Frédéric Senny

Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas

Given the importance of the detection and classification of sleep apneas and hypopneas (SAHs) in the diagnosis and the characterization of the SAH syndrome, there is a need for a reliable noninvasive technique measuring respiratory effort. This paper proposes a new method for the scoring of SAHs based on the recording of the midsagittal jaw motion (MJM, mouth opening) and on a dedicated automatic analysis of this signal. Continuous wavelet transform is used to quantize respiratory effort from the jaw motion, to detect salient mandibular movements related to SAHs and to delineate events which are likely to contain the respiratory events. The classification of the delimited events is performed using multilayer perceptrons which were trained and tested on sleep data from 34 recordings. Compared with SAHs scored manually by an expert, the sensitivity and specificity of the detection were 86.1% and 87.4%, respectively. Moreover, the overall classification agreement in the recognition of obstructive, central, and mixed respiratory events between the manual and automatic scorings was 73.1%. The MJM signal is hence a reliable marker of respiratory effort and allows an accurate detection and classification of SAHs.

Assessment of tumor motion reproducibility with audio-visual coaching through successive 4D CT sessions

This study aimed to compare combined audio‐visual coaching with audio coaching alone and assess their respective impact on the reproducibility of external breathing motion and, one step further, on the internal lung tumor motion itself, through successive sessions. Thirteen patients with NSCLC were enrolled in this study. The tumor motion was assessed by three to four successive 4D CT sessions, while the breathing signal was measured from magnetic sensors positioned on the epigastric region. For all sessions, the breathing was regularized with either audio coaching alone (AC, n=5) or combined with a real‐time visual feedback (A/VC, n=8) when tolerated by the patients. Peak‐to‐peak amplitude, period and signal shape of both breathing and tumor motions were first measured. Then, the correlation between the respiratory signal and internal tumor motion over time was evaluated, as well as the residual tumor motion for a gated strategy. Although breathing and tumor motions were comparable between AC and AV/C groups, A/VC approach achieved better reproducibility through sessions than AC alone (mean tumor motion of 7.2 mm±1 vs. 8.6 mm±1.8 mm, and mean breathing motion of 14.9 mm±1.2 mm vs. 13.3 mm±3.7 mm, respectively). High internal/external correlation reproducibility was achieved in the superior‐inferior tumor motion direction for all patients. For the anterior‐posterior tumor motion direction, better correlation reproducibility has been observed when visual feedback has been used. For a displacement‐based gating approach, A/VC might also be recommended, since it led to smaller residual tumor motion within clinically relevant duty cycles. This study suggests that combining real‐time visual feedback with audio coaching might improve the reproducibility of key characteristics of the breathing pattern, and might thus be considered in the implementation of lung tumor radiotherapy. PACS number: 87

Midsagittal Jaw Movements as a Sleep/Wake Marker

The seriousness of the obstructive sleep apnea/hypopnea syndrome is measured by the apnea-hypopnea index (AHI), the number of sleep apneas and hypopneas over the total sleep time (TST). Cardiorespiratory signals are used to detect respiratory events while the TST is usually assessed by the analysis of electroencephalogram traces in polysomnography (PSG) or wrist actigraphy trace in portable monitoring. This paper presents a sleep/wake automatic detector that relies on a wavelet-based complexity measure of the midsagittal jaw movement signal and multilayer perceptrons. In all, 63 recordings were used to train and test the method, while 38 recordings constituted an independent evaluation set for which the sensitivity, the specificity, and the global agreement of sleep recognition, respectively, reached 85.1%, 76.4%, and 82.9%, compared with the PSG data. The AHI computed automatically and only from the jaw movement analysis was significantly improved (p < 0.0001 ) when considering this sleep/wake detector. Moreover, a sensitivity of 88.6% and a specificity of 83.6% were found for the diagnosis of the sleep apnea syndrome according to a threshold of 15. Thus, the jaw movement signal is reasonably accurate in separating sleep from wake, and, in addition to its ability to score respiratory events, is a valuable signal for portable monitoring.

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 ≥15 h−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.

Mandible Behavior in Obstructive Sleep Apnea Patients Under CPAP Treatment

Aim: To investigate whether obstructive sleep apnea (OSA) patients present different behaviors of mandible movements before and under CPAP therapy. Materials and Methodology: In this retrospective study, patients were selected according to inclusion criteria: both the di- agnostic polysomnography recording showing an OSA with an apnea-hypopnea index (AHI) greater than 25 (n/h) and the related CPAP therapy control recordings were available, presence of mandible movement and mask pressure signals in the recordings, and tolerance to the applied positive pressure. Statistical analysis on four parameters, namely the apnea- hypopnea index (AHI), the arousal index (ArI), the average of the mandible lowering during sleep (aLOW), and the aver- age amplitude of the oscillations of the mandible movement signal (aAMPL), was performed on two sets of recordings: OSA and CPAP therapy. Results: Thirty-four patients satisfied the inclusion criteria, thus both OSA and CPAP groups included thirty-four record- ings each. Significant difference (p < 0.001) was found in the OSA group compared with the CPAP group when consider- ing either the four parameters or only the two ones related to mandible movements. Conclusions: When an efficient CPAP pressure is applied, the mouth is less open and presents fewer broad sharp closure movements, and oscillating mandible movements are absent or very small.

Mandibular Movements Identify Respiratory Efforts Due to Obstructive Sleep Apnoea in a Pre-school Child

Adenotonsillar hypertrophy is a major cause of obstructive sleep apnea (OSAS) in childhood. Increased respiratory effort associated with OSAS is accompanied by an increase in pulse transit time (PTT) but also mandibular movements (MMs) amplify with increased upper airway resistance. We compared dynamic changes in PTT and MMs using a magnetic distance sensor during polysomnography (PSG) in a pre-school child with severe OSAS before and after adenotonsillectomy. The results show that repetitive respiratory effort to overcome upper airway obstruction can be identified in children using MMs.

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 Cohens 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% (Cohens 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% (Cohens 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.

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.