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

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 diagnostic 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 apneahypopnea index (AHI), the arousal index (ArI), the average of the mandible lowering during sleep (aLOW), and the average 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 recordings each. Significant difference (p < 0.001) was found in the OSA group compared with the CPAP group when considering 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.Peer reviewe

SCREENING OF SLEEP APNEAS AND HYPOPNEAS THROUGH THE AUTOMATIC ANALYSIS OF MIDSAGITTAL JAW MOTION

This paper proposes a novel method for the scoring of sleep apneas and hypopneas (SAHs) based on the recording and the analysis of the midsagittal jaw movements. Continuous wavelet transform was used to delineate events which were likely to contain the SAHs, while hidden markov models (HMMs) classified events. Considering 28 recordings from which awakenings were discarded, the method detected SAHs with a sensitivity and a specificity of 82.2% and 78.3% respectively. Obstructive, central and mixed respiratory events were distinguished fairly accurately. The jaw motion is hence a reliable marker of respiratory efforts and may suffice by itself to score SAHs

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

Diagnosis of obstructive sleep apnoea and hypopnoea syndrome based on automatic analysis of mandibular movements

peer reviewedOur study showed the potential of a surrogate of the oesophageal pressure, the jaw motion, and a dedicated automatic analysis to diagnose the obstructive sleep apnoea and hypopnoea syndrome (OSAHS). Twentyfour patients were recorded in hospital settings and the jaw motion of fifteen of them was also recorded at home with an ambulatory device. The apnoea and hypopnoea index (AHI) of the gold standard, AHI_PSG, and the two AHI from the automatic analysis of the jaw motion, AHI_H (hospital) and AHI_A (ambulatory), were compared : AHI_PSG Vs AHI_H showed good correlation (r = 0.97) and under-estimation (slope p = 0.76). AHI_A Vs AHI_H revealed good reliability with r = 0.98 and slope p = 0.99. Finally, AHI_PSG Vs AHI_A showed very good accuracy of the diagnosis with sensitivity of 90% and specificity of 100%

Automatic Scoring of sleep apnea and hypopnea by analysis of mandibular movements

peer reviewedIn this paper, we propose a method for sleep apnea and hypopnea detection by analyzing the mandibular movements recorded during sleep. This signal, highly correlated with the esophageal pressure during apnea or hypopnea, is a good marker of respiratory efforts. The respiratory efforts and their background activity are used to detect salient mandibular movements related to sleep apnea and hypopnea, to delineate and to recognize episodes of sleep apnea and hypopnea. The method presented may be extended to others breathing disorders such as Respiratory Efforts Related Arousals (RERA)

Diagnostique du syndrome d’apnées et hypopnées obstructives du sommeil par l’analyse des mouvements de la mandibule

Les mouvements de la mandibule donnent une image des efforts respiratoires, particulièrement en période d’apnées et d’hypopnées du sommeil. Ils sont donc une alternative à la pression oesophagienne. Cette étude a mis en évidence le potentiel d’une analyse automatique des mouvements de la mâchoire pour le diagnostique du syndrome d’apnées et hypopnées obstructives du sommeil (SAHOS) par la mesure de l’indice d’apnées et d’hypopnées (IAH)

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±14years; body mass index: 29±7kgm -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&lt;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 &amp; 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. © 2012 European Sleep Research Society

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