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Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning

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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.
Keywords
OBSTRUCTIVE SLEEP-APNEA, HOME SLEEP, CARDIOVASCULAR-DISEASE, SCREENING, DEVICE, DIAGNOSIS, POLYSOMNOGRAPHY, VALIDATION, OXIMETRY, STROKE, Sleep apnea, Electrocardiography, Biomedical measurement, Current, measurement, Sensors, Electrodes, Impedance, Sleep apnea, HSAT, bio-impedance, deep-learning

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Citation

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MLA
Van Steenkiste, Tom, et al. “Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 24, no. 9, 2020, pp. 2589–98, doi:10.1109/JBHI.2020.2967872.
APA
Van Steenkiste, T., Groenendaal, W., Dreesen, P., Lee, S., Klerkx, S., de Francisco, R., … Dhaene, T. (2020). Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 24(9), 2589–2598. https://doi.org/10.1109/JBHI.2020.2967872
Chicago author-date
Van Steenkiste, Tom, Willemijn Groenendaal, Pauline Dreesen, Seulki Lee, Susie Klerkx, Ruben de Francisco, Dirk Deschrijver, and Tom Dhaene. 2020. “Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 24 (9): 2589–98. https://doi.org/10.1109/JBHI.2020.2967872.
Chicago author-date (all authors)
Van Steenkiste, Tom, Willemijn Groenendaal, Pauline Dreesen, Seulki Lee, Susie Klerkx, Ruben de Francisco, Dirk Deschrijver, and Tom Dhaene. 2020. “Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 24 (9): 2589–2598. doi:10.1109/JBHI.2020.2967872.
Vancouver
1.
Van Steenkiste T, Groenendaal W, Dreesen P, Lee S, Klerkx S, de Francisco R, et al. Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. 2020;24(9):2589–98.
IEEE
[1]
T. Van Steenkiste et al., “Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning,” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 24, no. 9, pp. 2589–2598, 2020.
@article{8675564,
  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.}},
  author       = {{Van Steenkiste, Tom and Groenendaal, Willemijn and Dreesen, Pauline and Lee, Seulki and Klerkx, Susie and de Francisco, Ruben and Deschrijver, Dirk and Dhaene, Tom}},
  issn         = {{2168-2194}},
  journal      = {{IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}},
  keywords     = {{OBSTRUCTIVE SLEEP-APNEA,HOME SLEEP,CARDIOVASCULAR-DISEASE,SCREENING,DEVICE,DIAGNOSIS,POLYSOMNOGRAPHY,VALIDATION,OXIMETRY,STROKE,Sleep apnea,Electrocardiography,Biomedical measurement,Current,measurement,Sensors,Electrodes,Impedance,Sleep apnea,HSAT,bio-impedance,deep-learning}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{2589--2598}},
  title        = {{Portable detection of apnea and hypopnea events using bio-impedance of the chest and deep learning}},
  url          = {{http://dx.doi.org/10.1109/JBHI.2020.2967872}},
  volume       = {{24}},
  year         = {{2020}},
}

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