The sleep/wake state scoring from mandible movement signal

Mandibular movement monitor provides faster, yet accurate diagnosis for obstructive sleep apnoea: A randomised controlled study

Many patients with obstructive sleep apnoea (OSA) remain undiagnosed and thus untreated, and in part this relates to delay in diagnosis. Novel diagnostic strategies may improve access to diagnosis. In a multicentre, randomised study, we evaluated time to treatment decision in patients referred for suspected OSA, comparing a mandibular movement (MM) monitor to respiratory polygraphy, the most commonly used OSA detection method in the UK. Adults with high pre-test probability OSA were recruited from both northern Scotland and London. 40 participants (70 % male, mean±SD age 46.8 ± 12.9 years, BMI 36.9 ± 7.5 kg/m2, ESS 14.9 ± 4.1) wore a MM monitor and respiratory polygraphy simultaneously overnight and were randomised (1:1) to receive their treatment decision based on results from either device. Compared to respiratory polygraphy, MM monitor reduced time to treatment decision by 6 days (median(IQR): 13.5 (7.0-21.5) vs. 19.5 (13.7-35.5) days, P = 0.017) and saved an estimated 29 min of staff time per patient.

Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography

Background

The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG.

Methods

40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour).

Results

31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI −23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI < 5: MM-ORDI mean overestimation + 5.58 (95% CI + 2.03 to + 7.46) events/hour; PSG-ORDI > 5–15: MM-ORDI overestimation + 3.70 (95% CI −0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (n = 9 with PSG-ORDI 15–30 events/h and n = 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation −8.70 (95% CI −28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively.

Conclusion

The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients’ own home.

Clinical Trial Registration

https://clinicaltrials.gov, identifier NCT04262557

Craniofacial morphology/phenotypes influence on mandibular range of movement in the design of a mandibular advancement device

Background

The mandibular opening path movements have different directions according to the craniofacial morphology of the patient but always downward and backward, therefore increasing the collapse of the upper airway. The aim of this work is to determine if there is a relationship between the craniofacial morphology and the mandibular movement to help understand the impact on the mandibular position.

Methods

52 students with full permanent dentition aged 19 to 23 years (mean 21.3 SD 1.7; 29 females and 23 males), participated in the study. Each subject had a lateral cephalometric radiograph taken. The opening angle was determined for two levels of vertical openings at 5 and 10 mm.

Results

The opening angle showed a greater variability between subjects ranging from 63.15 to 77.08 for 5 mm angle and from for 61.65 to 75.72 for the 10 mm angle. Differences of facial phenotypes was evident when comparing the individual dissoccluding angle of the low angle horizontal pattern and high angle vertical pattern.

Conclusions

The opening angle is related to craniofacial morphology with higher vertical anterior and shorter anteroposterior faces having a more horizontal path of mandibular movement than shorter vertical anterior and longer anteroposterior subjects who have a more vertical path.

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

IMPORTANCE

Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches.

OBJECTIVE

To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis.

DESIGN, SETTING, AND PARTICIPANTS

Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018.

MAIN OUTCOMES AND MEASURES

Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h.

RESULTS

Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, -1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, -0.69; 95% CI, -3.77 to 2.38 events/h). An Sr-RDI underestimation of -11.74 (95% CI, -20.83 to -2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively.

CONCLUSIONS AND RELEVANCE

Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.

The prevalence of sleep-disordered breathing in Northwest Russia: The ARKHsleep study

