Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept

Automatically detecting OSAHS patients based on transfer learning and model fusion

Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

Validation of Downloadable Mobile Snore Applications by Polysomnography (PSG)

Purpose

Obstructive sleep apnea (OSA) is a common breathing disorder during sleep that is associated with symptoms such as snoring, excessive daytime sleepiness, and breathing interruptions. Polysomnography (PSG) is the most reliable diagnostic test for OSA; however, its high cost and lengthy testing duration make it difficult to access for many patients. With the availability of free snore applications for home-monitoring, this study aimed to validate the top three ranked snore applications, namely SnoreLab (SL), Anti Snore Solution (ASS), and Sleep Cycle Alarm (SCA), using PSG.

Patients and Methods

Sixty participants underwent an overnight PSG while simultaneously using three identical smartphones with the tested apps to gather sleep and snoring data.

Results

The study discovered that all three applications were significantly correlated with the total recording time and snore counts of PSG, with ASS showing good agreement with snore counts. Furthermore, the Snore Score, Time Snoring of SL, and Sleep Quality of SCA had a significant correlation with the natural logarithm of apnea hypopnea index (lnAHI) of PSG. The Snore Score of SL and the Sleep Quality of SCA were shown to be useful for evaluating snore severity and for pre-diagnosing or predicting OSA above moderate levels.

Conclusion

These findings suggest that some parameters of free snore applications can be employed to monitor OSA progress, and future research could involve adjusted algorithms and larger-scale studies to further authenticate these downloadable snore and sleep applications.

How to objectively measure snoring: a systematic review

PURPOSE

Snoring is the most common symptom of obstructive sleep apnea. Various objective methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding objective measurement has been reached. This study aimed to review the literature related to the objective measurement of snoring, such as measurement devices, definitions, and device locations.

METHODS

A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 5, 2023. Twenty-nine articles were included in this study. Articles that mentioned only the equipment used for measurement and did not include individual details were excluded from the study.

RESULTS

Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications.

CONCLUSION

Numerous studies have investigated both obstructive sleep apnea and snoring. However, the objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

Technologies for sleep monitoring at home: wearables and nearables

Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.

Associations of smartphone usage patterns with sleep and mental health symptoms in a clinical cohort receiving virtual behavioral medicine care: a retrospective study

Study Objectives

We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care.

Methods

Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2300 patients receiving standard-of-care virtual mental healthcare across more than 111 000 days were retrospectively analyzed.

Results

Mean ESP duration was 8.4 h (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001).

Conclusions

Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e. greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Dependable algorithm for visualizing snoring duration through acoustic analysis: A pilot study

Snoring is a nuisance for the bed partners of people who snore and is also associated with chronic diseases. Estimating the snoring duration from a whole-night sleep period is challenging. The authors present a dependable algorithm for visualizing snoring durations through acoustic analysis. Both instruments (Sony digital recorder and smartphone's SnoreClock app) were placed within 30 cm from the examinee's head during the sleep period. Subsequently, spectrograms were plotted based on audio files recorded from Sony recorders. The authors thereby developed an algorithm to validate snoring durations through visualization of typical snoring segments. In total, 37 snoring recordings obtained from 6 individuals were analyzed. The mean age of the participants was 44.6 ± 9.9 years. Every recorded file was tailored to a regular 600-second segment and plotted. Visualization revealed that the typical features of the clustered snores in the amplitude domains were near-isometric spikes (most had an ascending-descending trend). The recorded snores exhibited 1 or more visibly fixed frequency bands. Intervals were noted between the snoring clusters and were incorporated into the whole-night snoring calculation. The correlative coefficients of snoring rates from digitally recorded files examined between Examiners A and B were higher (0.865, P < .001) than those with SnoreClock app and Examiners (0.757, P < .001; 0.787, P < .001, respectively). A dependable algorithm with high reproducibility was developed for visualizing snoring durations.

