Sleep duration and social jetlag in healthy adolescents. Association with anxiety, depression, and chronotype: a pilot study.

PURPOSE: Misalignment between sleep opportunity and chronotype preference during adolescence may affect sleep and mental health. The aim of this study was to objectively evaluate sleep duration and social jetlag (SJL) to observe if there is a relationship with anxiety, depression, or chronotype. METHODS: Community based cohort study (n = 65) was conducted in Northern Europe. Participants recorded their sleep during the regular school schedule for 3 school nights and 2 free nights with FDA-cleared/EU Medical Device Directive (CE-02862) compliant home sleep test. They also completed validated questionnaires to assess (Morningness Eveningness Questionnaire; MEQ), sleepiness (Epworth Sleepiness Scale; ESS), and insomnia (Insomnia Severity Index; ISI), anxiety (General Anxiety Disorder-7; GAD-7), and depression symptoms (Beck's Depression Inventory-II; BDI-II). Data were collected during the last week of February and the first 2 weeks of March, 2023. RESULTS: Average sleep duration on school nights was 7 h, 15 min, with 18% of participants on average sleeping ≥ 8 h. Average sleep onset was significantly later on free nights (1 hour, 47 minutes; p < 0.0001) causing a high prevalence of moderate-severe SJL, in 71% of participants. Evening chronotypes (prevalence 15.4%) fell asleep later on free nights, causing them to have significantly more SJL (2 hours, 58 minutes) than morning chronotypes (1 hour, 32 minutes) and intermediary chronotypes (1 hour, 36 minutes). Evening chronotypes also had more severe insomnia (ISI + 4.4, p = 0.006; + 4.0, p = 0.001) and greater depressive symptoms (BDI-II + 21.6, p < 0.0001; + 17.1, p < 0.0001). A significant negative correlation was observed between MEQ scores and ESS (r = - 0.287; p = 0.001), ISI (r = - 0.343, p < 0.0001), GAD-7 (r = - 0.185, p < 0.0001), BDI-II (r = - 0.501, p = 0.0001), and suicidal thoughts (r = - 0.294, p = 0.017). CONCLUSION: Adolescents are sleep-deprived and have a high prevalence of SJL that is positively correlated with depressive symptoms and evening chronotype. That evening chronotypes have shorter sleep duration, more severe SJL, and significantly more sleepiness and insomnia, anxiety, and depressive symptoms may indicate possible benefits in identifying this group with the aim to assist them in improving their sleep habits with potentially positive effects on mental health.

Sleep quality, sleep apnea, and metabolic health in children treated with adenotonsillectomy.

PURPOSE: The aim of this study was to determine if cardiopulmonary coupling (CPC) calculated sleep quality (SQI) may predict changes in metabolic health in children treated with early adenotonsillectomy (eAT) for obstructive sleep apnea (OSA). METHODS: Secondary analysis of the Childhood Adenotonsillectomy Trial (CHAT) was performed including children 5.0-9.9 years with OSA assigned to eAT. The cohort was stratified based on SQI and AHI to evaluate (1) response to eAT in children with high sleep quality (SQI ≥ 75) and mild-OSA, AHI < 5.0 (group1) and children with moderate-OSA AHI ≥ 5.0 or SQI < 75 (group2) at baseline and (2) effect of eAT therapy on metabolic health, GroupRemission (AHI < 1.0, SQI ≥ 75) compared to GroupResidual. RESULTS: At baseline group2 (n=124) had higher average heart rate during sleep (AHRSleep), 87 vs. 81 beats/minute (p < 0.001) compared to group1 (n=72). After surgery, group2 on average had less increase in BMI z-score 0.13 vs. 0.27, (p = 0.025), improved their SQI + 2.06 compared to decline - 3.75 in group1, (p = 0.015), decreased AHRSleep-- 2.90 vs. - 0.34 (p = 0.025) and AHI - 5.00 vs. - 0.36 (p = 0.002). GroupRemission was younger 6.59 vs. 7.41; p < 0.001; with lower BMI z-score 0.90 vs. 1.34; p = 0.021; AHRSleep 80.60 vs. 83.50; p = 0.032; fasting insulin (µIU/ml) 7.54 vs. 12.58; p = 0.017 and glucose (mmol/L) 4.45 vs. 4.60; p = 0.049, with better lipid metabolism though not statistically significantly, low-density-lipoprotein 90.26 mg/dL vs. 97.94; p = 0.081 and cholesterol 154.66 mg/dL vs. 164.36; p = 0.076. CONCLUSION: The results may indicate that children with mild-OSA and high-SQI may be less likely to benefit from eAT than children with moderate-OSA. To improve metabolic health, successfully treating both AHI and SQI is likely needed. CPC-calculated SQI may have a role to identify children less likely to benefit from eAT and to evaluate success of therapy. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00560859.

Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity.

STUDY OBJECTIVES: The main cause of death in patients with obesity hypoventilation syndrome (OHS) is cardiac rather than respiratory failure. Here, we investigated autonomic-respiratory coupling and serum cardiac biomarkers in patients with OHS and obstructive sleep apnea (OSA) with comparable body mass index and apnea-hypopnea index. METHODS: Cardiopulmonary coupling (CPC) and cyclic variation of heart rate analysis was performed on the electrocardiogram signal from the overnight polysomnogram. Cardiac serum biomarkers were obtained in patients with OHS and OSA with a body mass index > 40 kg/m2. Samples were obtained at baseline and after 3 months of positive airway pressure (PAP) therapy in both groups. RESULTS: Patients with OHS (n = 15) and OSA (n = 36) were recruited. No group differences in CPC, cyclic variation of heart rate, and serum biomarkers were observed at baseline and after 3 months of PAP therapy. An improvement in several CPC metrics, including the sleep apnea index, unstable sleep (low-frequency coupling and elevated low-frequency coupling narrow band), and cyclic variation of heart rate were observed in both groups with PAP use. However, distinct differences in response characteristics were noted. Elevated low-frequency coupling narrow band coupling correlated with highly sensitive troponin-T (P < .05) in the combined cohort. Baseline highly sensitive troponin-T inversely correlated with awake oxygen saturation in the OHS group (P < .05). CONCLUSIONS: PAP therapy can significantly improve CPC stability in patients with obesity with OSA or OHS, with key differences. Elevated low-frequency coupling narrow band may function as a surrogate biomarker for early subclinical cardiac disease. Low awake oxygen saturation could also increase this biomarker in OHS. CLINICAL TRIAL REGISTRATION: Registry: Australian New Zealand Clinical Trials Registry; Name: Obesity Hypoventilation Syndrome and Neurocognitive Dysfunction; URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367492; Identifier: ACTRN12615000122550. CITATION: Sivam S, Wang D, Wong KKH, et al. Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity. J Clin Sleep Med. 2022;18(4):1063-1071.

Characteristics of Children Likely to Have Spontaneous Resolution of Obstructive Sleep Apnea: Results from the Childhood Adenotonsillectomy Trial (CHAT).