Sleep-disordered breathing (SDB) is a chronic condition characterized by repeated breathing pauses during sleep. The reported prevalence of SDB in the general population has increased over time. Furthermore, in the literature, a distinction is made between SDB, obstructive sleep apnea (OSA), and "OSA syndrome" (OSAS). Patients with SDB are at increased risk of comorbid cardiovascular diseases (CVDs). The aim of the ARKHsleep study was to assess the prevalence of SDB in general and of OSA and OSAS in particular. A total of 1050 participants aged 30-70 years, who were randomly selected from a population register, were evaluated for the probability of SDB using the Epworth Sleepiness Scale score and body mass index. Sleep was recorded for one night via home sleep apnea testing (Somnolter®). Medical conditions were determined from medical records. Additional data included background characteristics, anthropometric variables, blood pressure, and scores from four questionnaires. The survey sample consisted of 41.2% males and had a mean age of 53.1 ± 11.3 years. The prevalence of mild-to-severe, moderate-to-severe, and severe SDB was 48.9% [45.8-51.9], 18.1% [15.9-20.6], and 4.5% [3.2-5.8], respectively. Individuals reporting snoring or breathing pauses had a higher severity of SDB than individuals free of symptoms. The ARKHsleep study revealed a high burden of both SDB and CVD; however, more large-scale cohort studies and intervention studies are needed to better understand whether the early recognition and treatment of mild SDB with or without symptoms will improve cardiovascular prognosis and/or quality of life.

A systematic review of the accuracy of sleep wearable devices for estimating sleep onset

The accurate estimation of sleep onset is required for many purposes, including the administration of a behavioural treatment for insomnia called Intensive Sleep Retraining, facilitating power naps, and conducting objective daytime sleepiness tests. Specialised equipment and trained individuals are presently required to administer these applications in the laboratory: a costly and impractical procedure which limits their utility in practice. A wearable device could be used to administer these applications outside the laboratory, increasing accessibility. This systematic review aimed to identify practical wearable devices that accurately estimate sleep onset. The search strategy identified seventy-one articles which compared estimations of sleep onset latency from wearable devices against polysomnography (PSG). Actigraphy devices produced average estimations of sleep onset latency that were often not significantly different from PSG, but there was large inter-individual variability depending on participant characteristics. As expected, electroencephalography-based devices produced more accurate and less variable estimates. Devices that measured behavioural aspects of sleep onset consistently overestimated PSG-determined sleep onset latency, but to a comparatively low degree. This sleep measurement method could be deployed in a simple wearable device to accurately estimate sleep onset and administer Intensive Sleep Retraining, power naps, and objective daytime sleepiness tests outside the laboratory setting.

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device

STUDY OBJECTIVES

To assess the sleep detection and staging validity of a non-contact, commercially available bedside bio-motion sensing device (S+, ResMed) and evaluate the impact of algorithm updates.

METHODS

Polysomnography data from 27 healthy adult participants was compared epoch-by-epoch to synchronized data that were recorded and staged by actigraphy and S+. An update to the S+ algorithm (common in the rapidly evolving commercial sleep tracker industry) permitted comparison of the original (S+V1) and updated (S+V2) versions.

RESULTS

Sleep detection accuracy by S+V1 (93.3%), S+V2 (93.8%), and actigraphy (96.0%) was high; wake detection accuracy by each (69.6%, 73.1%, and 47.9%, respectively) was low. Higher overall S+ specificity, compared to actigraphy, was driven by higher accuracy in detecting wake before sleep onset (WBSO), which differed between S+V2 (90.4%) and actigraphy (46.5%). Stage detection accuracy by the S+ did not exceed 67.6% (for stage N2 sleep, by S+V2) for any stage. Performance is compared to previously established variance in polysomnography scored by humans: a performance standard which commercial devices should ideally strive to reach.

CONCLUSIONS

Similar limitations in detecting wake after sleep onset (WASO) were found for the S+ as have been previously reported for actigraphy and other commercial sleep tracking devices. S+ WBSO detection was higher than actigraphy, and S+V2 algorithm further improved WASO accuracy. Researchers and clinicians should remain aware of the potential for algorithm updates to impact validity.

COMMENTARY

A commentary on this article appears in this issue on page 935.

Validation of midsagittal jaw movements to measure sleep in healthy adults by comparison with actigraphy and polysomnography

OBJECTIVE

In a device based on midsagittal jaw movements analysis, we assessed a sleep-wake automatic detector as an objective method to measure sleep in healthy adults by comparison with wrist actigraphy against polysomnography (PSG).