Sleep Pattern Analysis in Unconstrained and Unconscious State

Sleep accounts for one-third of an individual's life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Correlation between snoring sounds and obstructive sleep apnea in adults: a meta-regression analysis

OBJECTIVE

Snoring is a dominant clinical symptom in patients with obstructive sleep apnea (OSA), and analyzing snoring sounds might be a potential alternative to polysomnography (PSG) for the assessment of OSA. This study aimed to systematically examine the correlation between the snoring sounds and the apnea-hypopnea index (AHI) as the measures of OSA severity.

MATERIAL AND METHODS

A comprehensive literature review using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases identified the published studies reporting the correlations between and severity of snoring and the AHI values by meta-regression analysis.

RESULTS

In total, 13 studies involving 3,153 adult patients were included in this study. The pooled correlation coefficient for snoring sounds and AHI values was 0.71 (95%CI: 0.49, 0.85) from the random-effects meta-analysis with the Knapp and Hartung adjustment. The I 2 and chi-square Q test demonstrated significant heterogeneity (97.6% and p<0.001). After adjusting for the effects of the other covariates, the mean value of the Fisher's r-to-z transformed correlation coefficient would have 0.80 less by the snoring rate (95%CI = -1.02, -0.57), 1.46 less by the snoring index (95%CI = -1.85, -1.07), and 0.21 less in the mean body mass index (95%CI = -0.31, -0.11), but 0.15 more in the mean age (95%CI = 0.10, 0.20). It fitted the data very well (R 2=0.9641).

CONCLUSION

A high correlation between the severity of snoring and the AHI was found in the studies with PSG. As compared to the snoring rate and the snoring index, the snoring intensity, the snoring frequency, and the snoring time interval index were more sensitive measures for the severity of snoring.

Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review

Mental health issues are a serious consequence of the COVID-19 pandemic, influencing about 700 million people worldwide. These physiological issues need to be consistently observed on the people through non-invasive devices such as smartphones, and fitness bands in order to remove the burden of having the conciseness of continuously being monitored. On the other hand, technological improvements have enhanced the abilities and roles of conventional mobile phones from simple communication to observations and improved accessibility in terms of size and price may reflect growing familiarity with the smartphone among a vast number of consumers. As a result of continuous monitoring, together with various embedded sensors in mobile phones, raw data can be converted into useful information about the actions and behaviors of the consumers. Thus, the aim of this comprehensive work concentrates on the literature work done so far in the prediction of mental health issues via passive monitoring data from smartphones. This study also explores the way users interact with such self-monitoring technologies and what challenges they might face. We searched several electronic databases (PubMed, IEEE Xplore, ACM Digital Libraries, Soups, APA PsycInfo, and Mendeley Data) for published studies that are relevant to focus on the topic and English language proficiency from January 2015 to December 2020. We identified 943 articles, of which 115 articles were eligible for this scoping review based on the predetermined inclusion and exclusion criteria carried out manually. These studies provided various works regarding smartphones for health monitoring such as Physical activity (26.0 percent; 30/115), Mental health analysis (27.8 percent; 32/115), Student specific monitoring (15.6 percent; 18/115) are the three analyses carried out predominantly.

Convolutional Neural Networks for Apnea Detection from Smartphone Audio Signals: Effect of Window Size

Although sleep apnea is one of the most prevalent sleep disorders, most patients remain undiagnosed and untreated. The gold standard for sleep apnea diagnosis, polysomnography, has important limitations such as its high cost and complexity. This leads to a growing need for novel cost-effective systems. Mobile health tools and deep learning algorithms are nowadays being proposed as innovative solutions for automatic apnea detection. In this work, a convolutional neural network (CNN) is trained for the identification of apnea events from the spectrograms of audio signals recorded with a smartphone. A systematic comparison of the effect of different window sizes on the model performance is provided. According to the results, the best models are obtained with 60 s windows (sensitivity-0.72, specilicity-0.89, AUROC = 0.88), For smaller windows, the model performance can be negatively impacted, because the windows become shorter than most apnea events, by which sound reductions can no longer be appreciated. On the other hand, longer windows tend to include multiple or mixed events, that will confound the model. This careful trade-off demonstrates the importance of selecting a proper window size to obtain models with adequate predictive power. This paper shows that CNNs applied to smartphone audio signals can facilitate sleep apnea detection in a realistic setting and is a first step towards an automated method to assist sleep technicians. Clinical Relevance- The results show the effect of the window size on the predictive power of CNNs for apnea detection. Furthermore, the potential of smartphones, audio signals, and deep neural networks for automatic sleep apnea screening is demonstrated.

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

Importance

Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.

Objective

To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection.

Design, Setting, and Participants

A cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020.

Main Outcomes and Measures

Accuracy of OSA prediction models.

Results

A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error.

Conclusions and Relevance

In this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Objectives

To assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS).

Methods

Six databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool.

Results

Eleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735].

Conclusions

Smartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.

A systematic review of smartphone applications and devices for obstructive sleep apnea

OBJECTIVE

Sleep is fundamental for both health and wellness. The advent of "on a chip" and "smartphone" technologies have created an explosion of inexpensive, at-home applications and devices specifically addressing sleep health and sleep disordered breathing. Sleep-related smartphone Applications and devices are offering diagnosis, management, and treatment of a variety of sleep disorders, mainly obstructive sleep apnea. New technology requires both a learning curve and a review of reliability. Our objective was to evaluate which app have scientific publications as well as their potential to help in the diagnosis, management, and follow-up of sleep disordered breathing.

METHODS

We search for relevant sleep apnea related apps on both the Google Play Store and the Apple App Store. In addition, an exhaustive literature search was carried out in MEDLINE, EMBase, web of science and Scopus for works of apps or devices that have published in the scientific literature and have been used in a clinical setting for diagnosis or treatment of sleep disordered breathing performing a systematic review.

RESULTS

We found 10 smartphone apps that met the inclusion criteria.

CONCLUSIONS

The development of these apps and devices has a great future, but today are not as accurate as other traditional options. This new technology offers accessible, inexpensive, and continuous at home data monitoring of obstructive sleep apnea, but still does not count with proper testing and their validation may be unreliable.

A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network

Background and objective

Continuous monitoring of breathing activity plays a vital role in the detection of respiratory-based diseases (SA, COPD, etc.). Sleep Apnea (SA) is characterized by recurrent upper airway obstruction during sleep associated with arterial blood desaturation, sympathetic nervous system activation, and cardiovascular impairment. Untreated patients with SA have increased mortality rates compared to the general population. This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors in the Covid-19 era.

Methods

This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors. Our study focuses on a novel portable real-time low-cost sleep apnea monitoring system utilizing the GSM network (GSM Shield Sim900a). Proposed system is a remote monitoring and patient tracking system to detect the apnea event in real time, and to provide information of the sleep position, pulse, and respiratory and oxygen saturation to the medical specialists (SpO₂) by establishing a direct contact. As soon as an abnormal condition is detected in the light of these parameters, the condition is reported (instant or in the form of short reports after sleep) to the patient relatives, the doctor’s mobile telephone or to the emergency medical centers (EMCs) through a GSM network to handle the case depending on the patient’s emergency condition.

Results

A study group was formed of six patients for monitoring apnea events (three males and three females) between the ages of 20 and 60. The patients in the study group have sleep apnea (SA) in different grades. All the apnea events were detected, and all the patients were successfully alerted. Also, the patient parameters were successfully sent to all patient relatives. Patients who could not get out of apnea were called through the CALL feature, and they were informed about their ongoing apnea event and told that intervention was necessary. The proposed system is tested on six patients. The beginning moment of apnea was successfully detected and the SMS/CALL feature was successfully activated without delay. During the testing, it has been observed that while some of the patients start breathing after the first SMS, some others needed the second or the third SMS. According to the measurement result, the maximum breathless time is 46 s among the patients, and a SMS is sent every 15 s. In addition, in cases where the patient was breathless for a long time, the CALL feature was actively sought from the relatives of the patient and enabled him to intervene. The proposed monitoring system could be used in both clinical and home settings.

