Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 25, Issue 8, Pages 2917-2927Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2021.3064694
Keywords
Neural networks; Hospitals; Training; Sleep apnea; Manuals; Indexes; Physics; Machine learning; Artificial neural networks; Obstructive sleep apnea; Respiratory event scoring
Categories
Funding
- Academy of Finland [313697, 323536]
- Research committee of the Kuopio University Hospital Catchment Area [5041781, 5041780, 5041797, 5041767, 5041794, 5041768]
- Instrumentarium Science Foundation
- Research Foundation for Pulmonary Diseases
- Foundation of the Finnish Anti-Tuberculosis Association
- Respiratory Foundation of Kuopio Region
- Paivikki and Sakari Sohlberg Foundation
- Paulo Foundation
- Business Finland
- Finnish Cultural Foundation
- Tampere Tuberculosis Foundation
- Academy of Finland (AKA) [323536, 323536] Funding Source: Academy of Finland (AKA)
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This study demonstrated the effectiveness of using a neural network for automatic scoring of respiratory events in sleep apnea diagnosis. The neural network achieved high accuracy and agreement with manual scoring, making it a valuable tool for analyzing large research datasets and potentially for clinical use in the future. Additionally, the automated scoring can be easily reviewed manually if needed, providing flexibility and reliability in the diagnostic process.
The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably improve the efficiency of sleep apnea diagnostics and release the resources currently needed for manual scoring to other areas of sleep medicine. In this study, we trained a long short-term memory neural network for automatic scoring of respiratory events using input signals from peripheral blood oxygen saturation, thermistor-airflow, nasal pressure -airflow, and thorax respiratory effort. The signals were extracted from 887 in-lab polysomnography recordings. 787 patients with suspected sleep apnea were used to train the neural network and 100 patients were used as an independent test set. The epoch-wise agreement between manual and automatic neural network scoring was high (88.9%, kappa = 0.728). In addition, the apnea-hypopnea index (AHI) calculated from the automated scoring was close to the manually determined AHI with a mean absolute error of 3.0 events/hour and an intraclass correlation coefficient of 0.985. The neural network approach for automatic scoring of respiratory events achieved high accuracy and good agreement with manual scoring. The presented neural network could be used for analysis of large research datasets that are unfeasible to score manually, and has potential for clinical use in the future In addition, since the neural network scores individual respiratory events, the automatic scoring can be easily reviewed manually if desired.
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