Non-contact diagnostic system for sleep apnea–hypopnea syndrome based on amplitude and phase analysis of thoracic and abdominal Doppler radars

Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

Validity of non-contact methods for diagnosis of Obstructive Sleep Apnea: a systematic review and meta-analysis

STUDY OBJECTIVE

Obstructive Sleep Apnea (OSA) is associated with increased perioperative cardiac, respiratory and neurological complications. Pre-operative OSA risk assessment is currently done through screening questionnaires with high sensitivity but poor specificity. The objective of this study was to evaluate the validity and diagnostic accuracy of portable, non-contact devices in the diagnosis of OSA as compared with polysomnography.

DESIGN

This study is a systematic review of English observational cohort studies with meta-analysis and risk of bias assessment.

SETTING

Pre-operative, including in the hospital and clinic setting.

PATIENTS

Adult patients undergoing sleep apnea assessment using polysomnography and an experimental non-contact tool.

INTERVENTIONS

A novel non-contact device, which does not utilize any monitor that makes direct contact with the patient's body, in conjunction with polysomnography.

MEASUREMENTS

Primary outcomes included pooled sensitivity and specificity of the experimental device in the diagnosis of obstructive sleep apnea, in comparison to gold-standard polysomnography.

RESULTS

Twenty-eight of 4929 screened studies were included in the meta-analysis. A total of 2653 patients were included with the majority being patients referred to a sleep clinic (88.8%). Average age was 49.7(SD±6.1) years, female sex (31%), average body mass index of 29.5(SD±3.2) kg/m², average apnea-hypopnea index (AHI) of 24.7(SD±5.6) events/h, and pooled OSA prevalence of 72%. Non-contact technology used was mainly video, sound, or bio-motion analysis. Pooled sensitivity and specificity of non-contact methods in moderate to severe OSA diagnosis (AHI > 15) was 0.871 (95% CI 0.841,0.896, I² 0%) and 0.8 (95% CI 0.719,0.862), respectively (AUC 0.902). Risk of bias assessment showed an overall low risk of bias across all domains except for applicability concerns (none were conducted in the perioperative setting).

CONCLUSION

Available data indicate contactless methods have high pooled sensitivity and specificity for OSA diagnosis with moderate to high level of evidence. Future research is needed to evaluate these tools in the perioperative setting.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

MCFN: A Multichannel Fusion Network for Sleep Apnea Syndrome Detection

Sleep apnea syndrome (SAS) is the most common sleep disorder which affects human life and health. Many researchers use deep learning methods to automatically learn the features of physiological signals. However, these methods ignore the different effects of multichannel features from various physiological signals. To solve this problem, we propose a multichannel fusion network (MCFN), which learns the multilevel features through a convolution neural network on different respiratory signals and then reconstructs the relationship between feature channels with an attention mechanism. MCFN effectively fuses the multichannel features to improve the SAS detection performance. We conducted experiments on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset, consisting of 2056 subjects. The experiment results show that our proposed network achieves an overall accuracy of 87.3%, which is better than other SAS detection methods and can better assist sleep experts in diagnosing sleep disorders.

Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0-67.6%, and the number of false-positive detections per participant was 23.4-52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805-0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776-0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648-0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.

A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry

OBJECTIVE

Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals.

APPROACH

Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2 with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study.

MAIN RESULTS

Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively.

SIGNIFICANCE

This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.

Intelligent facemask based on triboelectric nanogenerator for respiratory monitoring

The fast-spreading of novel coronavirus disease (COVID-19) has been sweeping around the globe and brought heavy casualties and economic losses, which creates dire needs for technological solutions into medical preventive actions. In this work, triboelectric nanogenerator for respiratory sensing (RS-TENG) has been designed and integrated with facemask, which endows the latter with respiratory monitoring function. The output of RS-TENG for respiratory flow can reach up to about 8 V and 0.8 μA respectively although it varies with different respiratory status, which proves the high sensitivity of RS-TENG for respiratory monitoring. An apnea alarm system can be constructed by combining the smart facemask with circuit modules so that timely alarm can be transmitted after people stop breathing. Furthermore, RS-TENG can be used to control household appliances, which brings convenience to the life of the disabled people. Considering its incomparable advantages such as small volume, easy fabrication, simple installation and economical applicability, such design is helpful for developing multifunctional health monitoring gadgets during the COVID-19 pandemic.

Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars

Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest-abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

3D Camera and Pulse Oximeter for Respiratory Events Detection

OBJECTIVE

The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO₂ to detect respiratory events comparable to polysomnography (PSG) based detection.