Conclusions

The monitoring of a patient in real time allows to intervene in any unexpected circumstances about the patient. The proposed work uses an acceleration sensor as a reliable method of the sleep apnea for monitoring and prevention. The developed device is more economical, comfortable, and convenient than existing systems not only for the patients but also for the doctors. The patients can easily use this device in their home environment, so which could yield a more comfortable, easy to use, cost-effective, and long-term breathing monitoring system for healthcare applications.

Graphical abstract

Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning

PURPOSE

This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder.

METHODS

We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard.

RESULTS

A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%.

CONCLUSION

Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.

Prediction of Oxygen Desaturation by Using Sound Data From a Noncontact Device: A Proof-of-Concept Study

OBJECTIVES/HYPOTHESIS

Prediction of the apnea-hypopnea index (AHI) from breathing sounds during sleep could be used to prescreen for obstructive sleep apnea (OSA). In addition, the oxygen desaturation index (ODI) is a known risk factor for developing cardiovascular disease in OSA patients. This study focused on estimation of ODI from a noncontact manner from sleep breathing sounds.

STUDY DESIGN

Retrospective study.

METHODS

Patients who visited the sleep center due to snoring or sleep apnea underwent polysomnography in lab overnight. Sound recordings were made during polysomnography using a microphone. After noise reduction, the sound data were segmented into 5 seconds windows and features were extracted. Binary classification and regression analyses were performed to estimate the ODI during sleep (model 1). This was re-tested after inclusion of body mass index (BMI) and age as additional features (model 2: BMI only, model 3: BMI and age).

RESULTS

We included 116 patients. The mean age and AHI of all patients were 50.4 ± 16.7 years and 23.0 ± 24.0 events/hr. In binary classification, for ODI cutoff values of 5, 15, and 30 events/hr, the areas under the curve were 0.88, 0.93, 0.91, respectively, and accuracies were 85.34, 86.21, and 87.07, respectively. In regression analysis, the correlation coefficient and mean absolute error were 0.80 and 9.60 events/hr, respectively. In models 2 and 3, the correlation coefficient and mean absolute error were 0.82, 9.44 events/hr and 0.81, 9.6 events/hr, respectively.

CONCLUSION

Prediction of ODI from sleep sound seems to be feasible. Additional clinical feature such as BMI may increase overall predictability.

LEVEL OF EVIDENCE

IV Laryngoscope, 2021.

Detection of Sleep-Disordered Breathing in Patients with Spinal Cord Injury Using a Smartphone

Patients with spinal cord injury (SCI) have an increased risk of sleep-disordered breathing (SDB), which can lead to serious comorbidities and impact patients’ recovery and quality of life. However, sleep tests are rarely performed on SCI patients, given their multiple health needs and the cost and complexity of diagnostic equipment. The objective of this study was to use a novel smartphone system as a simple non-invasive tool to monitor SDB in SCI patients. We recorded pulse oximetry, acoustic, and accelerometer data using a smartphone during overnight tests in 19 SCI patients and 19 able-bodied controls. Then, we analyzed these signals with automatic algorithms to detect desaturation, apnea, and hypopnea events and monitor sleep position. The apnea–hypopnea index (AHI) was significantly higher in SCI patients than controls (25 ± 15 vs. 9 ± 7, p < 0.001). We found that 63% of SCI patients had moderate-to-severe SDB (AHI ≥ 15) in contrast to 21% of control subjects. Most SCI patients slept predominantly in supine position, but an increased occurrence of events in supine position was only observed for eight patients. This study highlights the problem of SDB in SCI and provides simple cost-effective sleep monitoring tools to facilitate the detection, understanding, and management of SDB in SCI patients.

Non-invasive devices for respiratory sound monitoring

Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.

Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.

We present the literature review of non-invasive sound acquisition devices and techniques.

The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.

Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.

PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies

The sleep apnea syndrome is a chronic condition that affects the quality of life and increases the risk of severe health conditions such as cardiovascular diseases. However, the prevalence of the syndrome in the general population is considered to be heavily underestimated due to the restricted number of people seeking diagnosis, with the leading cause for this being the inconvenience of the current reference standard for apnea diagnosis: Polysomnography. To enhance patients' awareness of the syndrome, a great endeavour is conducted in the literature. Various home-based apnea detection systems are being developed, profiting from information in a restricted set of polysomnography signals. In particular, breathing sound has been proven highly effective in detecting apneic events during sleep. The development of accurate systems requires multitudinous datasets of audio recordings and polysomnograms. In this work, we provide the first open access dataset, comprising 212 polysomnograms along with synchronized high-quality tracheal and ambient microphone recordings. We envision this dataset to be widely used for the development of home-based apnea detection techniques and frameworks.

Accuracy of a Smartphone Application Measuring Snoring in Adults-How Smart Is It Actually?

About 40% of the adult population is affected by snoring, which is closely related to obstructive sleep apnea (OSA) and can be associated with serious health implications. Commercial smartphone applications (apps) offer the possibility of monitoring snoring at home. However, the number of validation studies addressing snoring apps is limited. The purpose of the present study was to assess the accuracy of recorded snoring using the free version of the app SnoreLab (Reviva Softworks Ltd., London, UK) in comparison to a full-night polygraphic measurement (Miniscreen plus, Löwenstein Medical GmbH & Co., KG, Bad Ems, Germany). Nineteen healthy adult volunteers (4 female, 15 male, mean age: 38.9 ± 19.4 years) underwent simultaneous polygraphic and SnoreLab app measurement for one night at home. Parameters obtained by the SnoreLab app were: starting/ending time of monitoring, time in bed, duration and percent of quiet sleep, light, loud and epic snoring, total snoring time and Snore Score, a specific score obtained by the SnoreLab app. Data obtained from polygraphy were: starting/ending time of monitoring, time in bed, total snoring time, snore index (SI), snore index obstructive (SI obstructive) and apnea-hypopnea-index (AHI). For different thresholds of percentage snoring per night, accuracy, sensitivity, specificity, positive and negative predictive values were calculated. Comparison of methods was undertaken by Spearman-Rho correlations and Bland-Altman plots. The SnoreLab app provides acceptable accuracy values measuring snoring >50% per night: 94.7% accuracy, 100% sensitivity, 94.1% specificity, 66.6% positive prediction value and 100% negative prediction value. Best agreement between both methods was achieved in comparing the sum of loud and epic snoring ratios obtained by the SnoreLab app with the total snoring ratio measured by polygraphy. Obstructive events could not be detected by the SnoreLab app. Compared to polygraphy, the SnoreLab app provides acceptable accuracy values regarding the measurement of especially heavy snoring.

SleepPos App: An Automated Smartphone Application for Angle Based High Resolution Sleep Position Monitoring and Treatment

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events.

Snoring intensity assessment with three different smartphones using the SnoreLab application in one participant

STUDY OBJECTIVES

To compare the assessment of snoring using the SnoreLab application (app) using three different smartphones by one participant to validate SnoreLab as a method for collecting data for studies on the effectiveness of snoring treatment.

METHODS

A person from the research group was monitored for 30 consecutive nights with the SnoreLab app using three different smartphones (Xiaomi MI8Pro, Samsung Galaxy Alpha, and BQ Aquaris V). The SnoreLab app instructions were strictly followed, and data were collected from the app.

RESULTS

No significant differences were found in the measurements from the three smartphones in the time in bed, all snoring time, snoring percentage, and quiet time. BQ and Samsung smartphones determined significantly more light snoring time than did the Xiaomi smartphone. The Samsung smartphone assessed significantly less loud snoring time than did the Xiaomi smartphone and measured the shortest epic snoring time. The lowest Snore Score was calculated with the Samsung smartphone, the highest with the Xiaomi smartphone. Pearson's correlation coefficients demonstrated a relatively strong relationship between the Snore Score measured with the three smartphones.

CONCLUSIONS

Even though there was a relatively strong relationship between the Snore Score measured with the three smartphones by one participant, the observed differences make it difficult to use this index as a method of collecting data for studies on snoring treatment effectiveness when patients use different smartphones; however, the SnoreLab app may be handy to quantify treatment effectiveness for a specific patient, provided the patient always uses the same smartphone.

