Performance of the Alice PDx Device With the Somnolyzer Automated Scoring Algorithm for the Diagnosis of Obstructive Sleep Apnea.

Objective Automated scoring of respiratory events could allow a swifter obstructive sleep apnea (OSA) identification. We assessed the accuracy of the Alice PDx device with the Somnolyzer automated scoring algorithm, compared to the manually reviewed scoring by a trained sleep technician, for the diagnosis of OSA. Methods A prospective study was conducted between March 2021 and March 2022 in Centro Hospitalar do Baixo Vouga, a level 2 hospital in Aveiro, Portugal. Patients with high pre-test probability for OSA performed a type III home sleep apnea testing with the Alice PDx device. Data were scored automatedly by the Sleepware G3 with the Somnolyzer digital system and manually by a trained sleep technician. Correlation and dependent t-tests were used. Sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and area under the receiver operating characteristic curve (AUROC) of automated scoring were calculated. Data were analyzed using the Stata Statistical Software (Release 17, StataCorp., 2023, College Station, TX: StataCorp LLC). Results In 150 participants (mean age 57.8 ± 13.9 years), the mean apnea-hypopnea index (AHI) was 21.9 ± 21.8 events/hour by manual scoring and 25.4 ± 21.6 events/hour by automated scoring. The mean difference was 3.4 ± 4.4 events/hour, and a strong, positive, linear correlation was found between the two scores (r = 0.98). At the altered AHI (AHI ≥ 5 events/hour), mild, moderate, and severe OSA, the automated scoring sensitivity/specificity values were 91.2%/100.0%, 80.0%/68.6%, 91.6%/41.9%, and 98.1%/80.9%, respectively. The PPVs/NPVs for the same categories were 100.0%/69.4%, 89.3%/51.1%, 79.7%/66.7%, and 91.8%/95.0%, respectively. Finally, the AUROC was 0.85, 0.70, 0.73, and 0.93, respectively. Conclusion The automated scoring obtained from the Alice PDx portable device, using Sleepware G3 with the Somnolyzer digital system, seems accurate enough to diagnose OSA and validate the initiation of PAP therapy in the correct clinical setting. Nevertheless, it does not replace manual reviewing by a trained sleep technician in the case of mild and moderate OSA, to obtain a correct severity classification. With this valuable time-saving tool, we expect to hasten OSA diagnosis and treatment and thus tackle the underdiagnosis problem.

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network.

Objective.Explore a network architecture that can efficiently perform single-lead electrocardiogram (ECG) sleep apnea (SA) detection by utilizing the beneficial information of extended ECG segments and reducing the impact of their noisy information.Approach.We propose an effective deep-shallow fusion network (EDSFnet). The deeper residual network is used to extract high-level features with stronger semantics and less noise from the original ECG segments. The shallower convolutional neural network is used to extract lower-level features with higher resolution containing more detailed neighborhood information from the extended ECG segments. These two types of features are then fused using Effective Channel Attention, implementing automatic weight assignment to take advantage of their complementary nature.Main results.The performance of EDSFnet is evaluated on the Apnea-ECG dataset and the FAH-ECG dataset. In the Apnea-ECG dataset with 35 subjects as the training set and 35 subjects as the test set, the accuracy of EDSFnet was 92.6% and 100% for per-segment and per-recording test, respectively. In the FAH-ECG dataset with 348 subjects as the training set and 88 subjects as the test set, the accuracy of EDSFnet was 89.0% and 93.2% for per-segment and per-recording test, respectively. EDSFnet has achieved state-of-the-art results in both experiments using the publicly available Apnea-ECG dataset and subject-independent experiments using the FAH-ECG clinical dataset.Significance.The success of EDSFnet in handling SA detection underlines its robustness and adaptability. By achieving superior results across different datasets, EDSFnet offers promise in advancing the cost-effective and efficient detection of SA through single-lead ECG, reducing the burden on patients and healthcare systems alike.

Relationship between airflow limitation in response to upper airway negative pressure during wakefulness and obstructive sleep apnea severity.

