SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

Piriyajitakonkij, Maytus; Warin, Patchanon; Lakhan, Payongkit; Leelaarporn, Pitshaporn; Pianpanit, Theerasarn; Kumchaiseemak, Nakorn; Suwajanakorn, Supasorn; Niparnan, Nattee; Mukhopadhyay, Subhas Chandra; Wilaiprasitporn, Theerawit

doi:10.1109/jbhi.2020.3025900

Cited by 61 publications

(28 citation statements)

References 48 publications

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“…Moreover, the occurrence of the AH events is greatly influenced by the sleep posture as well as the sleep stage [41]; however, an accelerometer was not considered in this study. Nevertheless, recently, studies have been conducted to classify the sleep stages and sleep posture based on IR-UWB radar signals [42], [43], [44]. Combining these approaches with our method will allow us to classify the AH events and calculate the AHI accurately from a single IR-UWB radar.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…However, this warrants further investigations. Recently, radar technology has been shown to accurately classify sleep in healthy adults [16] and to possibly detect body position during sleep (supine, prone, side) [22,23]. If radar technology also could reliably classify sleep in persons with sleep apnea, the proposed solution could calculate AHI, possibly detect RERAs, and investigate sleep disruption.…”

Section: Discussionmentioning

confidence: 99%

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

Toften¹,

Kjellstadli²,

Tyvold³

et al. 2021

Journal of Sensors

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The gold standard for assessing sleep apnea, polysomnography, is resource intensive and inconvenient. Thus, several simpler alternatives have been proposed. However, validations of these alternatives have focused primarily on estimating the apnea-hypopnea index (apnea events per hour of sleep), which means information, clearly important from a physiological point of view such as apnea type, apnea duration, and temporal distribution of events, is lost. The purpose of the present study was to investigate if this information could also be provided with the combination of radar technology and pulse oximetry by classifying sleep apnea events on a second-by-second basis. Fourteen patients referred to home sleep apnea testing by their medical doctor were enrolled in the study (6 controls and 8 patients with sleep apnea; 4 mild, 2 moderate, and 2 severe) and monitored by Somnofy (radar-based sleep monitor) in parallel with respiratory polygraphy. A neural network was trained on data from Somnofy and pulse oximetry against the polygraphy scorings using leave-one-subject-out cross-validation. Cohen’s kappa for second-by-second classifications of no event/event was 0.81, or almost perfect agreement. For classifying no event/hypopnea/apnea and no event/hypopnea/obstructive apnea/central apnea/mixed apnea, Cohen’s kappa was 0.43 (moderate agreement) and 0.36 (fair agreement), respectively. The Bland-Altman 95% limits of agreement for the respiratory event index (apnea events per hour of recording) were -8.25 and 7.47, and all participants were correctly classified in terms of sleep apnea severity. Furthermore, the results showed that the combination of radar and pulse oximetry could be more accurate than the two technologies separately. Overall, the results indicate that radar technology and pulse oximetry could reliably provide information on a second-by-second basis for no event/event which could be valuable for management of sleep apnea. To be clinically useful, a larger study is necessary to validate the algorithm on a general population.

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“…EQ-Radio [101] converts FMCW-based breathing rate measurement into human emotions using machine learning algorithms. SleepPoseNet [102] utilizes Ultra-Wideband (UBW) radar for Sleep Postural Transition (SPT) recognition which can be used to detect sleep disorders. Ultrasoundbased breathing rate monitoring study based on the human thorax movement has shown capability to monitor abnormal breathing activities [43].…”

Section: Elderly and Patient Carementioning

confidence: 99%

Contactless Human Activity Analysis: An Overview of Different Modalities

Abir

Faisal

Shahid

et al. 2021

Contactless Human Activity Analysis

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Human Activity Analysis (HAA) is a prominent research field in this modern era which has enlightened us with the opportunities of monitoring regular activities or the surrounding environment as per our desire. In recent times, Contactless Human Activity Analysis (CHAA) has added a new dimension in this domain as these systems perform without any wearable device or any kind of physical contact with the user. We have analyzed different modalities of CHAA and arranged them into three major categories: RF-based, sound-based, and vision-based modalities. In this chapter, we have presented state-of-the-art modalities, frequently faced challenges with some probable solutions, and currently used applications of CHAA with future directions.

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SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

Cited by 61 publications

References 48 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

A Pilot Study of Detecting Individual Sleep Apnea Events Using Noncontact Radar Technology, Pulse Oximetry, and Machine Learning

Contactless Human Activity Analysis: An Overview of Different Modalities

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