Quantitative assessment of the quality of home sleep studies: A computer‐assisted approach. (9th August 2019)

• Record Type: Journal Article
• Authors: Maestri, Roberto; Robbi, Elena; Lovagnini, Marta; Taurino, Anna Eugenia; Fanfulla, Francesco; Pinna, Gian Domenico
• Is Part Of: Journal of sleep research. Volume 29:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-09
• Subjects: central sleep apnea -- chronic heart failure -- computer‐assisted classification -- obstructive sleep apnea -- sleep analysis; Sleep -- Periodicals; Sleep disorders -- Periodicals; 612.821
• DOI: 10.1111/jsr.12899
• Languages: English
• ISSNs: 0962-1105

Abstract

Abstract: Home monitoring is the most practical means of collecting sleep data in large‐scale research investigations. Because the portion of recording time with poor‐quality data is higher than in attended polysomnography, a quantitative assessment of the quality of each signal should be recommended. Currently, only qualitative or semi‐quantitative assessments are carried out, likely because of the lack of computer‐based applications to carry out this task efficiently. This paper presents an innovative computer‐assisted procedure designed to perform a quantitative quality assessment of standard respiratory signals recorded by Type 2 and Type 3 portable sleep monitors. The proposed system allows to assess the quality (good versus bad) of consecutive 1‐min segments of thoraco‐abdominal movements, oronasal, nasal airflow and oxygen saturation through an automatic classifier. The performance of the classifier was evaluated in a sample of 30 unattended polysomnography recordings, comparing the computer output with the consensus of two expert scorers. The difference (computer versus scorers) in the percentage of good‐quality segments was on average very small, ranging from −3.1% (abdominal movements) to 0.8% (nasal flow), with an average total classification accuracy from 90.2 (oronasal flow) to 94.9 (nasal flow), a Sensitivity from 0.93 (oronasal flow) to 0.98 (nasal flow), and a Specificity from 0.74 (nasal flow) to 0.86 (abdominal movements). In practical applications, the scorer can run a check‐and‐edit procedure, further improving the classification accuracy. Considering a sample of 270 unattended polysomnography recordings (recording time: 545 ± 44 min), the average time taken for the check‐and‐edit procedure of each recording was 6.9 ± 2.1 min for all respiratory signals.