OBJECTIVE: To evaluate if cardiopulmonary coupling (CPC) calculated sleep quality (SQI) may have a role in identifying children that may benefit from other intervention than early adenotonsillectomy (eAT) in management of obstructive sleep apnea (OSA). METHODS: A secondary analysis of electrocardiogram-signals (ECG) and oxygen saturation-data (SpO2) collected during polysomnography-studies in the prospective multicenter Childhood Adenotonsillectomy Trial (CHAT) to calculate CPC-SQI and apnea hypopnea index (AHI) was executed. In the CHAT, children 5-9 years with OSA without prolonged oxyhemoglobin desaturations were randomly assigned to adenotonsillectomy (eAT) or watchful waiting with supportive care (WWSC). The primary outcomes were to document change in attention and executive function evaluated with the Developmental Neuropsychological Assessment (NEPSY). In our analysis, children in the WWSC-group with spontaneous resolution of OSA (AHIObstructive < 1.0) and high-sleep quality (SQI ≥ 75) after 7-months were compared with children that showed residual OSA. RESULTS: Of the 227 children randomized to WWSC, 203 children had available data at both baseline and 7-month follow-up. The group that showed resolution of OSA at month 7 (n = 43, 21%) were significantly more likely to have high baseline SQI 79.96 [CI95% 75.05, 84.86] vs. 72.44 [CI95% 69.50, 75.39], p = 0.005, mild OSA AHIObstructive 4.01 [CI95% 2.34, 5.68] vs. 6.52 [CI95% 5.47, 7.57], p= 0.005, higher NEPSY-attention-executive function score 106.22 [CI95% 101.67, 110.77] vs. 101.14 [CI95% 98.58, 103.72], p = 0.038 and better quality of life according to parents 83.74 [CI95% 78.95, 88.54] vs. 77.51 [74.49, 80.53], p = 0.015. The groups did not differ when clinically evaluated by Mallampati score, Friedman palate position or sleep related questionnaires. CONCLUSIONS: Children that showed resolution of OSA were more likely to have high-SQI and mild OSA, be healthy-weight and have better attention and executive function and quality of life at baseline. As this simple method to evaluate sleep quality and OSA is based on analyzing signals that are simple to collect, the method is practical for sleep-testing, over multiple nights and on multiple occasions. This method may assist physicians and parents to determine the most appropriate therapy for their child as some children may benefit from WWSC rather than interventions. If the parameters can be used to plan care a priori, this would provide a fundamental shift in how childhood OSA is diagnosed and managed.

Can improvements in sleep quality positively affect serum adiponectin-levels in patients with obstructive sleep apnea?

BACKGROUND: Assess if changes in sleep quality (Sleep Quality Index, SQI) based on cardiopulmonary coupling-analysis (CPC) impacts serum adiponectin-levels in patients with cardiovascular disease (CVD). METHODS: Secondary analysis of electrocardiogram (ECG) data from the Heart Biomarker Evaluation in Apnea Treatment study (HeartBEAT), a multicenter, controlled trial in patients with CVD and moderate-severe sleep apnea, randomly assigned to intervention of Continuous Positive Airway Pressure (CPAP), Nocturnal Supplemental Oxygen (NSO) or Healthy Lifestyle and Sleep Hygiene Education (HLSE; control group). Participants with good-quality ECG-signal (n = 241) were included. RESULTS: Improving CPC-sleep quality was associated with net average improvements in serum adiponectin-levels 2.69 µg/ml (p = 0.005) irrespective of therapy initiated. After controlling for confounders, a unit increase in SQI was associated with increase in serum adiponectin-levels 0.071 µg/ml (p = 0.012) and decrease in insulin-levels 0.197 µIU/ml (p = 0.0018). Similarly, a percentage point increase in sleep apnea indicator (SAI) was associated with decrease in serum adiponectin-levels of 0.071 µg/ml (p = 0.017) and increase in insulin-levels of 0.218 µIU/ml (p = 0.020). A percentage point increase in CPC-sleep fragmentation (eLFCBB) had a predicted increase in glucose-levels 0.371 mg/dl (p = 0.009) and insulin-levels 0.284 µIU/ml (p = 0.010). In patients receiving CPAP-therapy, a difference in serum adiponictin levels of 3.82 µg/ml (p = 0.025) is observed comparing patients in which SQI-improved to patients that SQI-declined during the study period. The difference is mostly due to a decrease in serum adiponectin levels in patients that decline in SQI (-3.20 µg/ml). CONCLUSION: Improvements in sleep quality were associated with higher serum adiponectin-levels, and improved measures of glycemic metabolism which may have beneficial effects on metabolic syndrome and cardiovascular health. CLINICAL TRIAL REGISTRATION NAME AND NUMBER: The Heart Biomarker Evaluation in Apnea Treatment (HeartBEAT) study is registered at https://clinicaltrials.gov/ct2/show/NCT01086800.

