Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals

Rolón, Román Emanuel; Gareis, Iván; Larrateguy, Luis; Persia, Leandro E. Di; Spies, Rubén D.; Rufiner, Hugo Leonardo

doi:10.1016/j.bspc.2020.102062

Cited by 20 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…187 The possibility of automatic scoring of apnea and hypopnea events using blood oxygen saturation signals had been confirmed. 187 Pulse oximetry is a simple non-invasive method of monitoring the percentage of hemoglobin in blood, which is saturated with oxygen. 188…”

Section: Application In Environmental Researchmentioning

confidence: 95%

“…Oxygen saturation is an important physiological parameter of the respiratory cycle. The oxygen required in the metabolic process enters blood through the respiratory system, and the oxygen content will drop continuously with the prolongation of apnea 187 . The possibility of automatic scoring of apnea and hypopnea events using blood oxygen saturation signals had been confirmed 187 .…”

Section: Immature Assessment Of Sleep Qualitymentioning

confidence: 99%

“…The oxygen required in the metabolic process enters blood through the respiratory system, and the oxygen content will drop continuously with the prolongation of apnea 187 . The possibility of automatic scoring of apnea and hypopnea events using blood oxygen saturation signals had been confirmed 187 . Pulse oximetry is a simple non‐invasive method of monitoring the percentage of hemoglobin in blood, which is saturated with oxygen 188 …”

Section: Immature Assessment Of Sleep Qualitymentioning

confidence: 99%

See 2 more Smart Citations

Objective sleep assessments for healthy people in environmental research: A literature review

Lian

2022

Indoor Air

View full text Add to dashboard Cite

To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring‐oxygen saturation monitoring (RM‐OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM‐OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non‐contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

show abstract

Section: Application In Environmental Researchmentioning

confidence: 95%

Section: Immature Assessment Of Sleep Qualitymentioning

confidence: 99%

Section: Immature Assessment Of Sleep Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Objective sleep assessments for healthy people in environmental research: A literature review

Lian

2022

Indoor Air

View full text Add to dashboard Cite

show abstract

“…A reduced number of required channels would also be an advantage. There are algorithms that can operate on only one signal and provide locations of the sleep apnea episodes [ 15 , 16 ]. However, since this work aims for analyzing polygraphic examinations and not screening, channels commonly available in polygraphs can be used without further complication of the setup.…”

Section: Introductionmentioning

confidence: 99%

“…Limiting factors include lack of precise episode boundaries determination and exclusion of patients with central or mixed sleep apnea, as well as with cardiovascular diseases. Another interesting approach is documented in [ 16 ]: the SpO2 signal alone was used for detection and classification of apnea and hypopnea episodes separately. A window of 128 s with 50% overlap was chosen, thus temporal resolution of this approach is relatively low.…”

Section: Introductionmentioning

confidence: 99%

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Drzazga

Cyganek

2021

Sensors

View full text Add to dashboard Cite

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

show abstract

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea

Gutiérrez‐Tobal

Álvarez

Vaquerizo-Villar

et al. 2022

Advances in the Diagnosis and Treatment of Sleep Apnea

View full text Add to dashboard Cite

Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals

Cited by 20 publications

References 28 publications

Objective sleep assessments for healthy people in environmental research: A literature review

Objective sleep assessments for healthy people in environmental research: A literature review

An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea

Contact Info

Product

Resources

About