Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry

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.

Automatic monitoring of obstructive sleep apnea based on multi-modal signals by phone and smartwatch

Obstructive Sleep Apnea (OSA) is the most common sleep-related breathing disorder, with an overall population prevalence ranging from 9% to 38%, and it is associated with many cardiovascular diseases. The diagnosis of OSA requires polysomnography (PSG) testing, which is unsuitable for large-scale preliminary screening due to its high cost and discomfort to wear. Therefore, a simple and inexpensive screening method would be of great value. This study presents a novel at-home OSA screening method using a smartwatch and a smartphone to obtain several physiological signals, snoring segments, and questionnaire information during a whole night's sleep. The proposed method can distinguish four OSA risk levels based on machine learning (ML) classifications; the system was validated by conducting an in-hospital study on 350 subjects with sleep disorders. The estimated OSA risk levels are in good agreement with the OSA severity diagnosed by PSG (correlation with apnea-hypopnea index (AHI) = 0.92), and an encouraging classification performance is achieved (accuracy = 88.1%, 84.5%, 85.1%, sensitivity = 89.1%, 84.2%, 85.6% for mild, moderate and severe OSA). These findings reveal that wearable devices have the potential for large-scale OSA screening.

Improving the Duration and Rate of Home-Based Kangaroo Mother Care: A Before-and-After Intervention Study

Introduction Kangaroo mother care (KMC) is an evidence-based, simple, time-tested, low-cost, and high-impact intervention for neonatal survival in hospitals and the community, particularly in resource-constrained areas. This has many beneficial effects on sick and stable low-birth-weight babies, lactating mothers, families, society, and the government. However, despite the World Health Organization (WHO) and United Nations International Children's Emergency Fund (UNICEF) recommendations for KMC, there is no satisfactory implementation of it in the community as well as in facilities. This study aimed to improve the duration of home-based kangaroo mother care (HBKMC). Material and methods  We conducted a before-and-after intervention hospital-based, single-center study in a level III neonatal intensive care unit (NICU) to improve the duration of HBKMC. The KMC duration was classified into four categories: short, extended, long, and continuous where KMC was provided for 4 hours/day, 5-8 hours/day, 9-12 hours/day, and more than 12 hours/day, respectively. All neonates with birth weight < 2.0 kg and their mothers/alternate KMC providers at a tertiary care hospital in India in the time period of five months from April 2021 to July 2021 were considered eligible for the study. We tested three sets of interventions by using the plan-do-study-act cycle (PDSA cycle). The first set of interventions was the sensitization of parents and healthcare workers regarding the benefits of KMC by comprehensive counseling to mothers and other family members using educational lectures, videos, charts, and posters. The second set of interventions was to reduce maternal anxiety/stress while maintaining maternal privacy by providing more female staff and teaching proper gown-wearing techniques. The third set of interventions was to solve lactation and environment temperature issues by providing antenatal and postnatal lactation counseling and warming of the nursery. The paired T-test and one-way analysis of variance (ANOVA) were used for statistical analysis, and p<0.05 was taken as significant.  Results  One hundred and eighty neonates were enrolled along with their mothers/alternate KMC providers in four phases, and three PDSA cycles were implemented. Out of 180 LBW infants, 21 (11.67%) infants received KMC < 4 hours/day. According to the KMC classification, 31% have continuous KMC in the institution, followed by 24% long KMC, 26% extended KMC, and 18% short KMC. After three PDSA cycles, HBKMC was 38.88% continuous KMC, followed by 24.22% long KMC, 20.55% extended KMC, and 16.11% short KMC. Continuous KMC was improved from 21% to 46% at the institute and 16% to 50% at home from phase 1 to phase 4 of the study after the implementation of three sets of interventions in three PDSA cycles. The phase-by-phase KMC rate and duration were improved after the application of the PDSA cycles, and this was maintained in HBKMC as well, but it was statistically not significant. Conclusion Sets of intervention packages based on needs analysis using the PDSA cycle were able to improve the rate and duration of KMC in the hospital and at home.

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 Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

Association of sleep-disordered breathing and wound healing in patients with diabetic foot ulcers

STUDY OBJECTIVES

Sleep-disordered breathing (SDB) is prevalent and associated with an increased risk of morbidity and mortality. However, whether SDB has an adverse impact on wound healing in patients with diabetic foot ulcers (DFUs) is uncertain. The purpose of this study was to investigate the association of SDB with wound healing in patients with DFUs.

