The use of tracheal sounds for the diagnosis of sleep apnoea

Validation of Tracheal Sound-Based Respiratory Effort Monitoring for Obstructive Sleep Apnoea Diagnosis

Background: Respiratory effort is considered important in the context of the diagnosis of obstructive sleep apnoea (OSA), as well as other sleep disorders. However, current monitoring techniques can be obtrusive and interfere with a patient's natural sleep. This study examines the reliability of an unobtrusive tracheal sound-based approach to monitor respiratory effort in the context of OSA, using manually marked respiratory inductance plethysmography (RIP) signals as a gold standard for validation. Methods: In total, 150 patients were trained on the use of type III cardiorespiratory polygraphy, which they took to use at home, alongside a neck-worn AcuPebble system. The respiratory effort channels obtained from the tracheal sound recordings were compared to the effort measured by the RIP bands during automatic and manual marking experiments. A total of 133 central apnoeas, 218 obstructive apnoeas, 263 obstructive hypopneas, and 270 normal breathing randomly selected segments were shuffled and blindly marked by a Registered Polysomnographic Technologist (RPSGT) in both types of channels. The RIP signals had previously also been independently marked by another expert clinician in the context of diagnosing those patients, and without access to the effort channel of AcuPebble. The classification achieved with the acoustically obtained effort was assessed with statistical metrics and the average amplitude distributions per respiratory event type for each of the different channels were also studied to assess the overlap between event types. Results: The performance of the acoustic effort channel was evaluated for the events where both scorers were in agreement in the marking of the gold standard reference channel, showing an average sensitivity of 90.5%, a specificity of 98.6%, and an accuracy of 96.8% against the reference standard with blind expert marking. In addition, a comparison using the Embla Remlogic 4.0 automatic software of the reference standard for classification, as opposed to the expert marking, showed that the acoustic channels outperformed the RIP channels (acoustic sensitivity: 71.9%; acoustic specificity: 97.2%; RIP sensitivity: 70.1%; RIP specificity: 76.1%). The amplitude trends across different event types also showed that the acoustic channels exhibited a better differentiation between the amplitude distributions of different event types, which can help when doing manual interpretation. Conclusions: The results prove that the acoustically obtained effort channel extracted using AcuPebble is an accurate, reliable, and more patient-friendly alternative to RIP in the context of OSA.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor

BACKGROUND

Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)-a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body-or photoplethysmography (PPG).

METHODS

The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired.

RESULTS

The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM.

CONCLUSIONS

The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process.

Dynamic Bayesian Model for Detecting Obstructive Respiratory Events by Using an Experimental Model

In this study, we propose a model-based tool for the detection of obstructive apnea episodes by using ECG features from a single lead channel. Several sequences of recurrent apnea were provoked in separate 15-min periods in anesthetized rats during an experimental model of obstructive sleep apnea (OSA). Morphology-based ECG markers and the beat-to-beat interval (RR) were assessed in each sequence. These markers were used to train dynamic Bayesian networks (DBN) with different orders and feature combinations to find a good tradeoff between network complexity and apnea-detection performance. By using a filtering approach, the resulting DBNs were used to infer the apnea probability signal for subsequent episodes in the same rat. These signals were then processed using by 15-s epochs to determine whether epochs were classified as apneic or nonapneic. Our results showed that fifth-order models provided suitable RMSE values, since higher order models become significantly more complex and present worse generalization. A global threshold of 0.2 gave the best overall performance for all combinations tested, with Acc = 81.3%, Se = 69.8% and Sp = 81.5%, using only two parameters including the RR and Ds (R-wave downslope) markers. We concluded that multivariate models using DBNs represent a powerful tool for detecting obstructive apnea episodes in short segments, which may also serve to estimate the number of total events in a given time period.

Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions

In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks

Obstructive sleep apnea (OSA) is a chronic and prevalent condition with well-established comorbidities. However, many severe cases remain undiagnosed due to poor access to polysomnography (PSG), the gold standard for OSA diagnosis. Accurate home-based methods to screen for OSA are needed, which can be applied inexpensively to high-risk subjects to identify those that require PSG to fully assess their condition. A number of methods that analyse speech or breathing sounds to screen for OSA have been previously investigated. However, these methods have constraints that limit their use in home environments (e.g., they require specialised equipment, are not robust to background noise, are obtrusive or depend on tightly controlled conditions). This paper proposes a novel method to screen for OSA, which analyses sleep breathing sounds recorded with a smartphone at home. Audio recordings made over a whole night are divided into segments, each of which is classified for the presence or absence of OSA by a deep neural network. The percentage of segments predicted as containing evidence of OSA is then used to screen for the condition. Audio recordings made during home sleep apnea testing from 103 participants for 1 or 2 nights were used to develop and evaluate the proposed system. When screening for moderate OSA the acoustics based system achieved a sensitivity of 0.79 and a specificity of 0.80. The sensitivity and specificity when screening for severe OSA were 0.78 and 0.93, respectively. The system is suitable for implementation on consumer smartphones.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

Comparison of Drug-Induced Sleep Endoscopy and Natural Sleep Endoscopy in the Assessment of Upper Airway Pathophysiology During Sleep: Protocol and Study Design

Study Objectives: Obstructive sleep apnea (OSA) is increasingly recognized as a complex and heterogenous disorder. As a result, a "one-size-fits-all" management approach should be avoided. Therefore, evaluation of pathophysiological endotyping in OSA patients is emphasized, with upper airway collapse during sleep as one of the main features. To assess the site(s) and pattern(s) of upper airway collapse, natural sleep endoscopy (NSE) is defined as the gold standard. As NSE is labor-intensive and time-consuming, it is not feasible in routine practice. Instead, drug-induced sleep endoscopy (DISE) is the most frequently used technique and can be considered as the clinical standard. Flow shape and snoring analysis are non-invasive measurement techniques, yet are still evolving. Although DISE is used as the clinical alternative to assess upper airway collapse, associations between DISE and NSE observations, and associated flow and snoring signals, have not been quantified satisfactorily. In the current project we aim to compare upper airway collapse identified in patients with OSA using endoscopic techniques as well as flow shape analysis and analysis of tracheal snoring sounds between natural and drug-induced sleep. Methods: This study is a blinded prospective comparative multicenter cohort study. The study population will consist of adult patients with a recent diagnosis of OSA. Eligible patients will undergo a polysomnography (PSG) with NSE overnight and a DISE within 3 months. During DISE the upper airway is assessed under sedation by an experienced ear, nose, throat (ENT) surgeon using a flexible fiberoptic endoscope in the operating theater. In contrast to DISE, NSE is performed during natural sleep using a pediatric bronchoscope. During research DISE and NSE, the standard set-up is expanded with additional PSG measurements, including gold standard flow and analysis of tracheal snoring sounds. Conclusions: This project will be one of the first studies to formally compare collapse patterns during natural and drug-induced sleep. Moreover, this will be, to the authors' best knowledge, the first comparative research in airflow shape and tracheal snoring sounds analysis between DISE and NSE. These novel and non-invasive diagnostic methods studying upper airway mechanics during sleep will be simultaneously validated against DISE and NSE. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT04729478.

A New Sleep Staging System for Type III Sleep Studies Equipped with a Tracheal Sound Sensor

Type III sleep studies record cardio-respiratory channels only. Compared with polysomnography, which also records electrophysiological channels, they present many advantages: they are less expensive, less time-consuming, and more likely to be performed at home. However, their accuracy is limited by missing sleep information. That is why many studies present specific cardio-respiratory parameters to assess the causal effects of sleep stages upon cardiac or respiratory activities. For this paper, we gathered many parameters proposed in literature, leading to 1,111 features. The pulse oximeter, the PneaVoX sensor (recording tracheal sounds), respiratory inductance plethysmography belts, the nasal cannula and the actimeter provided the 112 worthiest ones for automatic sleep scoring. Then, a 3-step model was implemented: classification with a multi-layer perceptron, sleep transition rules corrections (from the AASM guidelines), and sequence corrections using a Viterbi hidden Markov model. The whole process was trained and tested using 300 and 100 independent recordings provided from patients suspected of having sleep breathing disorders. Results indicated that the system achieves substantial agreement with manual scoring for classifications into 2 stages (wake vs. sleep: mean Cohen's Kappa of 0.63 and accuracy rate Acc of 87.8%) and 3 stages (wake vs. R stage vs. NREM stage: mean of 0.60 and Acc of 78.5%). It indicates that the method could provide information to help specialists while diagnosing sleep. The presented model had promising results and may enhance clinical diagnosis.