Automated Apnea-Hypopnea Index from Oximetry and Spectral Analysis of Cardiopulmonary Coupling.

Rationale: The increased prevalence of obstructive sleep apnea (OSA) coincides with a severe shortage of sleep physicians. There is a need for widescale home-sleep-testing devices with accurate automated scoring to accelerate access to treatment.Objectives: To examine the accuracy of an automated apnea-index (AHI) derived from spectral analysis of cardiopulmonary coupling (CPC) extracted from electrocardiograms, combined with oximetry signals, in relation to polysomnograms (PSGs).Methods: Electrocardiograms and pulse-oximeter tracings on PSGs from APPLES (Apnea Positive Pressure Long-term Efficacy Study) were analyzed. Distinct CPC spectral bands were combined with the oxygen desaturation index to create a derived AHI (DAHI). Correlation statistics between the DAHI and the conventionally scored AHI, in which hypopneas required ≥50% airflow reduction alone or a lesser airflow reduction associated with ≥3% desaturation or arousal, using PSGs from APPLES were calculated.Results: A total of 833 adult subjects were included. The DAHI has excellent and strong correlation with the conventionally scored AHI on PSGs, with Pearson coefficients of 0.972 and receiver operating characteristic curves demonstrating strong agreement in all OSA categories: 98.5% in mild OSA (95% confidence interval [CI], 97.6-99.3%), 96.4% in moderate OSA (95% CI, 95.3-97.5%), and 98.5% in severe OSA (95% CI, 97.8-99.2%).Conclusions: An accurate automated AHI can be derived from oximetry and CPC.

Cardiopulmonary coupling-derived sleep quality is associated with improvements in blood pressure in patients with obstructive sleep apnea at high-cardiovascular risk.

OBJECTIVE: Investigate if changes in objective sleep quality index (SQI) assessed through cardiopulmonary-coupling analysis impacts blood pressure (BP) in patients with obstructive sleep apnea at high-cardiovascular risk. METHODS: Secondary analysis of ECG and pulse-oximetry-[oxygen saturation (SpO2)] data from the Heart Biomarker Evaluation in Apnea Treatment study, multicenter, controlled trial in patients with cardiovascular disease and moderate-severe obstructive sleep apnea, randomly assigned to intervention of healthy lifestyle and sleep hygiene education (HLSE; control group), continuous positive airway pressure (CPAP) or nocturnal supplemental oxygen (NSO). Participants with good-quality ECG-signal and SpO2-signal (n = 241) were included. RESULTS: CPAP-therapy significantly improved BP, with net average improvement in mean arterial blood pressure during sleep (MAP) when compared with nocturnal supplemental oxygen-therapy or healthy lifestyle and sleep education-therapy, -3.92 (P = 0.012) and -3.83 (P = 0.016), respectively. When stratified on the basis of baseline-SQI, CPAP-therapy improves 24-h MAP -3.02 (P = 0.030) and MAP -5.00 (P = 0.001), in patients with compromised baseline-SQI (SQI < 55). Stratifying the cohort based on changes in SQI during the study period (SQI-SQI), controlling for sex, age over 60, apnea-hypopnea index, SpO2 less than 80%, baseline BP and cardiovascular disease, significant differences are observed comparing the groups that Improved-SQI (SQI < 55, SQI ≥ 55) and Declined-SQI (SQI ≥ 55, SQI < 55) in MAP -4.87 (P = 0.046) and mean diastolic blood pressure (MDP) -4.42 (P = 0.026) as well as MAP -6.36 (P = 0.015), mean systolic blood pressure wake (MSP) -7.80 (P = 0.048) and MDP -5.64 (P = 0.009), respectively. Improved SQI reflects the magnitude of positive effect on BP which is reached mostly through initiation of CPAP-therapy. CONCLUSION: Cardiopulmonary coupling-derived sleep quality impacted 24-h MAP and MDP, as well as BP during wake, in patients participating in the Heart Biomarker Evaluation in Apnea Treatment-study.

