Technique


 

A Combination Index of Low Frequency Cardio-Pulmonary-Coupling and Oxygen Desaturation has a Strong

Authors:
Haitham S Al Ashry, Robert J Thomas, Hugi Hilmisson

Reference:
Sleep, Volume 42, Issue Supplement_1, April 2019, Page A188,

Objectives:
Deriving an AHI from limited measurements enables simple sleep apnea diagnosis. Cardiopulmonary coupling (CPC) is an ECG-based analysis incorporating cyclic variation in heart rate (CVHR) typical of apnea, and ECG-derived respiration, to generate sleep apnea metrics. Elevated Low Frequency Coupling (e-LFC) is a CPC pattern that has been associated with fragmented sleep. Narrow spectral band e-LFC (e-LFC-NB) associates with sustained central apnea, classic periodic breathing, and high loop gain obstructive sleep apnea OSA). Broad spectral band e-LFC (e-LFC-BB) associates with OSA. We combined e-LFC with oxygen desaturations to generate a SleepImage-AHI (SAHI) index and examined its correlation with the conventional 3%/arousal AHI using polysomnograms in 38 subjects. CPC analysis was done on the ECG from the PSG, generating a narrow-band index (NBI) and broad-band index (BBI). The PSG pulse oximeter trace was processed to calculate the Oxygen Desaturation Index (ODI) defined as events lasting 10 seconds or longer of 3% or more within each CPC LFC periods. The SAHI-Adult = (NBI + ODI), and the SAHI-Pediatric = (BBI + NBI + ODI), per hour of sleep. Pearson coefficient was calculated to estimate correlation between SAHI and 3% AHI, and Bland-Altman (BA) for agreement between the two methods.

Conclusions:
Combining CPC and ODI yields a metric comparable to AHI3%/arousal on PSGs

Practical Significance:
There were 20 adults age 42-90 (median: 69.5, SD 14.5). There were 18 children age 5-10 years. Females were 52.6%. Pearson coefficient was 0.7649. One adult PSG had short cycle non-hypoxic periodic breathing events without desaturations which were not scored leading to AHI of 13.1 vs. SAHI of 76.2. Removal of this outlier study resulted in a correlation coefficient 0.92. The BA analysis mean difference = -2.28 (CI -6.25 to 1.70) for the whole set and -0.637 (CI -2.87 to 1.59) with the outlier remove, 0.599 (CI -1.23 to 2.43) for the pediatric, -4.87 (CI -12.38 to 2.64) for adults with, and -1.81 (CI -5.94 to 2.32) with the outlier removed.

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A new approach to sleep study: does heart tell us a lot?

Authors:
Y.Ma, S.Sun

Reference:
Sleep Medicine Volume 14, Supplement 1, December 2013, Pages e188-e189

Objectives:
It has been proven that ECG-derived respiration signal is highly correlated with the actual respiration waveforms. Cardiopulmonary coupling (CPC) analysis is derived from an estimation of the coupling between the autonomic and respiratory drives, using heart rate and respiratory modulation of QRS amplitude, respectively. This dual information can be extracted from a single channel of ECG, and is highly correlated with the actual respiration waveforms. High frequency coupling (HFC) is the marker of stable sleep, and low-frequency coupling (LFC) is the marker of unstable sleep. Fragmented sleep is characterized by coupled low-frequency behaviors across numerous sleep based physiological stream. There have been an increasing number of papers evaluating CPC or using CPC as a clinical measurement.

Conclusions:
Using data derived from ECG can be used as clinical screen or post-treatment follow-up. This review confirmed the association between sleep physiology and sleep spectrums analyzed by cardiopulmonary coupling. As sleep problems are of growing concern, easier access of overnight ECG data can be used broadly when sleep monitor is necessary. With the techniques of cardiopulmonary analysis, a portable monitor for sleep can be effective by collecting enough data for sleep analysis, meanwhile be more convenient and cost-effective. Furthermore, adding actigraphy and/or oximetry will be recommended for clinical applications.

Practical Significance:
Materials and methods
The literature search was performed via the internet using the PubMed, the Cochrane database and Sleep abstract supplements. The search included papers only in English, published up to June 2013. The key words which were searched in the titles and abstracts were the terms “cardiopulmonary”, “coupling”, “CPC”, “ECG-derived” “Electrocardiogram-based spectrogram” in combination with the name of all types of sleep disorders (e.g. “insomnia”).

Results
46 relevant English articles were found, 29 (63%) was cardiopulmonary coupling. 9 (19.6%) articles explain mechanism, 30 (65.2%) articles are studies on SDB, and 7 (15.2%) articles relate to other sleep disorders or comorbidities. The methods of CPC analysis as a measurement of sleep evaluation has been compared with conventional PSG or PTT. While most of the articles are about sleep apnea, sleep quality, detecting central and obstructive, and evaluating effects of PAP therapies, studies have covered sleep quality study in sleep apnea, insomnia, hypertension, chronic heart failure, diabetics, fibromyalgia, as well as healthy subjects.

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An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea.

Authors:
Harrington J, Schramm PJ, Davies CR, Lee-Chiong TL Jr.

Reference:
Sleep Breath. 2013 Sep;17(3):1071-8.

Objectives:
The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

Conclusions:
Tests differentiated no and moderate to severe OSA groups by REM %, HFC, VLFC, and LFC/HFC ratio variables. The successful CPAP therapy group had more HFC, less LFC, and e-LFCBB compared to the unsuccessful CPAP therapy group. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

Practical Significance:
The results support the use of the SleepImage system to investigate and objectively measure sleep quality in patients complaining of a sleep disorder.

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An Electrocardiogram-based Technique to Assess Cardiopulmonary Coupling During Sleep

Authors:
Thomas RJ, Mietus JE, Peng CK, Goldberger AL

Reference:
SLEEP 2005;28:1151-1161

Objectives:
Evaluate a new automated measure of CPC during sleep using a single-lead electrocardiographic (ECG) signal

Conclusions:
A sleep spectrogram derived from information in a single lead electrocardiogram can be used to dynamically track cardiopulmonary interactions. The 2 distinct (bimodal) regimes demonstrate a closer relationship with visual cyclic alternating pattern (CAP) and non-cyclic alternating pattern states than with standard sleep stages. This technique may provide a complementary approach to the conventional characterization of graded non-rapid eye movement (NREM) sleep stages

Practical Significance:
This seminal work establishes the link between High Frequency CPC with good sleep quality, and Low Frequency CPC with poor quality sleep

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Applications of evolving technologies in sleep medicine

Authors:
Verbraecken J

Reference:
Breathe, December 2013, Volume 9, No 6

Objectives:
Nocturnal polysomnography (PSG) is the most important laboratory technique in the management of sleep–wake disturbances and is considered the “gold standard” [1]. New sensor technologies are entering the field, and rapid development in telecommunications and mobile technology has accelerated the introduction of telemedicine as a viable and reliable option [2]. The present broad review is an amalgam of the current knowledge with proposed new sensors and remote control. The reader should note that not all of the techniques discussed here have strong clinical validation, and this should be considered when purchasing equipment.

Conclusions:
Traditional sleep monitoring methods use a variety of leads and probes on the patient’s face and body to gather data. Additional information can be achieved from these signals by advanced processing based on complex algorithms. Moreover, a number of signals that are not traditionally used in clinical PSG will become of interest for specific patient categories. We are also faced with the development of innovative noncontact systems based on movement detection using radar and infrared technology. The idea of automatic sleep evaluation and monitoring through signals that are integrated into the environment (a sensorised bed) or through wearable textile technology will change the traditional paradigm of clinical polysomnography. Implementation of wireless applications and remote monitoring will lead to new platforms and evolve towards low-threshold sleep telemedicine. The available evidence base has, however, lagged far behind.

