Portable monitoring in sleep apnea

A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

BACKGROUND

The majority of patients with obstructive sleep apnea do not receive timely diagnosis and treatment because of the complexity of a diagnostic test. We aimed to predict obstructive sleep apnea based on heart rate variability, body mass index, and demographic characteristics in a large Korean population.

METHODS

Models of binary classification for predicting obstructive sleep apnea severity were constructed using 14 features including 11 heart rate variability variables, age, sex, and body mass index. Binary classification was conducted separately using apnea-hypopnea index thresholds of 5, 15, and 30. Sixty percent of the participants were randomly allocated to training and validation sets while the other forty percent were designated as the test set. Classifying models were developed and validated with 10-fold cross-validation using logistic regression, random forest, support vector machine, and multilayer perceptron algorithms.

RESULTS

A total of 792 (651 men and 141 women) subjects were included. The mean age, body mass index, and apnea-hypopnea index score were 55.1 years, 25.9 kg/m², and 22.9, respectively. The sensitivity of the best performing algorithm was 73.6%, 70.7%, and 78.4% when the apnea-hypopnea index threshold criterion was 5, 10, and 15, respectively. The prediction performances of the best classifiers at apnea-hypopnea indices of 5, 15, and 30 were as follows: accuracy, 72.2%, 70.0%, and 70.3%; specificity, 64.6%, 69.2%, and 67.9%; area under the receiver operating characteristic curve, 77.2%, 73.5%, and 80.1%, respectively. Overall, the logistic regression model using the apnea-hypopnea index criterion of 30 showed the best classifying performance among all models.

CONCLUSION

Obstructive sleep apnea was fairly predicted by using heart rate variability, body mass index, and demographic characteristics in a large Korean population. Prescreening and continuous treatment monitoring of obstructive sleep apnea may be possible simply by measuring heart rate variability.

Efficacy of portable sleep monitoring device in diagnosing central sleep apnea in patients with congestive heart failure

Introduction

Central sleep apnea (CSA) is a common and serious comorbidity mainly occurring in patients with heart failure (HF), which tends to be underdiagnosed and has not been widely studied. Overnight polysomnography (PSG) is the gold standard for diagnosing CSA; however, the time and expense of the procedure limit its applicability. Portable monitoring (PM) devices are convenient and easy to use; however, they have not been widely studied as to their effectiveness in detecting CSA in patients with HF. In the current study, we examined the diagnostic value of PM as a screening tool to identify instances of CSA among patients with HF.

Methods

A total of 22 patients under stable heart failure conditions with an ejection fraction of <50% were enrolled. All patients underwent PM and overnight PSG within a narrow time frame. The measurements of the apnea-hypopnea index (AHI), hypopnea index (HI), central apnea index (CAI), and obstructive apnea index (OAI) obtained from PSG, automatic scoring, and manual scoring of PM were recorded. The results obtained from PSG and those from PM (automatic and manual scoring) were compared to assess the accuracy of PM.

Results

Among the patients, CSA in 11 patients was found by PSG. The AHI measurements performed using manual scoring of PM showed a significant correlation with those performed using PSG (r = 0.69; P = 0.01). Nonetheless, mean AHI measurements showed statistically significant differences between PSG and automatic scoring of PM (40.0 vs. 23.7 events/hour, respectively; P < 0.001), as well as between automatic and manual scoring of PM (23.7 vs. 29.5 events/hour; P < 0.001). Central sleep apnea was detected by PM; however, the results were easily misread as obstructive apnea, particularly in automatic scoring.

Conclusion

PM devices could be used to identify instances of central sleep apnea among patients with HF. The results from PM were well-correlated with standard PSG results, and manual scoring was preferable to automated scoring.

Alternative algorithms and devices in sleep apnoea diagnosis: what we know and what we expect

PURPOSE OF REVIEW

Diagnosis of sleep apnoea was performed in sleep laboratories with polysomnography. This requires a room with supervision and presence of technologists and trained sleep experts. Today, clinical guidelines in most countries recommend home sleep apnoea testing with simple systems using six signals only. If criteria for signal quality, recording conditions, and patient selection are considered, then this is a reliable test with high accuracy.

RECENT FINDINGS

Recently diagnostic tools for sleep apnoea diagnosis become even more simple: smartwatches and wearables with smart apps claim to diagnose sleep apnoea when these devices are tracking sleep and sleep quality as part of new consumer health checking. Alternative and new devices range from excellent diagnostic tools with high accuracy and full validation studies down to very low-quality tools which only result in random diagnostic reports. Due to the high prevalence of sleep apnoea, even a random diagnosis may match a real disorder sometimes.

SUMMARY

Until now, there are no metrics established how to evaluate these alternative algorithms and simple devices. Proposals for evaluating smartwatches, smartphones, single-use sensors, and new algorithms are presented. New assessments may help to overcome current limitations in sleep apnoea severity metrics. VIDEO ABSTRACT: http://links.lww.com/COPM/A28.

Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders

Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r ² = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.

Classifying obstructive sleep apnea using smartphones

Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body's movement. Finally, we examine our system's ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare.