Automatic detection of sleep-disordered breathing from a single-channel airflow record.
Single-channel airflow monitors developed for screening of sleep-disordered breathing (SDB) have conflicting results for accuracy. It was hypothesised that the analytical algorithm is crucial for the performance and the present authors tried to develop a novel computer algorithm. A total of 399 polysomnography (PSG) records were employed, including a thermal sensor signal. The first 100 records were used in the development of the algorithm and the remainder for validation. In addition, 119 PSG records, including a thermocouple signal and a nasal pressure signal, were used for the validation. The algorithm was designed to obtain a time series (flow-power) using power spectral analysis, which expresses fluctuation in the airflow signal amplitude. From the time series the algorithm detects transient falls of the flow-power and calculates flow-respiratory disturbance index (RDI), defined as the number of falls per hour. In the validation group, the areas under receiver operating characteristic curves for diagnosis of SDB (apnoea/hypopnoea index > or =5) were 0.96, 0.95 and 0.95, for the records of the thermal sensor, thermocouple and nasal pressure system, respectively. The diagnostic sensitivity/specificity ratios of the flow-RDI were 96/76, 88/80 and 97%/77%, respectively. The present results suggest that a single-channel airflow monitor can be used to detect sleep-disordered breathing automatically if the analytic algorithm is optimised.
Nakano
H
H
Dept of Pulmonology, Fukuoka National Hospital, Minami-ku, Fukuoka, 811-1394, Japan. nakano_h@palette.plala.or.jp
Tanigawa
T
T
Furukawa
T
T
Nishima
S
S
D000328:Adult; D000465:Algorithms; D001331:Automation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000473:pathology