Practical Significance:
Based on the information obtained by electroencephalography, electro-oculography and electromyography (EMG), sleep stages can be defined according to the criteria of Rechtschaffenand Kales [3] and the new American Academy of Sleep Medicine criteria [4]. Ventilation is often measured qualitatively by means of thermistors but is more appropriately measured with nasal pressure cannulae, or by means of a pneumotachograph, connected with a full face mask, or calibrated inductance plethysmography, although calibration of this is difficult [5]. Breathing effort can also be detected by recording movements of the chest and abdomen, surface EMG, snoring, and changes in arterial blood pressure, but most effectively by detection of intrathoracic pressure swings. These swings can be detected by measuring oesophageal pressure. Movements of the chest and abdomen can be recorded with strain gauges (which detect changes in resistance according to length changes), inductance plethysmography or Respitrace (with detection of inductance characteristics of electrical conductors), impedance or even a static charge sensitive bed (which detects respiratory movements). If respiratory effort is detected during an apnoea, this can be explained by occlusion of the upper airways. Oxygen saturation is measured by means of pulse oximetry (SpO2), as well as transcutaneous carbon dioxide tension (PCO2). Sound recording is another method to detect ventilation. Frequency analysis of the sounds (predominantly snoring) can deliver more information on flow limitation. Routinely, body position (position sensor on the chest) is also recorded. The combined application of these measurement techniques allows the assessment of normal and abnormal physiological events in relation to sleep structure [6].

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Cardiopulmonary coupling spectrogram as an ambulatory clinical biomarker of sleep stability and quality in health, sleep apnea and insomnia

Authors:
Robert Joseph Thomas, M.D., M.M.Sc. Christopher Wood, B.S., R.P.S.G.T. Matt Travis Bianchi, M.D., Ph.D.

Reference:
None cited.

Objectives:
Ambulatory tracking of sleep and sleep pathology is rapidly increasing with the introduction of wearable devices. The objective of this study was to evaluate a wearable device which used novel computational analysis of the electrocardiogram (ECG), collected over multiple nights, as a method to track the dynamics of sleep quality in health and disease.Cardiopulmonary coupling spectrogram as an ambulatory clinical biomarker of sleep stability and quality in health, sleep apnea and insomnia

Conclusions:
The M1 and similar wearable devices provide new opportunities to measure sleep in dynamic ways not possible before. These measurements can yield new biological insights, and aid clinical management.

Practical Significance:
Data was collected from 10 healthy, 18 positive pressure treated sleep apnea, and 20 insomnia patients, 128, 65 and 121 nights respectively. In any subject, all nights were consecutive. High frequency coupling, the signal biomarker of stable breathing and stable sleep, showed high ICC’s in healthy subjects and sleep apnea patients (0.83, 0.89) but only 0.66 in insomnia subjects. The only statistically significant difference between weekday vs. weekend in healthy subjects was HFC duration: 242.8 ± 53.8 vs. 275.8 ± 57.1 minutes (89 vs. 39 total nights), F(1,126)=9.86, p: 0.002.

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Differentiating Obstructive from Central & Complex Sleep Apnea Using an Automated Electrocardiogram-based Method

Authors:
Thomas RJ, Mietus JE, Peng CK, Gilmartin G, Daly RW, Goldberger AL, Gottlie DJ.

Reference:
Sleep. 2007 Dec;30(12):1756-69.

Objectives:
Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG) based CPC technique to distinguish obstructive from central or complex sleep apnea

Conclusions:
ECG based spectral analysis allows automated, operator-independent characterization of probable interactions between impaired respiration and upper airway anatomical obstruction. The clinical utility of spectrographic classification, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested

Practical Significance:
Using the Heart Health Study population of 3989 subjects, this study shows that CPC not only differentiated obstructive vs. central vs. complex sleep apnea, but it positively correlated with periodic breathing episodes in PSG and was the strongest predictor of success or failure with PAP titration.

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HHT based cardiopulmonary coupling analysis for sleep apnea detection

Authors:
Dongdong Liu, Xiaochen Yang, Guangfa Wang, Jing Ma, Yanhui Liu, Chung-Kang Peng, Jue Zhang, Jing Fang

Reference:
Sleep Medicine 13(5):503-509, May 2012

Objectives:
To validate the feasibility of the Hilbert–Huang transform (HHT) based cardiopulmonary coupling (CPC) technique in respiratory events detection and estimation of the severity of apnea/hypopnea.

Conclusions:
The HHT-CPC spectrum provides much finer temporal resolution and frequency resolution (8s and 0.001Hz) compared with the original CPC (8.5min and 0.004Hz). The area under the ROC curve of pLFC was 0.79 in distinguishing respiratory events from normal breathing. Significant differences were found in TVDF among groups with different severities of OSAHS (normal, mild, moderate, and severe, p<0.001). TVDF has a strong negative correlation with the apnea/hypopnea index (AHI, correlation coefficient −0.71).