METHODS

Simultaneous and synchronized in-lab PSG, wrist actigraphy and jaw movements were carried out in 38 healthy participants. Epoch by epoch analysis was realized to assess the ability to sleep-wake distinction. Sleep parameters as measured by the three devices were compared. This included three regularly reported parameters: total sleep time, sleep onset latency, and wake after sleep onset. Also, two supplementary parameters, wake during sleep period and latency time, were added to measure quiet wakefulness state.

RESULTS

The jaw movements showed sensitivity level equal to actigraphy 96% and higher specificity level (64% and 48% respectively). The level of agreement between the two devices was high (87%). The analysis of their disagreement by discrepant resolution analysis used PSG as resolver revealed that jaw movements was right (58.9%) more often than actigraphy (41%). In sleep parameters comparison, the coefficient correlation of jaw movements was higher than actigraphy in all parameters. Moreover, its ability to distinct sleep-wake state allowed for a more effective estimation of the parameters that measured the quiet wakefulness state.

CONCLUSIONS

Midsagittal jaw movements analysis is a reliable method to measure sleep. In healthy adults, this device proved to be superior to actigraphy in terms of estimation of all sleep parameters and distinction of sleep-wake status.

Detection of sleep apnea by case-finding and home monitoring with Somnolter®: a pilot study

BACKGROUND

Obstructive sleep apnea and hypopnea syndrome (OSAHS) is a disorder that causes clinical symptoms (e.g. snoring, excessive daytime sleepiness and impaired concentration) that may increase the risk of traffic accidents, cardiovascular disease, type 2 diabetes and reduce the quality of life. A recently developed device (Somnolter®) detects apneas and hypopneas in a home setting, allowing to detect OSAHS in a more comfortable environment compared to the gold standard polysomnography. The aim of our study was to investigate whether the Somnolter® is useful in family practice to identify patients with OSAHS.

METHODS

Questionnaires were offered to patients in five general practitioner (GP) practices.Based on the questionnaire and body mass index, patients with an increased risk of OSAHS were contacted to collaborate in the study. In this convenience sample, 18 patients were successfully tested with the Somnolter® measuring SaO2, mandibular movements, body position, heart rate, nasal air flow and thoracic and abdominal breathing movements. The Somnolter® automatically analyses the data and different parameters to detect OSAHS. Afterwards, the data were manually revised by the researchers.

RESULTS

Out of 365 subjects, 31 met the inclusion criteria and 18 were successfully tested at home. Sixteen out of 18 patients had an Apnea Hypopnea Index (AHI) ≥ 5, ten of them had mild OSAHS, 3 were categorized as moderate OSAHS and finally 3 matched the criteria of severe OSAHS.

CONCLUSION

The proposed case-finding strategy still needs optimization, but is considered helpful in selecting patients at high risk of OSAHS. OSAHS was detected in 14 out of 18 patients tested with the Somnolter®. In the future the Somnolter® might be a feasible alternative to diagnose OSAHS.

Mandible behaviour interpretation during wakefulness, sleep and sleep-disordered breathing

The mandible movement (MM) signal provides information on mandible activity. It can be read visually to assess sleep-wake state and respiratory events. This study aimed to assess (1) the training of independent scorers to recognize the signal specificities; (2) intrascorer reproducibility and (3) interscorer variability. MM was collected in the mid-sagittal plane of the face of 40 patients. The typical MM was extracted and classified into seven distinct pattern classes: active wakefulness (AW), quiet wakefulness or quiet sleep (QW/S), sleep snoring (SS), sleep obstructive events (OAH), sleep mixed apnea (MA), respiratory related arousal (RERA) and sleep central events (CAH). Four scorers were trained; their diagnostic capacities were assessed on two reading sessions. The intra- and interscorer agreements were assessed using Cohen's κ. Intrascorer reproducibility for the two sessions ranged from 0.68 [95% confidence interval (CI): 0.59-0.77] to 0.88 (95% CI: 0.82-0.94), while the between-scorer agreement amounted to 0.68 (95% CI: 0.65-0.71) and 0.74 (95% CI: 0.72-0.77), respectively. The overall accuracy of the scorers was 75.2% (range: 72.4-80.7%). CAH MMs were the most difficult to discern (overall accuracy 65.6%). For the two sessions, the recognition rate of abnormal respiratory events (OAH, CAH, MA and RERA) was excellent: the interscorer mean agreement was 90.7% (Cohen's κ: 0.83; 95% CI: 0.79-0.88). The discrimination of OAH, CAH, MA characteristics was good, with an interscorer agreement of 80.8% (Cohen's κ: 0.65; 95% CI: 0.62-0.68). Visual analysis of isolated MMs can successfully diagnose sleep-wake state, normal and abnormal respiration and recognize the presence of respiratory effort.