PURPOSE: The objective was to determine if alteration in airflow induced by negative pressure (NP) applied to participants' upper airways during wakefulness, is related to obstructive sleep apnea (OSA) severity as determined by the apnea-hypopnea index (AHI). METHODS: Adults 18 years of age or greater were recruited. All participants underwent overnight polysomnography to assess their apnea-hypopnea index (AHI). While awake, participants were twice exposed, orally, to -3 cm H2O of NP for five full breaths. The ratio of the breathing volumes of the last two breaths during NP exposure to the last two breaths prior to NP exposure was deemed the NP ratio (NPR). RESULTS: Eighteen participants were enrolled. A strong relationship between the AHI and the exponentially transformed NPR (ExpNPR) for all participants was observed (R2 = 0.55, p < 0.001). A multivariable model using the independent variable ExpNPR, age, body mass index and sex accounted for 81% of variability in AHI (p = 0.0006). A leave-one-subject-out cross-validation analysis revealed that predicted AHI using the multivariable model, and actual AHI from participants' polysomnograms, were strongly related (R2 = 0.72, p < 0.001). CONCLUSION: We conclude that ExpNPR, was strongly related to the AHI, independently of demographic factors known to be related to the AHI.

Symptoms of sleep disordered breathing: Association with the apnea-hypopnea index and somatic arousal.

We investigated the association of typical symptoms of obstructive sleep apnea with a measure of daytime somatic arousal and with the apnea-hypopnea index. We extended the finding of an association between sleepiness, fatigue and somatic arousal previously reported in a US sleep apnea population to a German sleep apnea population (n = 374) and to other typical sleep apnea symptoms, insomnia, anxiety, daytime alertness and non-restorative sleep. Somatic arousal was measured using the body sensation questionnaire (BSQ). Correlations of apnea-hypopnea index and BSQ were computed with values of polysomnographic variables derived from overnight sleep studies and with severity of OSA symptoms. Apnea-hypopnea index and BSQ scores showed only a small negative correlation with each other; each correlated independently with the Epworth Sleepiness Scale score. Controlling for BSQ score, the apnea-hypopnea index was found to affect sleepiness only when it exceeded 50/h. Severity of all other sleep apnea symptoms did not increase with increasing apnea-hypopnea index. In contrast, severity of all symptoms of sleep apnea increased consistently with increasing BSQ scores. Thus, autonomic stress associated with obstructive sleep apnea may be the driving force behind sleep apnea symptoms rather than the sleep fragmentation associated with obstructive sleep apnea severity (apnea-hypopnea index). These findings support previously reported correlations by Gold and associates between the levels of somatic arousal, sleepiness and fatigue. Using the apnea-hypopnea index and BSQ together renders a more comprehensive assessment of sleep apnea than apnea-hypopnea index alone, which appears to impact only on sleepiness and only when it exceeds 50/h. More work is needed to elucidate the source of the chronic stress, which appears to arise endogenously in affected individuals, likely as a function of sleep disordered breathing, such as snoring/inspiratory flow limitation.

Toward standardizing the clinical testing protocols of point-of-care devices for obstructive sleep apnea diagnosis.

PURPOSE: In recent years, point-of-care (POC) devices, especially smart wearables, have been introduced to provide a cost-effective, comfortable, and accessible alternative to polysomnography (PSG)-the current gold standard-for the monitoring, screening, and diagnosis of obstructive sleep apnea (OSA). Thorough validation and human subject testing are essential steps in the translation of these device technologies to the market. However, every device development group tests their device in their own way. No standard guidelines exist for assessing the performance of these POC devices. The purpose of this paper is to critically distill the key aspects of the various protocols reported in the literature and present a protocol that unifies the best practices for testing wearable and other POC devices for OSA. METHODS: A limited review and graphical descriptive analytics of literature-including journal articles, web sources, and clinical manuscripts by authoritative agencies in sleep medicine-are performed to glean the testing and validation methods employed for POC devices, specifically for OSA. RESULTS: The analysis suggests that the extent of heterogeneity of the demographics, the performance metrics, subject survey, hypotheses, and statistical analyses need to be carefully considered in a systematic protocol for testing POC devices for OSA. CONCLUSION: We provide a systematic method and list specific recommendations to extensively assess various performance criteria for human subject testing of POC devices. A rating scale of 1-3 is provided to encourage studies to put a focus on addressing the key elements of a testing protocol.

Diagnosis of sleep apnea without sensors on the patient's face.