Practical Significance:
The spectrographic markers, pLFC and TVDF can be used to identify respiratory events and represent the disruption extent of sleep architecture in patients with sleep apnea/hypopnea, respectively.

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Mapping Sleep Using Coupled Biological Oscillations

Authors:
Thomas RJ, Mietus JE

Reference:
Conf. Proc IEEE Eng Med Biol. Soc. 2011;2011:1479-82.

Objectives:
To examine the utility of an electrocardiogram-derived sleep spectrogram to provide a different view of sleep

Conclusions:
Non-electroencephalogram (EEG) recordings can provide an alternative approach to viewing sleep quality.

Practical Significance:
Novel insights into physiology and pathology of sleep can be obtained through the coupling of ECG and respiratory signal influences on the ECG R wave.

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Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

Authors:
Penzel T, Kantelhardt JW, Bartsch RP, et al.

Reference:
Frontiers in Physiology. 2016;7:460. doi:10.3389/fphys.2016.00460

Objectives:
The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).

Conclusions:
Analysis of ECG data and heart rate during sleep provides an appreciable diversity of information on the physiology and the pathophysiology of sleep-wake regulation. Assessment of nocturnal ECGs with respect to cyclical fluctuations of heart rate, combined with study of respiration-dependent alterations in ECG morphology (e.g., amplitudes of the R-waves and T-waves), allows reliable recognition of sleep-related breathing disorders. The quality of sleep itself can also be approximately evaluated by analysis of heart-rate variations. Deep sleep and REM sleep, to be sure, demonstrate characteristic properties in heart-rate variability.

Even now, new methods are being applied in practice by presenting sleep findings that already include analysis of sleep and sleep-related breathing disorders with the aid of long-term ECG systems, data from pacemaker ECGs, and information from innovative, reduced-scale recording systems. To arrive at solid diagnostic and therapeutic conclusions from these results, it will be necessary to conduct prospective validation studies and to perform clinical evaluation with parallel out-of-center sleep studies and polysomnography. In addition new algorithms are needed which allow an automated processing of heart rate and HRV which results in a conclusive report, similar to the report created from sleep stage scoring or respiration scoring.

Practical Significance:
An important coupling phenomenon between systems is called phase synchronization. This had been described for the first time in the seventeenth century in conjunction with pendulum clocks (Huygens, 1673; Pikovsky et al., 2001). During cardiorespiratory phase synchronization, heartbeats occur more often during some phases of the respiratory cycle: e.g. At the beginning of the inspiration, at the end of inspiration, and in the middle of expiration (Figure 5; Schäfer et al., 1998, 1999; Toledo et al., 2002; Bartsch et al., 2007, 2012). The occurrence of cardiorespiratory phase synchronization is intermittent and not constant. This means that the phenomenon can be observed during a few percent of observational time only. For reliable tracking of phase synchronization throughout the night, surrogate data techniques are needed to check statistical significance for each detected synchronized episode (see, e.g., Toledo et al., 2002; Bartsch et al., 2007).

Phase synchronization between heartrate and respiration can occur independent of respiratory sinus arrhythmia. This is shown in Figures 4, 5. In addition, both coupling mechanisms are influenced by different physiological parameters. An important example for this influence is respiratory frequency. Whereas, the extent of respiratory sinus arrhythmia is distinctly dependent on respiratory frequency, this is not the case for phase synchronization (Figure 6; Bartsch et al., 2012).

The physical training condition of the persons examined is evidently significant for the extent of cardiorespiratory phase synchronization (Schäfer et al., 1998, 1999). Athletes have demonstrated pronounced synchronization between respiration and heartbeat, which leads to the conclusion that the occurrence of this synchronization represents ergonomically effective regulation. The influence of the extent and effectiveness of this coupling on physical or mental performance have not been determined until now. It is assumed that coupling could represent a good surrogate parameter for recovery periods after physical exercise.

