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Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
Automatic detection of sleep-disordered breathing from a single-channel airflow record
H Nakano1, T Tanigawa, T Furukawa
1Dept of Pulmonology, Fukuoka National Hospital, Minami-ku, Fukuoka, 811-1394, Japan. nakano_h@palette.plala.or.jp
The European Respiratory Journal
|January 26, 2007
Summary
A new computer algorithm significantly improves the accuracy of single-channel airflow monitors for detecting sleep-disordered breathing (SDB). This optimized algorithm enables reliable screening for SDB using simplified airflow monitoring devices.
Area of Science:
- Respiratory Medicine
- Biomedical Engineering
- Signal Processing
Background:
- Screening for sleep-disordered breathing (SDB) using single-channel airflow monitors yields inconsistent accuracy.
- The performance of these devices is hypothesized to be critically dependent on the analytical algorithm used.
- Developing a novel, optimized algorithm is essential for improving SDB detection accuracy.
Purpose of the Study:
- To develop and validate a novel computer algorithm for analyzing single-channel airflow signals.
- To assess the diagnostic performance of the developed algorithm in detecting sleep-disordered breathing (SDB).
- To determine if an optimized algorithm can enhance the accuracy of airflow monitors for SDB screening.
Main Methods:
- A total of 399 polysomnography (PSG) records with thermal sensor signals were used; 100 for algorithm development and 299 for validation.
- An additional 119 PSG records with thermocouple and nasal pressure signals were used for validation.
- The algorithm employed power spectral analysis to create a flow-power time series, detecting transient falls to calculate the flow-respiratory disturbance index (RDI).
Main Results:
- The developed algorithm achieved high diagnostic performance across different airflow sensor types.
- Areas under the receiver operating characteristic curves for SDB diagnosis (apnoea/hypopnoea index ≥5) were 0.96 (thermal sensor), 0.95 (thermocouple), and 0.95 (nasal pressure).
- Diagnostic sensitivity/specificity ratios for flow-RDI were 96%/76% (thermal sensor), 88%/80% (thermocouple), and 97%/77% (nasal pressure).
Conclusions:
- A single-channel airflow monitor, when coupled with an optimized analytical algorithm, can effectively detect sleep-disordered breathing.
- The developed flow-power analysis algorithm demonstrates high accuracy in identifying SDB.
- This approach offers a promising method for automated and accurate SDB screening.
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