STUDY OBJECTIVES: Thermistors, nasal cannulas, and respiratory inductance plethysmography (RIP) are the recommended reference sensors of the American Academy of Sleep Medicine (AASM) for the detection and characterization of apneas and hypopneas; however, these sensors are not well tolerated by patients and have poor scorability. We evaluated the performance of an alternative method using a combination of tracheal sounds (TSs) and RIP signals. METHODS: Consecutive recordings of 70 adult patients from the Pays de la Loire Sleep Cohort were manually scored in random order using the AASM standard signals and the combination TS and RIP signals, without respiratory sensors placed on the patient's face. The TS-RIP scoring used the TS and RIP-flow signals for detection of apneas and hypopneas, respectively, and the suprasternal pressure and RIP belt signals for the characterization of apneas. RESULTS: Sensitivity and specificity of the TS-RIP combination were 96.21% and 91.34% for apnea detection and 89.94% and 93.25% for detecting hypopneas, respectively, with a kappa coefficient of 0.87. For the characterization of apneas, sensitivity and specificity were 98.67% and 96.17% for obstructive apneas, 92.66% and 99.36% for mixed apneas, and 96.14% and 98.89% for central apneas, respectively, with a kappa coefficient of 0.94. The TS-RIP scoring revealed a high agreement for classifying obstructive sleep apnea into severity classes (none, mild, moderate, and severe obstructive sleep apnea) with a Cohen's kappa coefficient of 0.96. CONCLUSIONS: Compared with the AASM reference sensors, the TS-RIP combination allows reliable noninvasive detection and characterization of respiratory events with a high degree of sensitivity and specificity. TS-RIP combination could be used for diagnosis of obstructive sleep apnea in adults, either as an alternative to the AASM sensors or in combination with the recommended AASM sensors.

In-Home Diagnosis of Obstructive Sleep Apnea Using Automatic Video Analysis.

STUDY OBJECTIVES: To evaluate the diagnostic accuracy of a non-invasive technology based on image processing for the identification of obstructive sleep apnea (OSA) and its severity at patients' home. METHODS: Observational, prospective, diagnostic accuracy study to evaluate the degree of measure agreement between Sleepwise (SW), in-laboratory attended polysomnography (PSG) and a home sleep apnea test (HSAT). 38 consecutive subjects with suspected OSA referred as outpatients to the sleep unit were recruited from September 2016 to September 2017. All patients underwent in-laboratory attended PSG and image processing with SW simultaneously overnight. Subsequently, a HSAT and image processing with SW were performed simultaneously overnight at patients' home, and the 2 nights after, patients underwent only image processing with SW consecutively. RESULTS: In-laboratory polysomnography and SW had a Lin's concordance correlation coefficient of 0.933 and a κ of 0.930. Between HSAT and SW the Lin's concordance correlation coefficient was 0.842 and a κ of 0.571. Agreement between two consecutive nights with SW recording showed a Lin's concordance correlation coefficient of 0.923 and a κ of 0. 837. CONCLUSIONS: SW was highly accurate for non-invasive and automatic diagnosis of OSA in outpatients compared to standard methods for OSA diagnosis either in-laboratory attended PSG or HSAT. SW proved to be a technique with repeatable and concordant results on different nights for the same patient. We conclude SW is a non-invasive, easy-to-use, portable, effective and highly accurate system for the in-home diagnosis of OSA.

Obstructive sleep apnea and nocturnal hypoxemia are associated with an increased risk of lung cancer.

STUDY OBJECTIVES: To identify a link between sleep disordered breathing, nocturnal hypoxemia, and lung cancer. METHODS: We conducted a cross-sectional study of a combined cohort of 302 individuals derived from the sleep apnea in lung cancer study (SAIL; NCT02764866) investigating the prevalence of sleep apnea in lung cancer, and the sleep apnea in lung cancer screening study (SAILS; NCT02764866) investigating the prevalence of sleep apnea in a lung cancer screening program. All subjects had spirometry and a chest CT, underwent home sleep apnea testing (HSAT), and completed a sleep related questionnaire. Subjects from the SAIL study underwent HSAT prior to initiating oncologic therapy or surgery. Subjects with an apnea-hypopnea index (AHI) > 15 were compared with a control group of individuals with an AHI < 15. Propensity score, near neighbor matching, and logistic regression adjusted for potential confounders, were used in order to evaluate the association between sleep apnea, the AHI, oxygen desaturation indices and lung cancer. RESULTS: The prevalence of sleep apnea and lung cancer in the combined cohort was 42% and 21%, respectively. Lung cancer was 8% more prevalent in patients with an AHI >15. The difference was statistically significant when assessed by propensity score matching (p = 0.015) and nearest neighbor matching (p = 0.041). Binary logistic regression adjusted for potential confounders revealed a statistically significant association between AHI (p = 0.04), nocturnal hypoxemia, including time spent below 90% oxyhemoglobin saturation (T90%; p = 0.005), 3% oxygen desaturation index (ODI3; p = 0.02) and lung cancer. CONCLUSIONS: Sleep apnea and nocturnal hypoxemia are associated with an increased prevalence of lung cancer. CLINICAL TRIAL REGISTRATION: SAIL study (NCT02764866) and SAILS study (NCT02764866).