METHODS

We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO₂ desaturation to score respiratory events. The method was tested on 61 patients' synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated.

RESULTS

The predicted AHI scored an ICC r = 0.94 (0.90 - 0.96 at 95% confidence interval, p < 0.001) compared to manualPSG. Severity classification scored 80% accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohen's kappa = 0.72, accuracy = 0.88, sensitivity = 0.80, and specificity = 0.91.

CONCLUSION

Our detection method using SpO₂ and 3D camera had excellent reliability and substantial agreement with PSG-based scoring.

SIGNIFICANCE

This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

Validation of novel automatic ultra-wideband radar for sleep apnea detection

Background

To validate the accuracy of ultra-wideband (UWB) wireless radar for the screening diagnosis of sleep apnea.

Methods

One hundred and seventy-six qualified participants were successfully recruited. Apnea-hypopnea index (AHI) results from polysomnography (PSG) were reviewed by physicians, while the radar device automatically calculated AHI values with an embedded chip. All results were statistically analyzed.

Results

A UWB radar-based AHI algorithm was successfully developed according to respiratory movement and body motion signals. Of all 176 participants, 63 exhibited normal results (AHI <5/hr) and the remaining 113 were diagnosed with obstructive sleep apnea. Significant correlation was detected between radar AHI and PSG AHI (Intraclass correlation coefficient 0.98, P<0.001). Receiver operating characteristic curve (ROC) analysis revealed high sensitivity and specificity. High concordance in participants with varying gender, age, BMI, and PSG AHI was reached.

Conclusions

The UWB radar may be a portable, convenient, and reliable device for obstructive sleep apnea screening.

Non-contact monitoring of heart rate variability using medical radar for the evaluation of dynamic changes in autonomic nervous activity during a head-up tilt test

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin's correlation coefficient and Bland-Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin's correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.

Screening Sleep Disordered Breathing with Noncontact Measurement in a Clinical Site

[abstFig src='/00290002/06.jpg' width='300' text='Respiratory rate from simulator and Kinect' ]<span class=”bold”>Background and purpose:</span>It has been considered that sleep-disordered breathing disorders, such as sleep apnea syndrome (SAS), cause an increase in the risk of cardiovascular disease or traffic accident risk, and thus early detection of SAS is important. It has been also important for medical workers at clinical sites to quantitatively evaluate the respiratory condition of hospitalized patients who are asleep in a simple method. A noncontact-type system was proposed to monitor the respiratory condition of sleeping patients and minimized patient-related stress such that medical workers could use the system for SAS screening and perform a preliminary check prior to definite diagnosis.<span class=”bold”>Method:</span>The system included Microsoft Kinect™ for windows® (Kinect), a tripod, and a PC. A depth sensor of Kinect was used to measure movement in the thorax motion. Data obtained from periodic waveforms were divided with the intervals of 1 min, and the number of peaks was used to obtain the respiratory rate. Additionally, a frequency analysis was performed to calculate the respiratory frequency from a frequency at which the maximum amplitude was observed. In Experiment 1), a METI-man® PatientSimulator (CAE healthcare) (simulator) was used to study the respiratory rate and frequency calculated from the Kinect data by gradually changing the designated respiratory rate. In Experiment 2), the respiratory condition of four sleeping subjects was monitored to calculate their respiratory rate and frequencies. Furthermore, a video camera was used to confirm periodic waveforms and spectrum features of body movements during sleep.<span class=”bold”>Results:</span>In Experiment 1), the results indicated that both the respiratory rate and frequency corresponded to the designated respiratory rate in each time zone. In Experiment 2), the results indicated that the respiratory rate of examines 1, 2, 3, and 4 corresponded to 12.79±2.44 times/min (average ± standard deviation), 16.46±4.33 times/min, 28.24±2.79 times/min, and 13.05±2.64 times/min, respectively. The findings also indicated that the frequency of examines 1, 2, 3, and 4 corresponded to 0.20±0.04 Hz, 0.26±0.06 Hz, 0.45±0.12 Hz, and 0.22±0.06 Hz, respectively. The periodic waveforms and amplitude spectra were enhanced with respect to body movements although regular waveform data were obtained after the body movement occurred.<span class=”bold”>Discussions:</span>The results indicated that body movement and posture temporarily affected monitoring of the system. However, the findings also revealed that it was possible to calculate the respiratory rate and frequency, and thus it was considered that the system was useful for monitoring the respiration confirm with the non-contact or SAS screening of patients in clinical site.