Sleep apnea severity based on estimated tidal volume and snoring features from tracheal signals

Sleep apnea can be characterized by reductions in the respiratory tidal volume. Previous studies showed that the tidal volume can be estimated from tracheal sounds and movements called tracheal signals. Additionally, tracheal sounds include the sounds of snoring, a common symptom of obstructive sleep apnea. This study investigates the feasibility of estimating the severity of sleep apnea, as quantified by the apnea/hypopnea index (AHI), using the estimated tidal volume and snoring sounds extracted from tracheal signals. Tracheal signals were recorded simultaneously with polysomnography (PSG). The tidal volume was estimated from tracheal signals. The reductions in the tidal volume were detected as potential respiratory events. Additionally, features related to snoring sounds, which quantified variability, temporal clusters, and dominant frequency of snores, were extracted. A step-wise regression model and a greedy search algorithm were used sequentially to select the optimal set of features to estimate the apnea/hypopnea index and classify participants into healthy individuals and patients with sleep apnea. Sixty-one participants with suspected sleep apnea (age: 51 ± 16, body mass index: 29.5 ± 6.4 kg/m² , apnea/hypopnea index: 20.2 ± 21.2 event/h) who were referred for a sleep test were recruited. The estimated apnea/hypopnea index was strongly correlated with the polysomnography-based apnea/hypopnea index (R²  = 0.76, p < 0.001). The accuracy of detecting sleep apnea for the apnea/hypopnea index cutoff of 15 events/h was 78.69% and 83.61% with and without using snore-related features. These findings suggest that acoustic estimation of airflow and snore-related features can provide a convenient and reliable method for screening of sleep apnea.

Estimation of heart rate from tracheal sounds recorded for the sleep apnea syndrome diagnosis

Obstructive sleep apnea is a common sleep disorder with a high prevalence and often accompanied by significant snoring activity. To diagnose this condition, polysomnography is the standard method, where a neck microphone could be added to record tracheal sounds. These can then be used to study the characteristics of breathing, snoring or apnea. In addition cardiac sounds, also present in the acquired data, could be exploited to extract heart rate. The paper presents new algorithms for estimating heart rate from tracheal sounds, especially in very loud snoring environment. The advantage is that it is possible to reduce the number of diagnostic devices, especially for compact home applications. Three algorithms are proposed, based on optimal filtering and cross-correlation. They are tested firstly on one patient presenting significant pathology of apnea syndrome, with a recording of 509 min. Secondly, an extension to a database of 16 patients is proposed (16 hours of recording). When compared to a reference ECG signal, the final results obtained from tracheal sounds reach an accuracy of 81% to 98% and an RMS error from 1.3 to 4.2 bpm, according to the level of snoring and to the considered algorithm.

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

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.

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.

Automatic Respiratory Phase Identification Using Tracheal Sounds and Movements During Sleep

One of the most important signals to assess respiratory function, especially in patients with sleep apnea, is airflow. A convenient method to estimate airflow is based on analyzing tracheal sounds and movements. However, this method requires accurate identification of respiratory phases. Our goal is to develop an automatic algorithm to analyze tracheal sounds and movements to identify respiratory phases during sleep. Data from adults with suspected sleep apnea who were referred for in-laboratory sleep studies were included. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device attached to the suprasternal notch. First, an adaptive detection algorithm was developed to localize the respiratory phases in tracheal sounds. Then, for each phase, a set of morphological features from sound energy and tracheal movement were extracted to classify the localized phases into inspirations or expirations. The average error and time delay of detecting respiratory phases were 7.62% and 181 ms during normal breathing, 8.95% and 194 ms during snoring, and 13.19% and 220 ms during respiratory events, respectively. The average classification accuracy was 83.7% for inspirations and 75.0% for expirations. Respiratory phases were accurately identified from tracheal sounds and movements during sleep.

Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed—Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR.

A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification

We propose an unobtrusive, wearable, and wireless system for the pre-screening and follow-up in the domestic environment of specific sleep-related breathing disorders. This group of diseases manifests with episodes of apnea and hypopnea of central or obstructive origin, and it can be disabling, with several drawbacks that interfere in the daily patient life. The gold standard for their diagnosis and grading is polysomnography, which is a time-consuming, scarcely available test with many wired electrodes disseminated on the body, requiring hospitalization and long waiting times. It is limited by the night-by-night variability of sleep disorders, while inevitably causing sleep alteration and fragmentation itself. For these reasons, only a small percentage of patients achieve a definitive diagnosis and are followed-up. Our device integrates photoplethysmography, an accelerometer, a microcontroller, and a bluetooth transmission unit. It acquires data during the whole night and transmits to a PC for off-line processing. It is positioned on the nasal septum and detects apnea episodes using the modulation of the photoplethysmography signal during the breath. In those time intervals where the photoplethysmography is detecting an apnea, the accelerometer discriminates obstructive from central type thanks to its excellent sensitivity to thoraco-abdominal movements. Tests were performed on a hospitalized patient wearing our integrated system and the type III home sleep apnea testing recommended by The American Academy of Sleep Medicine. Results are encouraging: sensitivity and precision around 90% were achieved in detecting more than 500 apnea episodes. Least thoraco-abdominal movements and body position were successfully classified in lying down control subjects, paving the way toward apnea type classification.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea

A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess the utility of two important machine-learning techniques to classify subjects with obstructive sleep apnea (OSA) using their daytime tracheal breathing sounds. We evaluate and compare the performance of the random forest (RF) and regularized logistic regression (LR) as feature selection tools and classification approaches for wakefulness OSA screening. Results show that the RF, which is a low-variance committee-based approach, outperforms the regularized LR in terms of blind-testing accuracy, specificity, and sensitivity with 3.5%, 2.4%, and 3.7% improvement, respectively. However, the regularized LR was found to be faster than the RF and resulted in a more parsimonious model. Consequently, both the RF and regularized LR feature reduction and classification approaches are qualified to be applied for the daytime OSA screening studies, depending on the nature of data and applications' purposes. Graphical Abstract.

Acoustic characterization of upper airway variations from wakefulness to sleep with respect to obstructive sleep apnea

The upper airway (UA) is in general thicker and narrower in obstructive sleep apnea (OSA) population than in normal. Additionally, the UA changes during sleep are much more in the OSA population. The UA changes can alter the tracheal breathing sound (TBS) characteristics. Therefore, we hypothesize the TBS changes from wakefulness to sleep are significantly correlated to the OSA severity; thus, they may represent the physiological characteristics of the UA. To investigate our hypothesis, we recorded TBS of 18 mild-OSA (AHI < 15) and 22 moderate/severe-OSA (AHI > 15) during daytime (wakefulness) and then during sleep. The power spectral density (PSD) of the TBS was calculated and compared within the two OSA groups and between wakefulness and sleep. The average PSD of the mild-OSA group in the low-frequency range (< 280 Hz) was found to be decreased significantly from wakefulness to sleep (p-value < 10^-4). On the other hand, the average PSD of the moderate/severe-OSA group in the high-frequency range (> 900 Hz) increased marginally significantly from wakefulness to sleep (p-value < 9 × 10^-3). Our findings show that the changes in spectral characteristics of TBS from wakefulness to sleep correlate with the severity of OSA and can represent physiological variations of UA. Therefore, TBS analysis has the potentials to assist with diagnosis and clinical management decisions in OSA patients based on their OSA severity stratification; thus, obviating the need for more expensive and time-consuming sleep studies. Graphical abstract Tracheal breathing sound (TBS) changes from wakefulness to sleep and their correlation with Obstructive sleep apnea (OSA) were investigated in individuals with different levels of OSA severity. We also assessed the classification power of the spectral characteristics of these TBS for screening purposes. Consequently, we analyzed and compared spectral characteristics of TBS recorded during wakefulness (a combination of mouth and nasal TBS) to those during sleep for mild and moderate/severe OSA groups.

