Detection of airway obstructions and sleep apnea by analyzing the phase relation of respiration movement signals

Varady, P.; Bongár, Szabolcs; Benyó, Zoltán

doi:10.1109/tim.2003.809095

Cited by 37 publications

(24 citation statements)

References 5 publications

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“…Its output can directly serve as the input for sleep stage classifiers [47,21], or apnea detectors [41,3]. As such it extends the benefits of such contactless monitoring to couples and individuals sharing the same bed.…”

Section: Chapter 7 Conclusionmentioning

confidence: 99%

Extracting Multi-Person Respiration from Entangled RF Signals

Yue

Wang

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

148

104

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Recent advances in wireless systems have demonstrated the possibility of tracking a person's respiration using the RF signals that bounce off her body. The resulting breathing signal can be used to infer the person's sleep quality and stages; it also allows for monitoring sleep apnea and other sleep disordered breathing; all without any body contact. Unfortunately however past work fails when people are close to each other, e.g., a couple sharing the same bed. In this case, the breathing signals of nearby individuals interfere with each other and super-impose in the received signal.This thesis presents DeepSleep, the first RF-based respiration monitoring system that can recover the breathing signals of multiple individuals even when they are separated by zero distance. To design DeepSleep, we model interference due to multiple reflected RF signals and demonstrate that the original breathing can be recovered via independent component analysis. We design a full system that eliminates interference and recovers the original breathing signals. We empirically evaluate DeepSleep using 21 nights of sleep and over 150 hours of data from 13 couples who share the bed. Our results show that DeepSleep is very accurate. Specifically, the differences between the breathing signals it recovers and the ground truth are on par with the difference between the same breathing signal measured at the person's chest and belly. Then, I would like to thank my collaborator Hao He. Hao and I have an enjoyable collaboration during this study. We have discussed every corner of our system and spent countless hours together improving its performance.

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Section: Chapter 7 Conclusionmentioning

confidence: 99%

Extracting Multi-Person Respiration from Entangled RF Signals

Yue

Wang

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

148

104

View full text Add to dashboard Cite

show abstract

“…PSG consists of several multiple vital signals; body oxygen saturation (SaO 2 ), electrooculogram (EOG), electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), respiration signals (nasal airflow, thoracic abdominal movements), body position. According to the accepted clinical criteria, sleep apnea can primarily be detected by the analysis of PSG [13]. Manually scoring PSG is a tiresome and timeconsuming work for the sleep expert.…”

Section: Introductionmentioning

confidence: 99%

Sleep apnea classification based on respiration signals by using ensemble methods

Avci

Akbaş

2015

BME

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Abstract. In this study, an efficient and robust method classifying the minute based occurrence of sleep apnea is aimed. Three respiration signals obtained from abdominal, chest and nasal way extracted from polysomnography recordings. Wavelet transform based on feature extraction methods are applied on the 1 minute length respiration signals. Dimension reduction process is facilitated by using principal component analysis. The features obtained from 8 recordings are used for the classification sleep apnea by using three ensemble classifiers. According to the results, the classification accuracies have been obtained between 92.07-98.43%, 92.75-98.68% and 92.42-98.61% by using three different ensemble classifier based on abdominal, chest and nasal based analysis, respectively for AdaBoost, Random Forest and Random Subspace. However the best result is obtained analyzing nasal based respiratory signal by using Random Forest method. In this case accuracy is 98.68%.

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“…Várady et al . showed that phase difference between the thoracic and abdominal respiratory signals has 80–90% accuracy in classifying selected 1-min segments from OSA and control subjects [6]. Cross correlation between the two respiratory effort signals showed 66% accuracy compared with respiratory distress index for the whole night of sleep [7].…”

Section: Introductionmentioning

confidence: 99%

Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier

Al-Angari

Sahakian

2012

IEEE Trans. Inform. Technol. Biomed.

112

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Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.

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Detection of airway obstructions and sleep apnea by analyzing the phase relation of respiration movement signals

Cited by 37 publications

References 5 publications

Extracting Multi-Person Respiration from Entangled RF Signals

Extracting Multi-Person Respiration from Entangled RF Signals

Sleep apnea classification based on respiration signals by using ensemble methods

Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier

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