Sleep apnea diagnosis in children using software-generated apnea-hypopnea index (AHI) derived from data recorded with a single photoplethysmogram sensor (PPG) : Results from the Childhood Adenotonsillectomy Study (CHAT) based on cardiopulmonary coupling analysis.

OBJECTIVE: Sleep quality is vital for healthy development in children. Sleep disorders are prevalent and negatively affect sleep quality. Early identification and appropriate intervention can improve children's health and quality of life. The current reference standard, polysomnography (PSG) has limitations regarding availability, cost, and access and may not replicate normal sleep patterns in the home. Simple, accurate sleep tests, available for repeated testing should be beneficial in management of sleep disorders. METHOD: Secondary analysis of PSG data from the prospective multicenter Childhood Adenotonsillectomy Trial (CHAT) to evaluate FDA-cleared cloud-based software (Software-as-a-Medical-Device), which is based on analysis of photoplethysmogram data (PPG; plethysmogram-signal (PLETH) and oxygen saturation data (SpO2)), to automatically generate a novel apnea-hypopnea index (sAHI). sAHI is compared to manually scored AHI from PSG. RESULTS: Significant correlation is observed comparing the software-generated sAHI and manually derived AHI from the in-laboratory PSG-studies (Pearson correlation = 0.954, p < 0.0001) and receiver operating characteristics (ROC) demonstrate strong agreement in all OSA categories (mild, moderate, severe) 91.4%[CI95%89.5, 93.4]; 96.7%[CI95%95.4, 97.9]; 98.6%[CI95%97.8, 99.4], sensitivities 95.4%[CI95%93.2, 97.0]; 86.5%[CI95%80.3, 91.3]; 88.4%[CI95%78.4, 94.9] and specificities 84.4%[CI95%79.7, 88.4]; 99.2%[CI95%98.2, 99.7]; 99.6%[CI95%98.8, 99.9], respectively. CONCLUSION: sAHI is comparable to manual scoring of AHI from in-laboratory PSG studies and effective to rule-in and rule-out obstructive sleep apnea (OSA) in all disease categories, providing safe and convenient approach for diagnosis and management of OSA in children. The data is recorded with a single-sensor, making the method suitable for multi-night testing in the child's home at considerably lower cost. This technology provides a simple tool to adhere to guidelines for diagnosis and management of OSA in children. CLINICAL TRIAL REGISTRATION NAME AND NUMBER: Childhood Adenotonsillectomy Study for Children with OSA (CHAT) https://clinicaltrials.gov/ct2/show/NCT00560859.

The effect of acute exposure to morphine on breathing variability and cardiopulmonary coupling in men with obstructive sleep apnea: A randomized controlled trial.

Opioid-related deaths from respiratory depression are increasing but there is only limited information on the effect of morphine on breathing during sleep. This study aimed to detect and quantify opioid-induced cardiorespiratory pattern changes during sleep in obstructive sleep apnea (OSA) patients using novel automated methods and correlate these with conventional polysomnography (PSG) measures. Under a randomized double-blind placebo-controlled crossover design, 60 male OSA patients attended two one-night visits to the sleep laboratory, at least a week apart. Either a 40-mg controlled-release oral morphine dose or placebo was administered. Breathing during sleep was measured by standard in-laboratory PSG. We analysed the inter-breath interval (IBI) from the PSG flow channel to quantify breathing irregularity. Cardiopulmonary coupling (CPC) was analysed using the PSG electrocardiogram (ECG) channel. Following the consumption of morphine, the 60 OSA patients had fewer breaths (p = .0006), a longer inter-breath interval (p < .0001) and more irregular breathing with increased IBI coefficient of variation (CV) (p = .0015) compared to the placebo night. A higher CPC sleep quality index was found with morphine use. The change of key IBI and CPC parameters was significantly correlated with the change of key PSG sleep-disordered breathing parameters. In conclusion, 40 mg controlled-release morphine resulted in a longer breathing cycle and increased breathing irregularity but generally more stable sleep in OSA patients. The significant links between the IBI and CPC techniques and a range of PSG sleep-disordered breathing parameters may suggest a practical value as surrogate overnight cardiorespiratory measurements, because both respiratory flow and ECG can be detected by small portable devices.

