A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep. (May 2023)

• Record Type: Journal Article
• Authors: Schipper, Fons; JG van Sloun, Ruud; Grassi, Angela; Overeem, Sebastiaan; Fonseca, Pedro
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Accelerometer -- Respiratory effort -- Sleep staging -- Deep learning -- Convolutional neural network -- Receptive field -- Adam stochastic optimization -- Principal component analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104726
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A chest-worn accelerometer was used by 146 participants during an overnight sleep recording. A neural network was automatically generated and trained to extract respiratory activity. Reference and predicted respiratory waveforms were automatically aligned. The estimation error was halved compared to Principal Component Analysis. Differences in the respiratory signal between sleep stages could be reproduced. 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.