Apnea-hypopnea index prediction through an assessment of autonomic influence on heart rate in wakefulness

Screening of obstructive sleep apnea syndrome by the deep breathing technique

STUDY OBJECTIVES

Obstructive sleep apnea syndrome (OSAS) is associated with alterations in heart rate variability (HRV) in relation to chronic autonomic dysfunction. We tested the ability of the deep breathing technique-a simple way to evaluate HRV-to identify patients with OSAS.

METHODS

Consecutive patients referred for suspected OSAS (without obesity, diabetes, and heart diseases) were included. They underwent a measure of HRV at rest and of heart rate oscillations during expiration vs inspiration (DeltaHRDB) when breathing deeply at the resonant frequency of 6 cycles per minute (deep breathing technique) while sitting awake, followed by a nighttime polysomnography. We measured DeltaHRDB and performed temporal and spectral HRV analysis.

RESULTS

Of 31 included participants (77% male), 14 had mild to moderate OSAS (apnea-hypopnea index median [IQR]: 18 [12]) and 17 had no OSAS. The conventional HRV analysis did not reveal any difference between the groups with vs without OSAS. However, the DeltaHRDB was lower in those with than without OSAS. Lower DeltaHRDB correlated with higher apnea-hypopnea index, arousal index, and desaturation degree. A DeltaHRDB below 11 beats per minute (bpm) predicted OSAS with a sensitivity of 100% and specificity of 86%.

CONCLUSIONS

The deep breathing technique accurately identifies a reduction in cardiac changes in patients with mild to moderate OSAS. It could be used as a simple screening tool to select patients for polysomnography.

CITATION

Onanga M, Joanny S, Rivals I, et al. Screening of obstructive sleep apnea syndrome by the deep breathing technique. J Clin Sleep Med. 2023;19(2):293-302.

Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome

OBJECTIVE/BACKGROUND

Recently, several tools for screening obstructive sleep apnea-hypopnea syndrome (OSAHS) have been devised with varied shortcomings. To overcome these drawbacks, we aimed to propose a self-estimation method using an explainable prediction model with easy-to-obtain variables and evaluate its performance for predicting OSAHS.

PATIENTS/METHODS

This retrospective, cross-sectional study selected significant easy-to-obtain variables from patients, suspected of having OSAHS by regression analysis, and fed these variables into the proposed explainable fuzzy neural network (EFNN), a back propagation neural network (BPNN) and a stepwise regression model to compare the screening performance for OSAHS.

RESULTS

Of the 300 participants, three easily available features, such as waist circumference, mean blood pressure (BP) at the end of polysomnography and the difference in systolic BP between the end and start of polysomnography, were obtained from regression analysis with a five-fold cross-validation scheme. Feeding these three variables into the prediction models showed that the average prediction differences for apnea-hypopnea index (AHI) when using the EFNN, BPNN, and regression model were respectively 1.5 ± 18.2, 3.5 ± 19.1 and 0.1 ± 19.3, indicating none of the tested methods had good efficacy to predict the AHI values. The performance as determined by the sensitivity + specificity-1 value for screening moderate-to-severe OSAHS of the EFNN, BPNN and regression model were respectively 0.440, 0.414 and 0.380.

CONCLUSIONS

When fed with easy-to-obtain physiological features, the understandable EFNN should be the preferred method to predict moderate-to-severe OSAHS.

Smart technologies toward sleep monitoring at home

With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea-hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.