METHODS

A total of 167 patients with DFUs were enrolled between July 2013 and June 2019 at West China Hospital (Chengdu, China) to assess the association of SDB with wound healing, ulcer recurrence, and all-cause mortality.

RESULTS

Whereas there was no significant association between apnea-hypopnea index (AHI) and wound healing, total sleep time (per hour: hazard ratio [HR], 1.15; 95% confidence interval [CI], 1.01-1.30; P = .029), sleep efficiency (per 10%: HR, 1.20; 95% CI, 1.04-1.37; P = .012), and wakefulness after sleep onset (per 30 minutes: HR, 0.89; 95% CI, 0.82-0.97; P = .008) were associated with wound healing. Total sleep time (per hour: odds ratio, 0.71; 95% CI, 0.51-0.97; P = .035) and sleep efficiency (per 10%: odds ratio, 0.68; 95% CI, 0.47-0.97; P = .033) were also associated with ulcer recurrence. Mean oxygen saturation (per 3%: HR, 0.68; 95% CI, 0.49-0.94; P = .021) and percentage of sleep time with oxygen saturation < 90% (per 10%: HR, 1.25; 95% CI, 1.03-1.53; P = .026) were significantly associated with mortality.

CONCLUSIONS

SDB is highly prevalent in patients with DFUs but its severity, as conventionally measured by AHI, is not associated with wound healing. Sleep fragmentation and hypoxemia are stronger predictors of poor wound healing, high ulcer recurrence, and increased risk of death in patients with DFUs.

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes

BACKGROUND

Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes.

METHODS

A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models.

RESULTS

ODI was strongly correlated with apnea-hypopnea index (AHI) (r_s = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively.

CONCLUSIONS

ODI is a sensitive and specific tool to predict SDB in patients with diabetes.

Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repeated airflow partial reduction or complete cessation due to upper airway collapse during sleep. OSAHS can induce frequent awake and intermittent hypoxia that is associated with hypertension and cardiovascular events. Full-channel Polysomnography (PSG) is the gold standard for diagnosing OSAHS; however, this PSG evaluation process is unsuitable for home screening. To solve this problem, a measuring module integrating abdominal and thoracic triaxial accelerometers, a pulsed oximeter (SpO₂) and an electrocardiogram sensor was devised in this study. Moreover, a long short-term memory recurrent neural network model is proposed to classify four types of sleep breathing patterns, namely obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP) events and normal breathing (NOR). The proposed algorithm not only reports the apnea-hypopnea index (AHI) through the acquired overnight signals but also identifies the occurrences of OSA, CSA, HYP and NOR, which assists in OSAHS diagnosis. In the clinical experiment with 115 participants, the performances of the proposed system and algorithm were compared with those of traditional expert interpretation based on PSG signals. The accuracy of AHI severity group classification was 89.3%, and the AHI difference for PSG expert interpretation was 5.0±4.5. The overall accuracy of detecting abnormal OSA, CSA and HYP events was 92.3%.

Automatic System for Obstructive Sleep Apnea Events Detection Using Convolutional Neural Network

Obstructive Sleep Apnea (OSA) is characterized by repetitive episodes of airflow reduction (hypopnea) or cessation (apnea), which, as a prevalent sleep disorder, can cause people to stop breathing for 10 to 30 seconds at a time and lead to serious problems such as daytime fatigue, impaired memory, and depression. This work intends to explore automatic detection of OSA events with 1-second annotation based on blood oxygen saturation, oronasal airflow, and ribcage and abdomen movements. Deep Learning (DL) technology, specifically, Convolutional Neural Network (CNN), is employed as a feature detector to learn the characteristics of the highorder correlation among visible data and corresponding labels. A fully-connected layer in the last stage of the CNN is connected to the output layer and constructs the desired number of outputs for sleep apnea events classification. A leave-one-out cross-validation has been conducted on the PhysioNet Sleep Database provided by St. Vincents University Hospital and University College Dublin, and an average accuracy of $79 .61$% across normal, hypopnea, and apnea, classes is achieved.