Assessment of a wireless headband for automatic sleep scoring

PURPOSE

Classically, professional assessment of sleep is done in the sleep laboratory using whole-night polysomnography (PSG). However, given a misbalance between accredited sleep laboratories and the large amount of patients suffering from sleep disorders, only few receive appropriate diagnostic assessment. Recently, some low-cost home sleep scoring systems have been proposed, yet such systems are rarely tested scientifically. The aim of the present study was to evaluate the staging accuracy of the home sleep scoring system Zeo (Newton, MA, USA).

METHODS

A final sample of 21 nights from ten subjects (aged 23-45) was digitally recorded with PSG as well as with the Zeo system. We compared scorings of Zeo (on an epoch-be-epoch basis) with the Somnolyzer 24 × 7 (an automatic staging algorithm), expert scorers as well as the freeware SleepExplorer.

RESULTS

It was revealed that Zeo shows moderate overall agreement as compared to our study standard Somnolyzer 24 × 7 (κ = 0.56). The most obvious performance difference between Zeo and both other scoring approaches was stage wake (sleep onset latency + wake after sleep onset). While Zeo detected only 40.8 % of the study standard wake epochs, 70.1 % were detected by the expert scorers and 83.4 % by the SleepExplorer, respectively.

CONCLUSIONS

Data suggest that the Zeo system produces acceptable sleep scoring for stage REM, light and deep sleep, with a specific weakness in correctly detecting waking periods.

Added value of a mandible movement automated analysis in the screening of obstructive sleep apnea

In-laboratory polysomnography is the 'gold standard' for diagnosing obstructive sleep apnea syndrome, but is time consuming and costly, with long waiting lists in many sleep laboratories. Therefore, the search for alternative methods to detect respiratory events is growing. In this prospective study, we compared attended polysomnography with two other methods, with or without mandible movement automated analysis provided by a distance-meter and added to airflow and oxygen saturation analysis for the detection of respiratory events. The mandible movement automated analysis allows for the detection of salient mandible movement, which is a surrogate for arousal. All parameters were recorded simultaneously in 570 consecutive patients (M/F: 381/189; age: 50±14 years; body mass index: 29±7 kg m(-2) ) visiting a sleep laboratory. The most frequent main diagnoses were: obstructive sleep apnea (344; 60%); insomnia/anxiety/depression (75; 13%); and upper airway resistance syndrome (25; 4%). The correlation between polysomnography and the method with mandible movement automated analysis was excellent (r: 0.95; P<0.001). Accuracy characteristics of the methods showed a statistical improvement in sensitivity and negative predictive value with the addition of mandible movement automated analysis. This was true for different diagnostic thresholds of obstructive sleep severity, with an excellent efficiency for moderate to severe index (apnea-hypopnea index ≥15h(-1) ). A Bland & Altman plot corroborated the analysis. The addition of mandible movement automated analysis significantly improves the respiratory index calculation accuracy compared with an airflow and oxygen saturation analysis. This is an attractive method for the screening of obstructive sleep apnea syndrome, increasing the ability to detect hypopnea thanks to the salient mandible movement as a marker of arousals.

Sleep/wake estimation using only anterior tibialis electromyography data

BACKGROUND

In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data.

METHODS

Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted.

RESULTS

Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients.

CONCLUSIONS

The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.