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[Analysis of Snoring in Patients with Obstructive Sleep Apnea (OSA) by Polysomnography and LEOSound]

INTRODUCTION

Snoring was monitored in patients with obstructive sleep apnea (OSA) using the LEOSound-Monitor and simultaneously polysomnographic (PSG) recording. In obstructive apneas snoring is normally apparent after apnea termination and the beginning of ventilation. We wanted to know how often obstructive apneas are terminated by ventilation in combination with snoring.

METHODS AND INTENTION

In 40 patients with OSA (AHI > 15/h) simultaneous polysomnographic recordings were performed amongst long-term respiratory sound monitoring using the LEOSound monitor. Patients' average age was 57±11 years. Average weight was 100±19 kg by a mean body mass index (BMI) of 33±7 kg/m². 12 out of 40 recordings had to be rejected for further analysis because of artifacts. Snoring recorded by polysomnography was compared with snoring monitored by LEOSound.

RESULTS

3778 obstructive apnea episodes were monitored. LEOSound identified snoring in 1921 (51,0%), polysomnography in 2229 (58,8%) obstructive apneas. Only in one patient there was a higher difference in snoring episodes between PSG and LEOSound.

DISCUSSION

In nearly 60% of obstructive apnea events we found snoring during apnea-terminating hyperpnoea. LEOSound is a good diagnostic tool to monitor snoring. It is necessary to clarify why only 60% of all obstructive events/hyperpnoea develop snoring. From a pathophysiological point of view opening of collapsed upper airway should lead in a very high percentage to turbulences in airstream and committed snoring.

Application of substitution box of present cipher for automated detection of snoring sounds

BACKGROUND AND PURPOSE

Snoring is one of the sleep disorders, and snoring sounds have been used to diagnose many sleep-related diseases. However, the snoring sound classification is done manually which is time-consuming and prone to human errors. An automated snoring sound classification model is proposed to overcome these problems.

MATERIAL AND METHOD

This work proposes an automated snoring sound classification method using three new methods. These methods are maximum absolute pooling (MAP), the nonlinear present pattern, and two-layered neighborhood component analysis, and iterative neighborhood component analysis (NCAINCA) selector. Using these methods, a new snoring sound classification (SSC) model is presented. The MAP decomposition model is applied to snoring sounds to extract both low and high-level features. The presented model aims to attain high performance for SSC problem. The developed present pattern (Present-Pat) uses substitution box (SBox) and statistical feature generator. By deploying these feature generators, both textural and statistical features are generated. NCAINCA chooses the most informative/valuable features, and these selected features are fed to k-nearest neighbor (kNN) classifier with leave-one-out cross-validation (LOOCV). The Present-Pat based SSC system is developed using Munich-Passau Snore Sound Corpus (MPSSC) dataset comprising of four categories.

RESULTS

Our model reached an accuracy and unweighted average recall (UAR) of 97.10 % and 97.60 %, respectively, using LOOCV. Moreover, a nocturnal sound dataset is used to show the universal success of the presented model. Our model attained an accuracy of 98.14 % using the used nocturnal sound dataset.

CONCLUSIONS

Our developed classification model is ready to be tested with more data and can be used by sleep specialists to diagnose the sleep disorders based on snoring sounds.

[Analysis of Snoring in Patients with Obstructive Sleep Apnea (OSA) by Polysomnography and LEOSound]

INTRODUCTION

 Snoring was monitored in patients with obstructive sleep apnea (OSA) using the LEOSound-Monitor and simultaneously polysomnographic (PSG) recording. In obstructive apneas snoring is normally apparent after apnea termination and the beginning of ventilation. We wanted to know how often obstructive apneas are terminated by ventilation in combination with snoring.

METHODS AND INTENTION

 In 40 patients with OSA (AHI > 15/h) simultaneous polysomnographic recordings were performed amongst long-term respiratory sound monitoring using the LEOSound monitor. Patients' average age was 57 ± 11 years. Average weight was 100 ± 19 kg by a mean body  mass  index (BMI) of 33 ± 7 kg/m². 12 out of 40 recordings had to be rejected for further analysis because of artifacts. Snoring recorded by polysomnography was compared with snoring monitored by LEOSound.

RESULTS

 3778 obstructive apnea episodes were monitored. LEOSound identified snoring in 1921 (51,0 %), polysomnography in 2229 (58,8 %) obstructive apneas. Only in one patient there was a higher difference in snoring episodes between PSG and LEOSound.

DISCUSSION

 In nearly 60 % of obstructive apnea events we found snoring during apnea-terminating hyperpnoea. LEOSound is a good diagnostic tool to monitor snoring. It is necessary to clarify why only 60 % of all obstructive events/hyperpnoea develop snoring. From a pathophysiological point of view opening of collapsed upper airway should lead in a very high percentage to turbulences in airstream and committed snoring.

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

OBJECTIVES

To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratory sounds recorded during polysomnography during all sleep stages between sleep onset and offset.

METHODS

Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audio recordings were performed with an air-conduction microphone during polysomnography. Analyses included all sleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmented into 5-s windows and sound features were extracted. Prediction models were established and validated with 10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for three different threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, including accuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under the curve (AUC) of the receiver operating characteristic were computed.

RESULTS

A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2 , and 23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughout sleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Prediction performances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%, 81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were 89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30.

CONCLUSION

This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificity of >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithms based on respiratory sounds may have a high value for prescreening OSA with mobile devices.

Snore sound recognition: On wavelets and classifiers from deep nets to kernels

In this paper, we present a comprehensive comparison of wavelet features for the classification of snore sounds. Wavelet features have proven to be efficient in our previous work; however, the benefits of wavelet transform energy (WTE) and wavelet packet transform energy (WPTE) features were not clearly established. In this study, we firstly present our updated snore sounds database, expanded from 24 patients (collected by one medical centre) to 40 patients (collected by three medical centres). We then study the effects of varying frame sizes and overlaps for extraction of the wavelet low-level descriptors, the effect of which have yet to be fully established. We also compare the performance of the WTE and WPTE features when fed into multiple classifiers, namely, Support Vector Machines (SVM), K-Nearest Neighbours, Linear Discriminant Analysis, Random Forests, Extreme Learning Machines, Kernel Extreme Learning Machines, Multilayer Perceptron, and Deep Neural Networks. Key results presented indicate that, when fed into a SVM, WTE outperforms WPTE (one-tailed z-test, p<;0.002). Further, WPTE can achieve a significant improvement when trained by a k-nearest neighbours classifier (one-tailed z-test, p <; 0.001).