A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep

Fons Schipper (Corresponding author), Ruud J.G. van Sloun, Angela Grassi, Sebastiaan Overeem, Pedro Fonseca

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
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Abstract

Objective: The objective is to develop a new deep learning method for the estimation of respiratory effort from a chest-worn accelerometer during sleep. We evaluate performance, compare it against a state-of-the art method, and assess whether it can differentiate between sleep stages. Methods: In 146 participants undergoing overnight polysomnography data were collected from an accelerometer worn on the chest. The study data were partitioned into train, validation, and holdout (test) sets. We used the train and validation sets to generate and train a convolutional neural network and performed model selection respectively, while we used the holdout set (72 participants) to evaluate performance. Results: A convolutional neural network with 9 layers and 207,855 parameters was automatically generated and trained. The neural network significantly outperformed the best performing conventional method, based on Principal Component Analysis; it reduced the Mean Squared Error from 0.26 to 0.11 and it also performed better in the detection of breaths (Sensitivity 98.4 %, PPV 98.2 %). In addition, the neural network exposed significant differences in characteristics of respiratory effort between sleep stages (p < 0.001). Conclusion: The deep learning method predicts respiratory effort with low error and is sensitive and precise in the detection of breaths. In addition, it reproduces differences between sleep stages, which may enable automatic sleep staging, using just a chest-worn accelerometer.

Original languageEnglish
Article number104726
Number of pages12
JournalBiomedical Signal Processing and Control
Volume83
DOIs
Publication statusPublished - May 2023

Bibliographical note



Publisher Copyright:
© 2023 The Author(s)

Funding

This work has been performed in the IMPULS framework of the Eindhoven MedTech Innovation Center (e/MTIC, incorporating Eindhoven University of Technology, Philips Research, and Sleep Medicine Center Kempenhaeghe). The funders had no role in the study design, decision to publish, or preparation of the manuscript. Fons Schipper, Pedro Fonseca, and Angela Grassi are employed by Philips Research. The employer had no influence on the study and on the decision to publish. Ruud van Sloun is employed by both Philips Research and by the Eindhoven University of Technology. The other authors declare no competing interests.

FundersFunder number
Sleep Medicine Centre Kempenhaeghe
Eindhoven University of Technology

    Keywords

    • Accelerometer
    • Adam stochastic optimization
    • Convolutional neural network
    • Deep learning
    • Principal component analysis
    • Receptive field
    • Respiratory effort
    • Sleep staging

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