Sleep Apnea Severity Estimation From Tracheal Movements Using a Deep Learning Model

Hafezi, Maziar; Montazeri, N.; Saha, Shumit; Zhu, Kevin; Gavrilovic, Bojan; Yadollahi, Azadeh; Taati, Babak

doi:10.1109/access.2020.2969227

Cited by 41 publications

(22 citation statements)

References 43 publications

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“…Thus, LSTM layers can encode relevant information of class-specific characteristics across time [26]. Owing to these characteristics, the models combined with CNN and LSTM, have been successfully applied in detecting SAHS using biosignal sequences, in recent studies [27], [28], [29]. On this premise, we take the temporal characteristics of radar signals into consideration and propose a hybrid model architecture that combines CNNs and LSTM network.…”

Section: Introductionmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

et al. 2022

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Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen's kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson's correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any handengineered feature regardless of the user's location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.INDEX TERMS convolutional neural network, impulse-radio ultra-wideband, long short-term memory network, non-contact monitoring, sleep apnea and hypopnea syndrome

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Section: Introductionmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

et al. 2022

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“…In addition to tracheal sounds, respiratory‐related vibrations of soft tissues in the pharynx above the trachea can be recorded by an accelerometer. In this paper, we called the recorded movements as tracheal movements (Bates et al., 2010; Hafezi et al., 2019, 2020; Hung et al., 2008). During inspiration, caudal forces actuated by the diaphragm and expansion in the chest stiffen the tracheal structure, resulting in respiratory‐related changes in tracheal movements (Rowley et al., 1996; Van de Graaff, 1991).…”

Section: Introductionmentioning

confidence: 99%

Relative tidal volume and respiratory airflow estimation using tracheal sound and movement during sleep

Ghahjaverestan

Kabir

Saha

et al. 2021

Journal of Sleep Research

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Summary Airflow is the reference signal to assess sleep respiratory disorders, such as sleep apnea. Previous studies estimated airflow using tracheal sounds in short segments with specific airflow rates, while requiring calibration or a few breaths for tuning the relationship between sound energy and airflow. Airflow−sound relationship can change by posture, sleep stage and airflow rate or tidal volume. We investigated the possibility of estimating surrogates of tidal volume without calibration in the adult sleep apnea population using tracheal sounds and movements. Two surrogates of tidal volume: thoracoabdominal range of sum movement and airflow level were estimated. Linear regression was used to estimate thoracoabdominal range of sum movement from sound energy and the range of movements. The sound energy lower envelope was found to correlate with airflow level. The agreement between reference and estimated signals was assessed by repeated‐measure correlation analysis. The estimated tidal volumes were used to estimate the airflow signal. Sixty‐one participants (30 females, age: 51 ± 16 years, body mass index: 29.5 ± 6.4 kg m−2, and apnoea−hypopnea index: 20.2 ± 21.2) were included. Reference and estimated thoracoabdominal range of sum movement of whole night data were significantly correlated with the reference signal extracted from polysomnography (r = 0.5 ± 0.06). Similarly, significant correlations (r = 0.3 ± 0.05) were found for airflow level. Significant differences in estimated surrogates of tidal volume were found between normal breathing and apnea/hypopnea. Surrogate of airflow can be extracted from tracheal sounds and movements, which can be used for assessing the severity of sleep apnea and even phenotyping sleep apnea patients based on the estimated airflow shape.

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“…Hafezi et al [ 59 ] estimate sleep apnea severity from tracheal movements via an accelerometer attached to the participant’s suprasternal notch. 7 morphological features were extracted from tracheal movements, on which a deep learning classifier using a combination of CNN and LSTM, was applied.…”

Section: Other Solutionsmentioning

confidence: 99%

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Ramachandran

Karuppiah

2021

Healthcare

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Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

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Sleep Apnea Severity Estimation From Tracheal Movements Using a Deep Learning Model

Cited by 41 publications

References 43 publications

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

Relative tidal volume and respiratory airflow estimation using tracheal sound and movement during sleep

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

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