Diagnosis of sleep apnea without sensors on the patient's face

STUDY OBJECTIVES

Thermistors, nasal cannulas, and respiratory inductance plethysmography (RIP) are the recommended reference sensors of the American Academy of Sleep Medicine (AASM) for the detection and characterization of apneas and hypopneas; however, these sensors are not well tolerated by patients and have poor scorability. We evaluated the performance of an alternative method using a combination of tracheal sounds (TSs) and RIP signals.

METHODS

Consecutive recordings of 70 adult patients from the Pays de la Loire Sleep Cohort were manually scored in random order using the AASM standard signals and the combination TS and RIP signals, without respiratory sensors placed on the patient's face. The TS-RIP scoring used the TS and RIP-flow signals for detection of apneas and hypopneas, respectively, and the suprasternal pressure and RIP belt signals for the characterization of apneas.

RESULTS

Sensitivity and specificity of the TS-RIP combination were 96.21% and 91.34% for apnea detection and 89.94% and 93.25% for detecting hypopneas, respectively, with a kappa coefficient of 0.87. For the characterization of apneas, sensitivity and specificity were 98.67% and 96.17% for obstructive apneas, 92.66% and 99.36% for mixed apneas, and 96.14% and 98.89% for central apneas, respectively, with a kappa coefficient of 0.94. The TS-RIP scoring revealed a high agreement for classifying obstructive sleep apnea into severity classes (none, mild, moderate, and severe obstructive sleep apnea) with a Cohen's kappa coefficient of 0.96.

CONCLUSIONS

Compared with the AASM reference sensors, the TS-RIP combination allows reliable noninvasive detection and characterization of respiratory events with a high degree of sensitivity and specificity. TS-RIP combination could be used for diagnosis of obstructive sleep apnea in adults, either as an alternative to the AASM sensors or in combination with the recommended AASM sensors.

Journal of Clinical Monitoring and Computing 2018-2019 end of year summary: respiration

This paper reviews 28 papers or commentaries published in Journal of Clinical Monitoring and Computing in 2018 and 2019, within the field of respiration. Papers were published covering endotracheal tube cuff pressure monitoring, ventilation and respiratory rate monitoring, lung mechanics monitoring, gas exchange monitoring, CO₂ monitoring, lung imaging, and technologies and strategies for ventilation management.

Breathing detection from tracheal sounds in both temporal and frequency domains in the context of phrenic nerve stimulation

Electrical stimulation of the phrenic nerves via implanted devices allows to counteract some disadvantages of mechanical ventilation in patients with high tetraplegia or Ondine's syndrome. Existing devices do not allow to monitor breathing or to adapt the electroventilation to patients' actual needs. A reliable breathing monitor with an inbuilt alarm function would improve patient safety. In our study, a real-time acoustic breathing detection method is proposed as a possible solution to improve implanted phrenic stimulation. A new algorithm to process tracheal sounds has been developed. It combines breathing detection in both temporal and frequency domains. The algorithm has been applied on recordings from 18 healthy participants. The obtained average sensitivity, specificity and accuracy of the detection are: 99.31%, 96.84% and 98.02%, respectively. These preliminary results show a first positive proof of the interest of such an approach. Additional experiments are needed, including longer recordings from individuals with tetraplegia or Ondine Syndrome in various environments to go further in the validation.

Tracheal sounds for the scoring of sleep respiratory events in children

STUDY OBJECTIVES

Oronasal thermistor and nasal cannula are recommended for the scoring of respiratory events (RE) but these sensors are poorly tolerated in children. The aim of the study was to evaluate tracheal sounds (TS) and suprasternal pressure (SSP) for the scoring of RE during sleep in children.

