Using entropy of snoring, respiratory effort and electrocardiography signals during sleep for OSA detection and severity classification

Study objectives Obstructive sleep apnea (OSA) is a very prevalent disease and its diagnosis is based on polysomnography (PSG). We investigated whether snoring-sound-, very low frequency electrocardiogram (ECG-VLF)- and thoraco-abdominal effort- PSG signal entropy values could be used as surrogate markers for detection of OSA and OSA severity classification. Methods The raw data of the snoring-, ECG- and abdominal and thoracic excursion signal recordings of two consecutive full-night PSGs of 86 consecutive patients (22 female, 53.74 ± 12.4 years) were analyzed retrospectively. Four epochs (30 s each, manually scored according to the American Academy of Sleep Medicine standard) of each sleep stage (N1, N2, N3, REM, awake) were used as the ground truth. Sampling entropy (SampEn) of all the above signals was calculated and group comparisons between the OSA severity groups were performed. In total, (86x4x5 = )1720 epochs/group/night were included in the training set as an input for a support vector machine (SVM) algorithm to classify the OSA severity classes. Analyses were performed for first- and second-night PSG recordings separately. Results Twenty-seven patients had mild (RDI = ≥ 5/h but <15/h), 21 patients moderate (RDI ≥15/h but <30/h) and 23 patients severe OSA (RDI ≥30/h). Fifteen patients had an RDI <5/h and were therefore considered non-OSA. Using SE on the above three PSG signal data and using a SVM pipeline, it was possible to distinguish between the four OSA severity classes. The best metric was snoring signal-SE. The area-under-the-curve (AUC) calculations showed reproducible significant results for both nights of PSG. The second night data were even more significant, with non-OSA (R) vs. light OSA (L) 0.61, R vs. moderate (M) 0.68, R vs. heavy OSA (H) 0.84, L vs. M 0.63, M vs. H 0.65 and L vs. H 0.82. The results were not confounded by age or gender. Conclusions SampEn of either snoring-, very low ECG-frequencies- or thoraco-abdominal effort signals alone may be used as a surrogate marker to diagnose OSA and even predict OSA severity. More specifically, in this exploratory study snoring signal SampEn showed the greatest predictive accuracy for OSA among the three signals. Second night data showed even more accurate results for all three parameters than first-night recordings. Therefore, technologies using only parts of the PSG signal, e.g. sound-recording devices, may be used for OSA screening and OSA severity group classification

A novel quantitative arousal-associated EEG-metric to predict severity of respiratory distress in obstructive sleep apnea patients

Respiratory arousals (RA) on polysomnography (PSG) are an important predictor of obstructive sleep apnea (OSA) disease severity. Additionally, recent reports suggest that more global indices of desaturation such as the hypoxic burden, namely the area under the curve (AUC) of the oxygen saturation (SaO2) PSG trace may better depict the desaturation burden in OSA. Here we investigated possible associations between a new metric, namely the AUC of the respiratory arousal electroencephalographic (EEG) recording, and already established parameters as the apnea/hypopnea index (AHI), arousal index and hypoxic burden in patients with OSA. In this data-driven study, polysomnographic data from 102 patients with OSAS were assessed (32 female; 70 male; mean value of age: 52 years; mean value of Body-Mass-Index-BMI: 31 kg/m2). The marked arousals from the pooled EEG signal (C3 and C4) were smoothed and the AUC was estimated. We used a support vector regressor (SVR) analysis to predict AHI, arousal index and hypoxic burden as captured by the PSG. The SVR with the arousal-AUC metric could quite reliably predict the AHI with a high correlation coefficient (0,58 in the training set, 0,65 in the testing set and 0,64 overall), as well as the hypoxic burden (0,62 in the training set, 0,58 in the testing set and 0,59 overall) and the arousal index (0,58 in the training set, 0,67 in the testing set and 0,66 overall). This novel arousal-AUC metric may predict AHI, hypoxic burden and arousal index with a quite high correlation coefficient and therefore could be used as an additional quantitative surrogate marker in the description of obstructive sleep apnea disease severity

Endoscopic dacryocystorhinostomy for acquired nasolacrimal duct obstruction: long-term results in 91 procedures

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Distinct EEG‐EMG‐coherence patterns associated with sleep‐disordered breathing severity grade [Abstract]

Objectives/Instruction: We investigated whether using EEG‐EMG‐coherence (EEC) as a feature fed to a support vector machine (SVM) algorithm may allow staging of disease severity among sleep‐disordered‐breathing (SDB) patients. Methods: EEG‐EMG‐coherence data resulted by applying a multitaper processing for estimating the power spectrums separately and calculating the coherence on raw C3‐/C4‐EEG‐ and EMG‐ chin data of polysomnographic (PSG) recordings of 102 SDB patients (33 female; age: 53, ± 12,4 yrs) acquired on the second of two consecutive PSG nights in each patient. Four epochs (30 s each, classified manually by AASM 2012‐ criteria) of each sleep stage (N1, N2, N3, REM) were marked (in total 1632 epochs/night) and were included in the analysis. After multitaper processing, EEC values were fed to a SVM algorithm to classify SDB disease severity based on respiratory disturbance index (RDI). Twenty patients had a mild (RDI ≥ 10/h and < 15/h), 30 patients had a moderate (RDI ≥ 15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥ 30/h). Twenty five patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator characteristic (ROC) curve. Results: EEG‐EMG coherence values could distinguish between SDB‐patients without OSA and OSA patients of the above three severity groups using an SVM algorithm. Using PSG data of the second night, in mild OSA the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions: Grading disease severity in SDB patients can be performed using PSG‐based multitaper‐processed EEC values processedwith a SVM algorithm. Disclosure: Nothing to disclose