More studies have systematically investigated this phase synchronization during sleep. This had been done for healthy subjects and also for patients with sleep-apnea by multiple groups (Cysarz et al., 2004; Kabir et al., 2010; Bartsch et al., 2012; Müller et al., 2012, 2014; Riedl et al., 2014; Solà-Soler et al., 2015). In healthy persons, it had been proven that the percentage of time spent with strong synchronization depends on sleep stages. Synchronization between respiration and heartbeat is observed for the largest percentage of time during deep sleep. In contrast to this the percentage of time with synchronization is smallest during REM sleep (Bartsch et al., 2012). This sleep-stage dependency is many times greater for phase synchronization than for respiratory sinus arrhythmia, and is likewise much greater than the variations in mean heart rate, HRV, and respiratory rate (Table1). In patients with sleep disordered breathing, such as sleep-apnea, the time spent with synchronization is strongly impaired.

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Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep.

Authors:
Thomas RJ, Mietus JE, Peng CK2, Guo D, Gozal D, Montgomery-Downs H, Gottlieb DJ, Wang CY, Goldberger AL.

Reference:
Sleep Med. 2014 Jan; 15(1):125-31.

Objectives:
To evaluate the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC.

Conclusions:
The overall correlation (r) between delta power and high-frequency coupling (HFC) power was statistically significant.

Practical Significance:
The results support a tight temporal relationship between EEG slow wave power and high frequency cardiopulmonary coupling.

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Role of Objectively Measuring Sleep in Drug Research

Authors:
Magnusdottir S.

Reference:
International Journal of Drug Research and Technology, [S.l.], v. 8, n. 2, may 2018. ISSN 2277-1506

Objectives:
Sleep quality and duration play an important role for overall health and wellbeing, as it is associated with development of various diseases like cardiovascular disease, diabetes and obesity, making it important in research to have objective information of sleep physiology that either may affect or be affected by sleep. In the past, self-reported habitual sleep quality and duration has been the standard practice in research as a screening method. Although asking about sleep quality and duration seems uncomplicated, subjective questionnaires have been found to have a low correlation with objective measures of sleep.
Until recently it has not been easy or accessible to collect objective data on sleep health. With improvements in sensor technology, now it is possible to apply ambulatory methods available to collect medically relevant physiological bio-signals like ECG that can be automatically analyzed to measure sleep duration sleep quality and sleep pathology providing a unique insight into sleep health.

Conclusions:
With advances in sensor technology, ambulatory methods to easily collect bio-signals like ECG are now available providing opportunities and possibilities to collect objective data for multiple nights for assessing sleep duration and sleep quality. Objective sleep data collection may have important implications for both research and clinical understanding of the inter-relationship between untreated sleep disorders and several of the most widely researched chronic conditions like cardiovascular disease, obesity, diabetes and depression.

Practical Significance:
The use of accessible and low-cost ambulatory methods to collect objective and medically relevant physiological signals offers possibilities to collect sleep data over multiple nights to provide comprehensive phenotypic profile on sleep quantity, sleep quality and sleep pathology (Heckman, EJ, et al., 2017 and Thomas, RJ, et al., 2018). Recording sleep for several nights provides information on night-to-night variability within and across individuals, providing a unique insight into research both in health and disease, information that has not been accessible to collect before and at the same time objective information offers both benefits and feedback on effectiveness of the therapy being tested. This ability to measure sleep health objectively, to see its impact on research outcomes should put an end to the era of subjective questionnaires for these purposes.

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Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomn

Authors:
Hilmisson, H., Lange, N. & Duntley, S.P.

Reference:
Sleep Breath (2018)

Objectives:
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.

Conclusions:
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.

Practical Significance:
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.

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.

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Sleep devices: wearables and nearables, informational and interventional, consumer and clinical.

Authors:
Bianchi, M.

