Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea

Abstract

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for these reasons, OSA is a heavily underdiagnosed disease as only 7% of women and 18% of men suffering from OSA have diagnosis. To resolve these issues, we introduce an artificial neural network (ANN) that estimates AHI and oxygen desaturation index (ODI) using only the blood oxygen saturation signal (SpO2), recorded during ambulatory polygraphy, as an input. Therefore, hypopneas associated only with an arousal were not considered in this study. SpO2 signals from 1692 patients were used for training and 99 for validation. Two test sets were used consisting of 198 and 1959 patients. In the primary test set, the median absolute errors of ANN estimated AHI and ODI were 0.78 events/hour and 0.68 events/hour respectively. Based on the ANN estimated AHI and ODI, 90.9% and 94.4% of the test patients were classified into the correct OSA severity category. In conclusion, AHI and ODI can be reliably determined using neural network analysis of SpO2 signal. The developed method may enable a more affordable screening of OSA.

Increased nocturnal arterial pulsation frequencies of obstructive sleep apnoea patients is associated with an increased number of lapses in a psychomotor vigilance task

“…In addition, the computation of spectrograms and spectrums is fast and convenient; the presented scheme can be easily implemented in existing diagnostic methods such as PSG or home sleep apnoea testing. These findings together with previous studies imply that the assessment of OSA severity and evaluation of related daytime dysfunctions could be conducted using pulse oximeter measurements and deep learning [ 9 , 10 , 36 ]. This would further facilitate referrals to in-depth examinations for those with the highest risk of severe consequences of OSA.…”

Automatic Respiratory Event Scoring in Obstructive Sleep Apnea Using a Long Short-Term Memory Neural Network

“…Artificial neural network (ANN) methods have been shown to be powerful tools in medical signal analysis and have also been used in sleep science for highly accurate automated sleep staging [16]- [18]. In addition, there is a large number of studies that use ANN-based methods to estimate sleep apnea severity using various input signals [19], [20]. However, these previous studies have only estimated the AHI or oxygen desaturation index (ODI), performed simple binary classification to OSA and non-OSA groups, or detected whether some signal segment includes respiratory events, but not their exact start time or duration [19], [20].…”

“…In addition, there is a large number of studies that use ANN-based methods to estimate sleep apnea severity using various input signals [19], [20]. However, these previous studies have only estimated the AHI or oxygen desaturation index (ODI), performed simple binary classification to OSA and non-OSA groups, or detected whether some signal segment includes respiratory events, but not their exact start time or duration [19], [20]. Therefore, these automatic methods cannot be directly compared to standard manual respiratory event scoring.…”

Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network

“…To simplify the diagnostic technique, various alternative methods have been proposed to replace PSG and minimize the number of sensors required in recent decades, such as ECG, 13 , 30 , 35 EEG, 14 , 36 SpO 2 , 31 , 37 respiratory, 16 , 38 and snoring signals. 39 , 40 However, the most appropriate physiological signals for the development of simple and accurate screening tests for OSA remain unknown.…”