METHODS

We compared the detection and characterization of RE by AASM-recommended sensors ("AASM" scoring), with the detection and characterization of RE by the combination of TS and SSP with respiratory inductance plethysmography-sum (TS-RIP scoring), and TS and SSP only (TS scoring).

RESULTS

The recordings of 17 patients were analyzed. The TS, SSP, and RIP flow signals were present during 95%, 95%, and 99% of the validated recording time, respectively, as compared to 79% and 86% for nasal cannula and oronasal thermistor. A total of 1,456 RE were scored with the "AASM" scoring, 1,335 with the TS-RIP scoring, and 1,311 with the TS scoring. Sensitivity for apnea and hypopnea detection was 88% and 84% for the TS-RIP scoring, and 86% and 77% for the TS scoring. For apnea characterization, the TS-RIP scoring sensitivities and specificities were 97% and 100%, 76% and 98%, and 95% and 97%, for obstructive, mixed, and central apnea, respectively. For the TS scoring, they were 95% and 100%, 95% and 97%, and 91% and 97%, respectively.

CONCLUSIONS

TS and SSP + RIP-sum has a good sensitivity and specificity for the detection and characterization of apnea and hypopnea in children. TS and SSP alone have good sensitivity and specificity for apnea detection and characterization but lower sensitivity for hypopnea detection.

Sleep/Wakefulness Detection Using Tracheal Sounds and Movements

PURPOSE

The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements.

MATERIALS AND METHODS

Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography.

RESULTS

Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3±8.66% with a sensitivity of 87.8±10.8 % (sleep), specificity of 71.4±18.5% (awake), F1 of 88.1±9.3% and Cohen's kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p<0.001) and 0.70 (p<0.001), respectively.

CONCLUSION

Sleep/wakefulness periods can be detected using tracheal sound and movements. The results of this study combined with our previous studies on screening sleep apnea with tracheal sounds provide strong evidence that respiratory sounds analysis can be used to develop robust, convenient and cost-effective portable devices for sleep apnea monitoring.

Comparison of Apnea Detection Using Oronasal Thermal Airflow Sensor, Nasal Pressure Transducer, Respiratory Inductance Plethysmography and Tracheal Sound Sensor

STUDY OBJECTIVES

Evaluation of apnea detection using a tracheal sound (TS) sensor during sleep in patients with obstructive sleep apnea.

METHODS

Polysomnographic recordings of 32 patients (25 male, mean age 66.7 ± 15.3 years, and mean body mass index 30.1 ± 4.5 kg/m²) were analyzed to compare the detection of apneas by four different methods of airflow signals: oronasal thermal airflow sensor (thermistor), nasal pressure transducer (NP), respiratory inductance plethysmography (RIPsum) and TS. The four used signals were scored randomly and independently from each other according to American Academy of Sleep Medicine rules. Results of apnea detection using NP, RIPsum and TS signals were compared to those obtained by thermistor as a reference signal.

RESULTS

The number of apneas detected by the thermistor was 4,167. The number of apneas detected using the NP was 5,416 (+29.97%), using the RIPsum was 2,959 (-29.71%) and using the TS was 5,019 (+20.45%). The kappa statistics (95% confidence interval) were 0.72 (0.71 to 0.74) for TS, 0.69 (0.67 to 0.70) for NP, and 0.57 (0.55 to 0.59) for RIPsum. The sensitivity/specificity (%) with respect to the thermistor were 99.23/69.27, 64.07/93.06 and 96.06/76.07 for the NP, RIPsum and TS respectively.

CONCLUSIONS

With the sensor placed properly on the suprasternal notch, tracheal sounds could help detecting apneas that are underscored by the RIPsum and identify apneas that may be overscored by the NP sensor due to mouth breathing. In the absence of thermistor, TS sensors can be used for apnea detection.

CLINICAL TRIAL REGISTRATION

Registry: German Clinical Trials Register (DRKS), Title: Using the tracheal sound probe of the polygraph CID102 to detect and differentiate obstructive, central, and mixed sleep apneas in patients with sleep disordered breathing, Identifier: DRKS00012795, URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00012795.