Enhanced Monitoring of Sleep Position in Sleep Apnea Patients: Smartphone Triaxial Accelerometry Compared with Video-Validated Position from Polysomnography

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal

STUDY OBJECTIVES

The aim of the study was to inspect acoustic properties and sleep characteristics of pre-apneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated.

METHODS

Participants with habitual snoring or heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted and snoring related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples and a machine learning algorithm was used to establish two prediction models.

RESULTS

A total of 74 eligible participants were included. Model 1 tested by five-fold cross validation achieved the accuracy of 0.92 and area under the curve of 0.94 for respiratory event prediction. model 2 with acoustic features and sleep information tested by Leave-One-Out cross validation had the accuracy of 0.78 and area under the curve of 0.80. Sleep position was found to be the most important amongst all sleep features contributing to the performance.

CONCLUSIONS

Pre-apneic sound presented unique acoustic characteristics and snoring related breathing sound could be deployed as a real-time apneic event predictor. The model combined with sleep information served as a promising tool for an early warning system to forecast apneic events.

Validation of snoring detection using a smartphone app

Purpose

Snoring is closely related to obstructive sleep apnea in adults. The increasing abundance and availability of smartphone technology has facilitated the examination and monitoring of snoring at home through snoring apps. However, the accuracy of snoring detection by snoring apps is unclear. This study explored the snoring detection accuracy of Snore Clock—a paid snoring detection app for smartphones.

Methods

Snoring rates were detected by smartphones that had been installed with the paid app Snore Clock. The app provides information on the following variables: sleep duration, snoring duration, snoring loudness (in dB), maximum snoring loudness (in dB), and snoring duration rate (%). In brief, we first reviewed the snoring rates detected by Snore Clock; thereafter, an ear, nose, and throat specialist reviewed the actual snoring rates by using the playback of the app recordings.

Results

In total, the 201 snoring records of 11 patients were analyzed. Snoring rates measured by Snore Clock and those measured manually were closely correlated (r = 0.907). The mean snoring detection accuracy rate of Snore Clock was 95%, with a positive predictive value, negative predictive value, sensitivity, and specificity of 65% ± 35%, 97% ± 4%, 78% ± 25%, and 97% ± 4%, respectively. However, the higher the snoring rates, the higher were the false-negative rates for the app.

Conclusion

Snore Clock is compatible with various brands of smartphones and has a high predictive value for snoring. Based on the strong correlation between Snore Clock and manual approaches for snoring detection, these findings have validated that Snore Clock has the capacity for at-home snoring detection.

[Application areas of medical apps in otolaryngology]

The use of medical apps is becoming increasingly important as it offers new solutions in healthcare. Steadily growing computing and storage capacities in combination with high-precision sensors make smartphones effective tools for medical diagnostics and treatment. The use of this technology offers immense advantages, such as direct availability or independence from opening times. However, it also harbors risks such as unfiltered data storage and transmission. The consulting physician should exercise great care when selecting and recommending apps, particularly since only a few have been certified as medical devices to date. There is a steadily growing range of products on the market for otorhinolaryngology. The scientific evidence and quality of the apps vary widely, but tools exist for their validation by physicians and patients. The present training course is intended to help increase knowledge in this new, rapidly developing area.

Snoring increases the development of coronary artery disease: a systematic review with meta-analysis of observational studies

PURPOSE

Snoring is one of the cardinal presentations of obstructive sleep apnea (OSA) and is more common than OSA. Abundant evidence has suggested a robust association between OSA and coronary artery disease (CAD). However, whether or not snoring alone is related to a higher risk of CAD is unknown. This study systematically reviewed observational studies with meta-analysis to evaluate the linkage between snoring and CAD.

