Publication History

Abstract

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%. Using Gradient Boosted Trees as the classifier, SERF obtains 0.766 κ and 0.870 AUC-ROC, within 2% of the current state-of-the-art black-box models.

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Cited By

Liu HValderrama CZhang XLiu Q(2024)MPSleepNet: Matrix Profile-Guided Transformer for Multi-Channel Sleep ClassificationProceedings of the 2024 7th International Conference on Signal Processing and Machine Learning10.1145/3686490.3686519(195-201)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3686490.3686519
Soleimani RBarahona JChen YBozkurt ADaniele MPozdin VLobaton E(2023)Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic ReviewPhysiologia10.3390/physiologia40100014:1(1-42)Online publication date: 20-Dec-2023
https://doi.org/10.3390/physiologia4010001
Al-Hussaini IMitchell C(2023)SeizFt: Interpretable Machine Learning for Seizure Detection Using WearablesBioengineering10.3390/bioengineering1008091810:8(918)Online publication date: 2-Aug-2023
https://doi.org/10.3390/bioengineering10080918
Show More Cited By

Index Terms

SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning algorithms

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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October 17 - 21, 2022

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Cited By

Liu HValderrama CZhang XLiu Q(2024)MPSleepNet: Matrix Profile-Guided Transformer for Multi-Channel Sleep ClassificationProceedings of the 2024 7th International Conference on Signal Processing and Machine Learning10.1145/3686490.3686519(195-201)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3686490.3686519
Soleimani RBarahona JChen YBozkurt ADaniele MPozdin VLobaton E(2023)Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic ReviewPhysiologia10.3390/physiologia40100014:1(1-42)Online publication date: 20-Dec-2023
https://doi.org/10.3390/physiologia4010001
Al-Hussaini IMitchell C(2023)SeizFt: Interpretable Machine Learning for Seizure Detection Using WearablesBioengineering10.3390/bioengineering1008091810:8(918)Online publication date: 2-Aug-2023
https://doi.org/10.3390/bioengineering10080918
Al-Hussaini IMitchell C(2023)Towards Interpretable Seizure Detection Using WearablesICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10097091(1-2)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10097091

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