Sleep apnea-hypopnea quantification by cardiovascular data analysis

TL;DR

This paper introduces a novel method for detecting sleep apnea using only systolic blood pressure signals, achieving 82% accuracy without requiring comprehensive polysomnography.

Contribution

It proposes a new apnea detection technique based on analyzing nonstationarities in blood pressure signals, eliminating the need for multiple recordings.

Findings

Achieved 82% accuracy in apnea detection.

Introduced segmentation-based analysis of blood pressure signals.

Provided an alternative to polysomnography for sleep apnea detection.

Abstract

Sleep apnea is the most common sleep disturbance and it is an important risk factor for cardiovascular disorders. Its detection relies on a polysomnography, a combination of diverse exams. In order to detect changes due to sleep disturbances such as sleep apnea occurrences, without the need of combined recordings, we mainly analyze systolic blood pressure signals (maximal blood pressure value of each beat to beat interval). Nonstationarities in the data are uncovered by a segmentation procedure, which provides local quantities that are correlated to apnea-hypopnea events. Those quantities are the average length and average variance of stationary patches. By comparing them to an apnea score previously obtained by polysomnographic exams, we propose an apnea quantifier based on blood pressure signal. This furnishes an alternative procedure for the detection of apnea based on a single time series, with an accuracy of 82%.