3D Camera and Pulse Oximeter for Respiratory Events Detection
Autor: | Marion Böck, Christoph Wiesmeyr, Thomas Penzel, Bernhard Kohn, Markus A. Wimmer, Eugenijus Kaniusas, Martin Glos, Gerhard Klösch, Heinrich Garn, Magdalena Mandl, Stefan Seidel, Andrijana Stefanic-Kejik, Carmina Coronel |
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Rok vydání: | 2021 |
Předmět: |
medicine.medical_specialty
Intraclass correlation Polysomnography Health Informatics 02 engineering and technology 03 medical and health sciences 0302 clinical medicine Health Information Management Internal medicine 0202 electrical engineering electronic engineering information engineering medicine Humans Oximetry Electrical and Electronic Engineering Respiratory system Sleep Apnea Obstructive medicine.diagnostic_test Pulse (signal processing) business.industry Reproducibility of Results Sleep apnea medicine.disease Confidence interval respiratory tract diseases Computer Science Applications Oxygen 3d camera Cardiology 020201 artificial intelligence & image processing business 030217 neurology & neurosurgery Kappa |
Zdroj: | IEEE Journal of Biomedical and Health Informatics. 25:181-188 |
ISSN: | 2168-2208 2168-2194 |
DOI: | 10.1109/jbhi.2020.2984954 |
Popis: | Objective: The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO2 to detect respiratory events comparable to polysomnography (PSG) based detection. Methods: We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO2 desaturation to score respiratory events. The method was tested on 61 patients’ synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated. Results: The predicted AHI scored an ICC r = 0.94 (0.90 – 0.96 at 95% confidence interval, p Conclusion: Our detection method using SpO2 and 3D camera had excellent reliability and substantial agreement with PSG-based scoring. Significance: This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts. |
Databáze: | OpenAIRE |
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