Design and Evaluation of a Non-ContactBed-Mounted Sensing Device for AutomatedIn-Home Detection of Obstructive Sleep Apnea:A Pilot Study.

We conducted a pilot study to evaluate the accuracy of a custom built non-contactpressure-sensitive device in diagnosing obstructive sleep apnea (OSA) severity as an alternative toin-laboratory polysomnography (PSG) and a Type 3 in-home sleep apnea test (HSAT). Fourteenpatients completed PSG sleep studies for one night with simultaneous recording from ourload-cell-based sensing device in the bed. Subjects subsequently installed pressure sensors in theirbed at home and recorded signals for up to four nights. Machine learning models were optimized toclassify sleep apnea severity using a standardized American Academy of Sleep Medicine (AASM)scoring of the gold standard studies as reference. On a per-night basis, our model reached a correctOSA detection rate of 82.9% (sensitivity = 88.9%, specificity = 76.5%), and OSA severity classificationaccuracy of 74.3% (61.5% and 81.8% correctly classified in-clinic and in-home tests, respectively).There was no difference in Apnea Hypopnea Index (AHI) estimation when subjects wore HSATsensors versus load cells (LCs) only (p-value = 0.62). Our in-home diagnostic system providesan unobtrusive method for detecting OSA with high sensitivity and may potentially be used forlong-term monitoring of breathing during sleep. Further research is needed to address the lowerspecificity resulting from using the highest AHI from repeated samples.

The clinical diagnosis of Obstructive Sleep Apnea Syndrome (OSAS). / La diagnosi clinic-strumentale della Sindrome delle Apnee Ostrutiive nel Sonno (OSAS).

Obstructive Sleep Apnea Syndrome (OSAS) is the most frequent sleep breathing disorder in the general population. To reach a correct diagnosis, the clinical work-up requires the association of comprehensive clinical evaluation (anamnesis, physical examination) and nocturnal polysomnography. Polysomnographic recordings may differ by number of bio-parameters recorded and setting (in laboratory or at home), and allow the identification of other sleep disorders in addition to the diagnosis of OSAS. Excessive daytime sleepiness (EDS) is the most frequent daytime complaint of OSAS patients. Its evaluation is fundamental in subjects with suspected OSAS and concomitant high risk of sleep-related accidents due to work-related factors (e.g. professional drivers). To test EDS, physicians may use subjective (questionnaires) and/or objective (polysomnografic or performance tests) measures. Objective tests are more advisable, but to date they are time consuming and expensive. Objective tests less time-consuming and easily applicable to clinic practice are being evaluated.

Diagnosing and managing sleep apnea in patients with chronic cerebrovascular disease: a randomized trial of a home-based strategy.

BACKGROUND: Obstructive sleep apnea is common and associated with poor outcomes after stroke or transient ischemic attack (TIA). We sought to determine whether the intervention strategy improved sleep apnea detection, obstructive sleep apnea (OSA) treatment, and hypertension control among patients with chronic cerebrovascular disease and hypertension. METHODS: In this randomized controlled strategy trial intervention, patients received unattended polysomnography at baseline, and patients with OSA (apnea-hypopnea index ≥5 events/h) received auto-titrating continuous positive airway pressure (CPAP) for up to 1 year. Control patients received usual care and unattended polysomnography at the end of the study, to identify undiagnosed OSA. Both groups received 24-h blood pressure assessments at baseline and end of the study. "Excellent" CPAP adherence was defined as cumulative use of ≥4 h/night for ≥70% of the nights. RESULTS: Among 225 randomized patients (115 control; 110 intervention), 61.9% (120/194) had sleep apnea. The strategy successfully diagnosed sleep apnea with 97.1% (102/105) valid studies; 90.6% (48/53, 95% CI 82.7-98.4%) of sleep apnea was undiagnosed among control patients. The intervention improved long-term excellent CPAP use: 38.6% (22/57) intervention versus 0% (0/2) control (p < 0.0001). The intervention did not improve hypertension control in this population with well-controlled baseline blood pressure: intervention, 132.7 mmHg (±standard deviation, 14.1) versus control, 133.8 mmHg (±14.0) (adjusted difference, -1.1 mmHg, 95% CI (-4.2, 2.0)), p = 0.48). CONCLUSIONS: Patients with cerebrovascular disease and hypertension have a high prevalence of OSA. The use of portable polysomnography, and auto-titrating CPAP in the patients' homes, improved both the diagnosis and the treatment for sleep apnea compared with usual care but did not lower blood pressure.