Reference:
Metabolism Volume 84, July 2018, Pages 99-108

Objectives:
Wearable technology is not new to sleep medicine, but never before has the situation been so promising to combine hardware and software capabilities to realize the potential for such technology to positively impact sleep health. Wrist-worn actigraphy has been used for decades, mainly in research settings, but also in some clinical circumstances, as an adjunct to diaries for understanding rest-activity patterns over days or weeks [1]. The past decade has seen an acceleration of consumer-facing technology for sleep tracking, initially with actigraphy type devices but additional sensors are now commonplace. The implied benefit of sleep tracking has been one of “knowledge is power”, that is, measuring sleep would lead to insights or patterns that an individual could use to improve sleep. More recent advancements have seen the growth of devices that more actively “intervene”, beyond the indirect approaches allowed by purely tracking approaches. Recent reviews in the area of consumer sleep tracking have highlighted the need for improved validation, including the contrast between strong marketing claims of consumer devices versus the relative paucity of available validation [2], [3], [4]. However, the consumer appetite for these products seems undaunted by validation uncertainties, even as companies and products rise and fall in recent years. Since our 2012 summary of sleep monitoring devices [5], five of the six consumer trackers on the market that we reviewed at that time no longer exist today. Even in the past year, we have seen the demise of popular trackers such as Hello Sense, Jawbone, and Basis (which was acquired by Intel, and subsequently had a safety recall for overheating; Intel has since dissolved its wearable division). By contrast, large companies have moved into this space, with Nokia acquiring Withings, ResMed acquiring Biancamed (and now spun off as SleepScore Labs), Apple acquiring Beddit, and Google's Verily announcing a phenotyping cohort of 10,000 adults that includes home sleep tracking.

Conclusions:
The field of sleep is in many ways ideally positioned to take full advantage of advancements in technology and analytics that is fueling the mobile health movement. Combining hardware and software advances with increasingly available big datasets that contain scored data obtained under gold standard sleep laboratory conditions completes the trifecta of this perfect storm. This review highlights recent developments in consumer and clinical devices for sleep, emphasizing the need for validation at multiple levels, with the ultimate goal of using personalized data and advanced algorithms to provide actionable information that will improve sleep health.

Practical Significance:
Technology advances in the clinical arena also continue, though at a slower pace that is not surprising given the distinctly different landscape of regulated versus consumer markets. Nevertheless, the hardware aspects of sleep tracking share much in common between the healthcare and consumer areas, and the sensor improvements driven by consumer advancements will be relevant for clinical applications as well. Improvements in computational capacity combined with increased availability of training datasets are likely to foster improved algorithm performance that will simultaneously benefit consumer and clinical sleep devices alike.

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Sleep Disorder Screening: Integration of Subjective and Objective Measures

Authors:
Magnusdottir S, Hilmisson H and Sveinsdottir E

Reference:
SM J Sleep Disord. 2017; 3(2): 1014.

Objectives:
Comparing the output of two subjective self evaluation sleep questionnaires commonly used in adult populations at-risk for sleep disorders, focusing on sleepiness and insomnia symptoms, to automated analysis of electrocardiography (ECG) data collected during sleep, to measure sleep quality.

Conclusions:
Our findings support the need to add an objective measure of sleep physiology and pathology to subjective patient self-evaluation of sleep quality when screening for sleep disorders. Concordance of abnormality could reasonably trigger a low-cost approach to confirm or exclude sleep disorders including sleep apnea, and proceed to appropriate therapy. Discordant responses in a high-risk population could benefit from a full polysomnography test to confirm the screening outcome before making a definitive diagnosis.

Practical Significance:
Data collected from 57 obese individuals, when starting a lifestyle program supervised by a primary care physician, was retrospectively analyzed. Of the 57 individuals, 50 recorded two consecutive nights. When compared to the objective CPC-output the questionnaires had low sensitivity, specificity and agreement: (1) ESS; sensitivity 23%, specificity 69% and agreement 51%. (2) BIS; 73%, specificity 43% and agreement 54%. Combining the questionnaires ESS/BIS had sensitivity 73%, specificity 29% and agreement 46%.

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Technical advances in the characterization of the complexity of sleep and sleep disorders.

Authors:
Bianchi MT, Thomas RJ.

Reference:
Prog Neuropsychopharmacol Biol Psychiatry. 2013 Aug 1;45:277-86.

Objectives:
A review of the spectrum of approaches that have been leveraged towards improved understanding of the complexity of sleep.

Conclusions:
The complexity of sleep physiology has inspired alternative metrics that are providing additional insights into the rich dynamics of sleep. Electro-encephalography, magneto-encephalography, and functional magnetic resonance imaging represent advanced imaging modalities for understanding brain dynamics.

Practical Significance:
These methods are complemented by autonomic measurements that provide additional important insights.

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Urgent Need to Improve PAP Management: The Devil Is in Two (Fixable) Details

Authors:
Robert J. Thomas, MD, MMSc, Matt T. Bianchi, MD, PhD

Reference:
J Clin Sleep Med. 2017;13(5):657–664.

