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Title: | Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning | Authors: | VAN STEENKISTE, Niels Groenendaal, Willemijn DREESEN, Pauline Lee, Seulki Klerkx, Susie de Francisco, Ruben Deschrijver, Dirk Dhaene, Tom |
Issue Date: | 2020 | Publisher: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Source: | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 24 (9) , p. 2589 -2598 | Abstract: | Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an overnight sleep study in a specialized sleep clinic. This setup is expensive and the number of beds and staff are limited, leading to a long waiting time. To enable more patients to be tested, and repeated monitoring for diagnosed patients, portable sleep monitoring devices are being developed. These devices automatically detect sleep apnea events in one or more respiration-related signals. There are multiple methods to measure respiration, with varying levels of signal quality and comfort for the patient. In this study, the potential of using the bio-impedance (bioZ) of the chest as a respiratory surrogate is analyzed. A novel portable device is presented, combined with a two-phase Long Short-Term Memory (LSTM) deep learning algorithm for automated event detection. The setup is benchmarked using simultaneous recordings of the device and the traditional polysomnography in 25 patients. The results demonstrate that using only the bioZ, an area under the precision-recall curve of 46.9% can be achieved, which is on par with automatic scoring using a polysomnography respiration channel. The sensitivity, specificity and accuracy are 58.4%, 76.2% and 72.8% respectively. This confirms the potential of using the bioZ device and deep learning algorithm for automatically detecting sleep respiration events during the night, in a portable and comfortable setup. | Notes: | Van Steenkiste, T (corresponding author), Univ Ghent, Dept Informat Technol, IMEC, IDLab, B-9052 Ghent, Belgium. tomd.vansteenkiste@ugent.be; groenendaal@imec-nl.nl; pauline.dreesen@zol.be; seulki.lee@imec-nl.nl; susie.klerkx@zol.be; ruben.defrancisco@oneramedical.com; dirk.deschrijver@ugent.be; tom.dhaene@ugent.be |
Other: | Van Steenkiste, T (corresponding author), Univ Ghent, Dept Informat Technol, IMEC, IDLab, B-9052 Ghent, Belgium. tomd.vansteenkiste@ugent.be; groenendaal@imec-nl.nl; pauline.dreesen@zol.be; seulki.lee@imec-nl.nl; susie.klerkx@zol.be; ruben.defrancisco@oneramedical.com; dirk.deschrijver@ugent.be; tom.dhaene@ugent.be | Keywords: | Sleep apnea;Electrocardiography;Biomedical measurement;Current measurement;Sensors;Electrodes;Impedance;HSAT;bio-impedance;deep-learning | Document URI: | http://hdl.handle.net/1942/32781 | ISSN: | 2168-2194 | e-ISSN: | 2168-2208 | DOI: | 10.1109/JBHI.2020.2967872 | ISI #: | WOS:000567435400017 | Rights: | Copyright 2020 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
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8675567.pdf Restricted Access | Published version | 1.04 MB | Adobe PDF | View/Open Request a copy |
8675567.pdf | Peer-reviewed author version | 1.04 MB | Adobe PDF | View/Open |
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