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Clinical characteristics and positive airway pressure adherence among elderly European sleep apnoea patients from the ESADA cohort

Background The prevalence of obstructive sleep apnoea (OSA) is growing as the population is ageing. However, data on the clinical characteristics of elderly patients with OSA and their adherence to positive airway pressure (PAP) treatment are scarce.Methods Data from 23 418 30-79-year-old OSA patients prospectively collected into the ESADA database during 2007-2019 were analysed. Information on PAP use (h.day(-1)) in association with a first follow-up visit was available for 6547 patients. The data was analysed according to 10-year age groups.Results The oldest age group was less obese, less sleepy and had a lower apnoea-hypopnoea index (AHI) compared with middle-aged patients. The insomnia phenotype of OSA was more prevalent in the oldest age group than in the middle-aged group (36%, 95% CI 34-38 versus 26%, 95% CI 24-27, p&lt;0.001). The 70-79-year-old group adhered to PAP therapy equally well as the younger age groups with a mean PAP use of 5.59 h.day(-1) (95% CI 5.44-5.75). PAP adherence did not differ between clinical phenotypes based on subjective daytime sleepiness and sleep complaints suggestive of insomnia in the oldest age group. A higher score on the Clinical Global Impression Severity (CGI-S) scale predicted poorer PAP adherence.Conclusion The elderly patient group was less obese, less sleepy, had more insomnia symptoms and less severe OSA, but were rated to be more ill compared with the middle-aged patients. Elderly patients with OSA adhered to PAP therapy equally well as middle-aged patients. Low global functioning (measured by CGI-S) in the elderly patient predicted poorer PAP adherence

The role of structured reporting and structured operation planning in functional endoscopic sinus surgery

Computed tomography (CT) scans represent the gold standard in the planning of functional endoscopic sinus surgeries (FESS). Yet, radiologists and otolaryngologists have different perspectives on these scans. In general, residents often struggle with aspects involved in both reporting and operation planning. The aim of this study was to compare the completeness of structured reports (SR) of preoperative CT images and structured operation planning (SOP) to conventional reports (CR) and conventional operation planning (COP) to potentially improve future treatment decisions on an individual level. In total, 30 preoperative CT scans obtained for surgical planning of patients scheduled for FESS were evaluated using SR and CR by radiology residents. Subsequently, otolaryngology residents performed a COP using free texts and a SOP using a specific template. All radiology reports and operation plannings were evaluated by two experienced FESS surgeons regarding their completeness for surgical planning. User satisfaction of otolaryngology residents was assessed by using visual analogue scales. Overall radiology report completeness was significantly higher using SRs regarding surgically important structures compared to CRs (84.4 vs. 22.0%, p<0.001). SOPs produced significantly higher completeness ratings (97% vs. 39.4%, p<0.001) regarding pathologies and anatomical variances. Moreover, time efficiency was not significantly impaired by implementation of SR (148 s vs. 160 s, p = 0.61) and user satisfaction was significantly higher for SOP (VAS 8.1 vs. 4.1, p<0.001). Implementation of SR and SOP results in a significantly increased completeness of radiology reports and operation planning for FESS. Consequently, the combination of both facilitates surgical planning and may decrease potential risks during FESS

EEG-EMG-Kohärenz bei Rhonchopathie-Patienten unter Verwendung eines Support Vector Machine-Algorithmus [Abstract]

Einleitung: Untersucht wurde, ob die EEG-EMG-Kohärenz die Differenzierung zwischen Rhonchopathie-Patienten ohne obstruktive Schlafapnoe (OSA) und Patienten mit OSA eines gering-, mäßig- oder schwergradigen Ausmaßes erlaubt. Methoden: Polysomnographische Aufzeichnungen von 102 Rhonchopathie-Patienten (33 weiblich Alter: 53,74 ± 12,4 Jahre) wurden mit der Multitaper-Kohärenz-Methodik (MTM) analysiert. Die Aufnahmen umfassten u.a. die C3- und C4-EEG-Kanäle und einen Kinn-EMG-Kanal. Vier Epochen (30 Sekunden, manuell nach AASM 2007-Kriterien klassifiziert) jedes Schlafstadiums wurden markiert (insgesamt 1632 Epochen), die in die Klassifikation-Analysen aufgenommen wurden. Die erhobenen Datensätze wurden als Input für den support vector machine (SVM) – Algorithmus eingegeben, um die 4 verschiedenen OSA-Schweregrade zu klassifizieren. Zwanzig Patienten hatten an einer milden (RDI ≥10/h und < 15/h), 30 Patienten an einer mäßigen (RDI ≥15/h und < 30/h) und 27 Patienten an einer schweren OSA (RDI ≥30/h) gelitten. 25 Patienten hatten ein RDI < 10/h. Der AUC (area under the curve)-Wert wurde bei jeder ROC (receiver operator curve)-Kurve errechnet. Ergebnisse: Mithilfe der EEG-EMG-Kohärenz konnte unter Verwendung eines SVM-Algorithmus zwischen den Rhonchopathie-Patienten ohne OSA und den OSA-Patienten der jeweiligen 3 Schweregrad-Gruppen unterschieden werden. Bei milder OSA lag der AUC-Wert bei 0.616 (p = 0.024), bei mäßiger OSA lag der AUC-Wert bei 0.659 (p = 0.003) und bei schwerer OSA lag der AUC-Wert bei 0.823 (p < 0.001). Schlussfolgerung: Rhonchopathie-Patienten mit OSA lassen sich von Rhonchopathie-Patienten ohne OSA allein durch die EEG-EMG-Kohärenz der Polysomnografie mithilfe der Multitaper-Kohärenz -Methodik (MTM) unter Verwendung eines SVM-Algorithmus unterscheiden