Objectives:
Several high-profile, large prospective sleep apnea therapy trials have failed to meet expected outcomes: Apnea Positive Pressure Long-term Efficacy Study (APPLES) (cognition),1 the Treatment of Predominant Central Sleep Apnoea by Adaptive Servo Ventilation in Patients With Heart Failure (SERVE-HF) trial (heart failure),2 the Canadian Positive Airway Pressure Trial (CANPAP), the Sleep Apnea cardioVascular Endpoints (SAVE) study (general cardiovascular),3 and the Heart Biomarker Evaluation in Apnea Treatment (HeartBEAT) (metabolic/hemodynamic).4 Each theoretically had the power to positively influence practice, but instead have cast doubt on the staple of our field: positive airway pressure. Struggling to navigate these findings, experts have invoked explanations ranging from inadequate use, too-short duration of therapy, overwhelming disease pathophysiology, treatment initiated too late in evolution of disease, and unknown pathophysiological constructs. Although these are important questions to advance our field, there are two arguably more fundamental details that must be addressed. First, the efficacy of continuous positive airway pressure (CPAP)—or adaptive ventilation in the case of the SERVE-HF study—remains unquestioned, either via titration data or via machine data download thresholds, despite emerging data suggesting otherwise. Second, off-positive airway pressure (PAP) sleep time is not measured or considered, yet it must be to understand overall PAP effectiveness. We propose that these two aspects must be addressed urgently, before we seek explanations beyond these fundamental aspects of PAP therapy to reconcile negative trial outcomes.

Conclusions:
Objective apnea burden assessment requires use of a device to estimate event recurrence during off-PAP time, though predictions can be made easily under the “worst-case” assumption of resumed baseline severity. Options could include oximetry with analysis that goes beyond just desaturations (eg, arousal analysis form heart rate), the WatchPAT15 (Itamar Medical, Caesarea, Israel), or any respiration monitor that can be used for multiple nights and can be worn concurrently with PAP (standard HSAT devices cannot easily be used in this way). Electrocardiography-based cardiopulmonary coupling can estimate stable breathing during off-therapy and on-therapy periods. It is possible that several other wearable devices can provide warning signs of the quality of sleep when off therapy.

Practical Significance:
We do not measure sleep apnea events during off-PAP sleep, which for some patients can represent well over 50% of their total sleep duration. For over a decade we have known that event recurrence varies from PAP withdrawal studies, and the surgical literature has clearly indicated the need for measuring all of sleep to understand PAP effectiveness given the issues surrounding compliance.

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Wearable Sleep Epidemiology In The Framingham Heart Study

Authors:
EJ Heckman, R Salazar, S Hardy, E Manders, Y Liu, R Au, G O’Connor, R Thomas

Reference:
Sleep, Volume 40, Issue suppl_1, 28 April 2017, Pages A289

Objectives:
Wearable devices for sleep assessments offer a cost-effective and convenient alternative to traditional measures of sleep. Devices are now available to measure oxygenation, respiration electrocardiogram, and electroencephalogram in the home environment. This study assessed standard (oximetry) and novel (cardiopulmonary coupling) measures of sleep state in a well-established epidemiology cohort.

Conclusions:
The results suggest that home/wearable assessment of sleep is 1) feasible, cost-effective, and yields reliable results; 2) inter-individual differences are stable; 3) measures can be readily repeated; 4) in-person visits are not required, markedly simplifying data collection. Both standard and novel measures can be collected.

Practical Significance:
A total of 972 participants agreed to participate. 126 participants were unable or refused to complete the study. 830 and 836 participants obtained at least 4 hours of data with the M1 and oximetry device for at least one night, respectively. 574 participants wore both devices for 2 consecutive nights (803 wore M1, 695 wore Ox for 2 consecutive nights). The mean (SD) were as follows: HFC 43.5%(18.8), LFC 37.28%(17.03), ODI 8.3(8.5), oxygen saturation below 90% 48.1(77.24) minutes, and 52.5% of the sample had narrow band coupling. The ICC for these variables ranged from 74.5%-99.9%, suggesting high night to night data and physiological signal stability. Associations with common medical co-morbidities will be presented.

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