METHODS AND RESULTS

We searched PubMed and Embase and retrieved 13 articles focusing on the relationship between snoring and CAD. These articles included a total of 151,366 participants and 9099 CAD patients. Quantitative analysis indicated that snoring was associated with a 28% (RR: 1.28, 95% CI: 1.13 to 1.45, P < 0.001) increase in the risk of developing CAD.

CONCLUSIONS

Snorers are exposed to a 28% increased risk for CAD. Although the association may be partly mediated through OSA, most snorers are not affected by apnea. Given the high prevalence of snoring and the disease burden of CAD in the general population, screening for snoring may be worthwhile for the early prevention of CAD.

Sleep apps: current limitations and challenges

Sleep app ownership is increasing exponentially, due to their accessibility and ease-of-use. However, there are several concerns regarding the use of sleep apps. Few sleep apps demonstrate empirical evidence to support their claims, and if they do, this evidence can be based on significant methodological limitations. In addition, there are data privacy concerns with regards to sleep apps, which share sensitive user data with business and marketing partners, unbeknownst to their users. Moreover, sleep apps may increase engagement with healthcare professionals, which may place additional strain on under-pressure sleep services. This would be compounded by the fact that some sleep apps produce many false positives, and clinicians would need more time to analyze the data provided by these apps. In the future, sleep apps must undergo rigorous validation studies and grant more autonomy to their users over how their data is shared.

Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review

Obstructive sleep apnoea (OSA) is a growing and serious worldwide health problem with significant health and socioeconomic consequences. Current diagnostic testing strategies are limited by cost, access to resources and over reliance on one measure, namely the apnoea-hypopnoea frequency per hour (AHI). Recent evidence supports moving away from the AHI as the principle measure of OSA severity towards a more personalised approach to OSA diagnosis and treatment that includes phenotypic and biological traits. Novel advances in technology include the use of signals such as heart rate variability (HRV), oximetry and peripheral arterial tonometry (PAT) as alternative or additional measures. Ubiquitous use of smartphones and developments in wearable technology have also led to increased availability of applications and devices to facilitate home screening of at-risk populations, although current evidence indicates relatively poor accuracy in comparison with the traditional gold standard polysomnography (PSG). In this review, we evaluate the current strategies for diagnosing OSA in the context of their limitations, potential physiological targets as alternatives to AHI and the role of novel technology in OSA. We also evaluate the current evidence for using newer technologies in OSA diagnosis, the physiological targets such as smartphone applications and wearable technology. Future developments in OSA diagnosis and assessment will likely focus increasingly on systemic effects of sleep disordered breathing (SDB) such as changes in nocturnal oxygen and blood pressure (BP); and may also include other factors such as circulating biomarkers. These developments will likely require a re-evaluation of the diagnostic and grading criteria for clinically significant OSA.

Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink™, ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

Automatic Event Detector from Smartphone Accelerometry: Pilot mHealth Study for Obstructive Sleep Apnea Monitoring at Home

Obstructive sleep apnea (OSA) is a common disorder with a low diagnosis ratio, leaving many patients undiagnosed and untreated. In the last decades, accelerometry has been found to be a feasible solution to obtain respiratory activity and a potential tool to monitor OSA. On the other hand, many smartphone-based systems have already been developed to propose solutions for OSA monitoring and treatment. The objective of this work was to develop an automatic event detector based on smartphone accelerometry and pulse oximetry, and to assess its ability to detect thoracic movements. It was validated with a commercial OSA monitoring system at home. Results of this preliminary pilot study showed that the proposed event detector for accelerometry signals is a feasible tool to detect abnormal respiratory events, such as apneas and hypopneas, and has potential to be included in smartphone-based systems for OSA assessment.

Innovations in research and clinical care using patient-generated health data

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Four-Year Trends in Sleep Duration and Quality: A Longitudinal Study Using Data from a Commercially Available Sleep Tracker

Background

Population estimates of sleep duration and quality are inconsistent because they rely primarily on self-reported data. Passive and ubiquitous digital tracking and wearable devices may provide more accurate estimates of sleep duration and quality.

Objective

This study aimed to identify trends in sleep duration and quality in New York City based on 2 million nights of data from users of a popular mobile sleep app.