A pilot study: portable out-of-center sleep testing as an early sleep apnea screening tool in acute ischemic stroke.

INTRODUCTION: Prompt diagnosis of obstructive sleep apnea (OSA) after acute ischemic stroke (AIS) is critical for optimal clinical outcomes, but in-laboratory conventional polysomnograms (PSG) are not routinely practical. Though portable out-of-center type III cardiopulmonary sleep studies (out-of-center cardiopulmonary sleep testing [OCST]) are widely available, these studies have not been validated in patients who have recently suffered from AIS. We hypothesized that OCST in patients with AIS would yield similar results when compared to conventional PSG. METHODS: Patients with AIS had simultaneous type III OCST and PSG studies performed within 72 hours from symptom onset. The accuracy of OCST was compared to PSG using: chi-square tests, receiver operatory characteristic curves, Bland-Altman plot, paired Student's t-test/Wilcoxon signed-rank test, and calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Twenty-one out of 23 subjects with AIS (age 61±9.4 years; 52% male; 58% African-American) successfully completed both studies (9% technical failure). Nearly all (95%) had Mallampati IV posterior oropharynx; the mean neck circumference was 16.8±1.6 in. and the mean body mass index (BMI) was 30±7 kg/m(2). The apnea hypopnea index (AHI) provided by OCST was similar to that provided by PSG (19.8±18.0 vs 22.0±22.7, respectively; P=0.49). On identifying subjects by OCST with an AHI ≥5 on PSG, OCST had the following parameters: sensitivity 100%, specificity 85.7%, PPV 93%, and NPV 100%. On identifying subjects with an AHI ≥15 on PSG, OCST parameters were as follows: sensitivity 100%, specificity 83.3%, PPV 81.8%, and NPV 100%. Bland-Altman plotting showed an overall diagnostic agreement between OCST and PSG modalities for an AHI cutoff >5, despite fine-grained differences in estimated AHIs. CONCLUSION: Compared with PSG, OCST provides similar diagnostic information when run simultaneously in AIS patients. OCST is a reliable screening tool for early diagnosis of OSA in AIS patients.

A pacemaker transthoracic impedance sensor with an advanced algorithm to identify severe sleep apnea: the DREAM European study.

BACKGROUND: Sleep apnea (SA) is associated with cardiovascular diseases and is highly prevalent in patients with pacemakers (PMs). OBJECTIVE: To validate a transthoracic impedance sensor with an advanced algorithm (sleep apnea monitoring) for identifying severe SA. METHODS: Patients with indications for PM (VVI/DDD) were enrolled regardless of symptoms suggesting SA. Severe SA diagnosis was acknowledged when the full polysomnography gave an apnea-hypopnea index (PSG-AHI) of ≥30 events/h. The PSG-AHI was compared with the respiratory disturbance index evaluated by the SAM algorithm (SAM-RDI) compiled from the device during the same diagnosis night, and the performance of the device and the SAM algorithm was calculated to identify patients with severe SA. The agreement between methods was assessed by using Bland and Altman statistics. RESULTS: Forty patients (mean age 73.8 ± 19.1 years; 67.5% men; body mass index 27.7 ± 4.4 kg/m(2)) were included. Severe SA was diagnosed by PSG in 56% of the patients. We did not retrieve SAM-RDI data in 14% of the patients. An optimal cutoff value for the SAM-RDI at 20 events/h was obtained by a receiver operator characteristic curve analysis, which yielded a sensitivity of 88.9% (95% confidence interval [CI] 65.3%-98.6%), a positive predictive value of 88.9% (95% CI 65.3%-98.6%), and a specificity of 84.6% (95% CI 54.6%-98.1%) (n = 31). The Bland-Altman limits of agreement for PSG-AHI (in events per hour) were [-14.1 to 32.4]. CONCLUSION: The results suggest that an advanced algorithm using PM transthoracic impedance could be used to identify SA in patients with PMs outside the clinic or at home.