Objective sleep quality and metabolic risk in healthy weight children results from the randomized Childhood Adenotonsillectomy Trial (CHAT).

BACKGROUND: We hypothesized that cardiopulmonary coupling (CPC) sleep quality reflects cardiovascular and cardiometabolic health, in healthy weight children. METHODS: Retrospective signal analysis of existing ECG data utilizing CPC, FDA cleared, software as medical device (SaMD). ECG signals were extracted from baseline polysomnography studies in the prospective Childhood Adenotonsillectomy Trial database, multicenter, single-blind, randomized controlled trial of 5.0-9.9-year-old children identified with obstructive sleep apnea syndrome without severe hypoxemia. Healthy weight was defined as age- and gender-specific BMI in the 5th-85th percentile range and overweight above the 85th percentile. The cohort was stratified based on CPC sleep quality Index (SQI) defined as high sleep quality (SQI ≥ 80) or low sleep quality (SQI < 60). Cardiovascular, cardiometabolic, quality of life, and cognition were compared between the sleep quality groups. RESULTS: Healthy weight children with low sleep quality had more fragmented sleep with significantly higher arousal index (10.0 ± 4.3 vs.7.2 ± 3.1; p = 0.00) and eLFCBB (12.4 ± 4.9 vs. 0.9 ± 1.0; p < 0.001) CPC indicator of sleep fragmentation, higher average heart rate during sleep (84.5 ± 10.6 vs. 79.4 ± 7.1; p = 0.03) and worse insulin/glucose ratio (1.7 ± 1.6 vs. 1.1 ± 1.1; p = 0.03) and fasting insulin levels (7.9 ± 7.2 vs.5.3 ± 5.5; p = 0.05) when compared to healthy weight children with high sleep quality. SQI significantly correlates with average heart rate during sleep, insulin and triglyceride levels; for a unit increase in SQI, there is 0.154 unit decrease in average heart rate during sleep, 0.109 unit in insulin levels and 0.332 unit in triglyceride levels, respectively. CONCLUSION: CPC sleep quality offers insights into pediatric sleep and how it affects cardiovascular and cardiometabolic health. ECG is simple signal to collect, which makes this method practical for testing sleep, over multiple nights, and on multiple occasions providing information on sleep dynamics not possible before. TRIAL REGISTRATION: NCT00560859.

Insomnia symptoms in primary care: A prospective study focusing on prevalence of undiagnosed co-morbid sleep disordered breathing.

OBJECTIVE: To determine prevalence of comorbid undiagnosed sleep disordered breathing (SDB) in chronic insomnia patients, using two complementary methods, one standard and one novel. METHODS: Using prospective design, adult patients diagnosed with chronic insomnia, treated with prescription pharmacological agents for >3 months without prior objective sleep evaluation or diagnosis of SDB were invited to participate. All patients recorded their sleep for two consecutive nights using level 3 home-sleep-apnea-test (HSAT) device to derive Respiratory Event Index (REI) for OSA diagnosis. The electrocardiogram-signal (ECG) recorded by the same device was analyzed using FDA cleared medical software, Cardiopulmonary Coupling (CPC) to quantify sleep time and identify sleep-quality and pathology. RESULTS: Of 110 chronic insomnia patients who volunteered between May 2017 and June 2018, 88% were women. Prevalence of moderate-severe SDB (REI > 15) was 25% based on REI-scoring. Surrogate markers of moderate-severe SDB detected by the novel method identified prevalence of 33%, with negative predictive value 96%, reclassifying 10 individuals that HSAT diagnosed with mild SDB with more advanced disease state. Agreement between the methods is 88%. CONCLUSION: High prevalence and overlap in symptoms between insomnia and SDB warrants objective testing when evaluating sleep complaints before therapy is initiated. Diagnostic caution is even more importantly warranted for female patients presenting insomnia sleep complaints, as SDB may not be initially considered as a biological symptom driver. CPC-analysis can complement standard HSAT or serve as a standalone option to evaluate sleep complaints in individuals presenting insomnia symptoms before therapy is initiated. CLINICAL TRIAL REGISTRY NAME AND NUMBER: Pilot study: Co-occurrence of Insomnia and Sleep Disordered Breathing (SDB) symptoms: Prospective study focusing on chronic insomnia patients treated with pharmacological agents. Approved by the Bioethics Committee on March 7th, 2017. VSNb: 17- 047- S1/ ST - GRA - 17029 - PDX - SH http://vsn.is/is/content/17-047.

Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index).

INTRODUCTION: Adequate sleep is fundamental to wellness and recovery from illnesses and lack thereof is associated with disease onset and progression resulting in adverse health outcomes. Measuring sleep quality and sleep apnea (SA) at the point of care utilizing data that is already collected is feasible and cost effective, using validated methods to unlock sleep information embedded in the data. The objective of this study is to determine the utility of automated analysis of a stored, robust signal widely collected in hospital and outpatient settings, a single lead electrocardiogram (ECG), using clinically validated algorithms, cardiopulmonary coupling (CPC), to objectively and accurately identify SA. METHODS: Retrospective analysis of de-identified PSG data with expert level scoring of Apnea Hypopnea Index (AHI) dividing the cohort into severe OSA (AHI > 30), moderate (AHI 15-30), mild (AHI 5-15), and no disease (AHI < 5) was compared with automated CPC analysis of a single lead ECG collected during sleep for each subject. Statistical analysis was used to compare the two methods. RESULTS: Sixty-eight ECG recordings were analyzed. CPC identified patients with moderate to severe SA with sensitivity of 100%, specificity of 81%, and agreement of 93%, LR+ (positive likelihood ratio) 5.20, LR- (negative likelihood ratio) 0.00 and kappa 0.85 compared with manual scoring of AHI. CONCLUSION: The automated CPC analysis of stored single lead ECG data often collected during sleep in the clinical setting can accurately identify sleep apnea, providing medically actionable information that can aid clinical decisions.

Ambulatory screening tool for sleep apnea: analyzing a single-lead electrocardiogram signal (ECG).

STUDY OBJECTIVE: The goal was to determine the utility and accuracy of automated analysis of single-lead electrocardiogram (ECG) data using two algorithms, cardiopulmonary coupling (CPC), and cyclic variation of heart rate (CVHR) to identify sleep apnea (SA). METHODS: The CPC-CVHR algorithms were applied to identify SA by analyzing ECG from diagnostic polysomnography (PSG) from 47 subjects. The studies were rescored according to updated AASM scoring rules, both manually by a certified technologist and using an FDA-approved automated scoring software, Somnolyzer (Philips Inc., Monroeville, PA). The CPC+CVHR output of Sleep Quality Index (SQI), Sleep Apnea Indicator (SAI), elevated low frequency coupling broadband (eLFCBB) and elevated low frequency coupling narrow-band (eLFCNB) were compared to the manual and automated scoring of apnea hypopnea index (AHI). RESULTS: A high degree of agreement was noted between the CPC-CVHR against both the manually rescored AHI and the computerized scored AHI to identify patients with moderate and severe sleep apnea (AHI > 15). The combined CPC+CVHR algorithms, when compared to the manually scored PSG output presents sensitivity 89%, specificity 79%, agreement 85%, PPV (positive predictive value) 0.86 and NPV (negative predictive value) 0.83, and substantial Kappa 0.70. Comparing the output of the automated scoring software to the manual scoring demonstrated sensitivity 93%, specificity 79%, agreement 87%, PPV 0.87, NPV 0.88, and substantial Kappa 0.74. CONCLUSION: The CPC+CVHR technology performed as accurately as the automated scoring software to identify patients with moderate to severe SA, demonstrating a clinically powerful tool that can be implemented in various clinical settings to identify patients at risk for SA. TRIAL REGISTRATION: NCT01234077.