Methods

We examined sleep duration and quality using 2,161,067 nights of data captured from 2015 to 2018 by Sleep Cycle, a popular sleep-tracking app. In this analysis, we explored differences in sleep parameters based on demographic factors, including age and sex. We used graphical matrix representations of data (heat maps) and geospatial analyses to compare sleep duration (in hours) and sleep quality (based on time in bed, deep sleep time, sleep consistency, and number of times fully awake), considering potential effects of day of the week and seasonality.

Results

Women represented 46.43% (1,003,421/2,161,067) of the sample, and men represented 53.57% (1,157,646/2,161,067) of individuals in the sample. The average age of the sample was 31.0 years (SD 10.6). The mean sleep duration of the total sample was 7.11 hours (SD 1.4). Women slept longer on average (mean 7.27 hours, SD 1.4) than men (mean 7 hours, SD 1.3; P<.001). Trend analysis indicated longer sleep duration and higher sleep quality among older individuals than among younger (P<.001). On average, sleep duration was longer on the weekend nights (mean 7.19 hours, SD 1.5) than on weeknights (mean 7.09 hours, SD 1.3; P<.001).

Conclusions

Our study of data from a commercially available sleep tracker showed that women experienced longer sleep duration and higher sleep quality in nearly every age group than men, and a low proportion of young adults obtained the recommended sleep duration. Future research may compare sleep measures obtained via wearable sleep trackers with validated research-grade measures of sleep.

Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device

OBJECTIVE

To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep.

STUDY DESIGN

Prospective cohort study.

SETTING

Tertiary referral hospital.

SUBJECT AND METHODS

Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation.

RESULTS

In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI.

CONCLUSION

AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.

Sleep/Wakefulness Detection Using Tracheal Sounds and Movements

PURPOSE

The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements.

MATERIALS AND METHODS

Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography.

RESULTS

Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3±8.66% with a sensitivity of 87.8±10.8 % (sleep), specificity of 71.4±18.5% (awake), F1 of 88.1±9.3% and Cohen's kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p<0.001) and 0.70 (p<0.001), respectively.

CONCLUSION

Sleep/wakefulness periods can be detected using tracheal sound and movements. The results of this study combined with our previous studies on screening sleep apnea with tracheal sounds provide strong evidence that respiratory sounds analysis can be used to develop robust, convenient and cost-effective portable devices for sleep apnea monitoring.

Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation

Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis.Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most highly-valued devices. Software apps have also been compared with regards to the market.Expert opinion: Thanks to technological advances, the reliability and accuracy of sensors has been significantly increased in recent years. According to validation studies, some actigraphs present a sensibility higher than 90%. However, the market analysis reveals that many hardware devices have not been validated, and especially software devices should be studied before their clinical use.

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

BACKGROUND

Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations.

OBJECTIVE

This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems.

METHODS

A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems?

RESULTS

Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred.

CONCLUSIONS

One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.

Sleep quality prediction in caregivers using physiological signals

Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

Systematic review of the different aspects of primary snoring

Primary snoring, also known as simple or non-apnoeic snoring, is regarded as the first stage of sleep disordered breathing without severe medical consequences for the snorer and co-sleeper. Although it is a highly prevalent phenomenon in the general population, our knowledge is limited because of the lack of a consensus on terminology. This systematic review of the aspects used in the definitions of simple/primary snoring was conducted to obtain an inventory of current practices and compare these definitions with the conceptual definition of the American Academy of Sleep Medicine. PubMed and Web of Science were searched from July 2016 onwards without any language limitations, and 362 references were obtained. After selection based on titles, 39 remained, among which 29 contained a definition or reference to a definition. In 69% of the studies, a cut-off <5 apnoea/Hypopnoea events per hour of sleep on the Apnoea-Hypopnoea Index was used. Despite this tendency, the cut-offs ranged from 0 to <15/h. Unfortunately, the cut-off and occasional requirements did not match the conceptual definition of the American Academy of Sleep Medicine. A consensus must be reached on an operational and clinically relevant definition based on the clear conceptual definition.