Search Results (12)

Spirometry is widely regarded as the clinical gold standard for quantifying lung function. It plays a central role in the diagnosis and management of cardiopulmonary disorders, including asthma and chronic obstructive pulmonary disease (COPD). However, the procedure relies on a forceful and often stressful expiratory maneuver that may cause patient discomfort and require substantial effort, frequently necessitating active coaching and trained personnel to ensure reproducible measurements. In this paper, we present the Digital Spirometry Patch (DSP), a soft, flexible, wearable patch capable of estimating lung function parameters by utilizing low-effort breathing maneuvers. Eighteen participants performed low-effort and forceful breathing maneuvers while wearing the DSP to collect tracheal sound and chest movement signals for spirometric parameter estimation using elastic net and simple linear regression. Using leave-one-subject-out cross-validation, the elastic net models achieved RMSEs of 0.668 L, 0.224 L, and 0.428 L/s for FVC, FEV₁, and PEF, respectively, using low-effort breathing maneuvers, and 0.499 L, 0.304 L, and 0.891 L/s using forceful exhalation maneuvers. These results demonstrate the potential of the DSP as a wearable, low-effort alternative for estimating lung function outside of conventional spirometry settings. Full article

Obstructive sleep apnea (OSA), defined by repetitive airway obstruction during sleep, is significantly underdiagnosed, mainly due to the resource-intensive and time-consuming nature of sleep assessment technologies. Machine learning analysis of the tracheal breathing sounds (TBS) whilst awake offers an alternative approach for OSA quick screening. This study aimed to address the challenge of wakefulness OSA detection using TBS recorded with an inexpensive microphone in a noisy environment. Data of 247 individuals with various degrees of OSA severity were analyzed. Recorded data were segmented into inspiration and expiration phases, followed by acoustic features extraction, feature reduction, and classification. A two-level ensemble architecture was implemented. Nine sub-classifiers were stratified by anthropometric profiles. Each sub-classifier was constructed as an ensemble of bagged decision trees, with a final prediction via probability-based voting. The proposed algorithm achieved an accuracy of 77.1%, sensitivity of 84.3%, and specificity of 59.9%. Although these results have lower performance than those obtained previously using a high-quality microphone in a quiet room, they demonstrate that acoustic OSA detection whilst awake remains feasible, even in very noisy environments. Nevertheless, microphone quality emerged as a key determinant of classification performance. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a prevalent, yet underdiagnosed, disorder associated with cardiovascular and cognitive risks. While overnight polysomnography (PSG) remains the diagnostic gold standard, it is resource-intensive and impractical for large-scale rapid screening. Methods: This study extends prior work on feature extraction and binary classification using tracheal breathing sounds (TBS) and anthropometric data by introducing a meta-modeling framework that utilizes machine learning (ML) and aggregates six one-vs.-one classifiers for multi-class OSA severity prediction. We employed out-of-bag (OOB) estimation and three-fold cross-validation to assess model generalization performance. To enhance reliability, the framework incorporates conformal prediction to provide calibrated confidence sets. Results: In the three-class setting (non, mild, moderate/severe), the model achieved 76.7% test accuracy, 77.7% sensitivity, and 87.1% specificity, with strong OOB performance of 91.1% accuracy, 91.6% sensitivity, and 95.3% specificity. Three-fold confirmed stable performance across folds (mean accuracy: 77.8%; mean sensitivity: 78.6%; mean specificity: 76.4%) and conformal prediction achieved full coverage with an average set size of 2. In the four-class setting (non, mild, moderate, severe), the model achieved 76.7% test accuracy, 75% sensitivity, and 92% specificity, with OOB performance of 88.2% accuracy, 91.6% sensitivity, and 88.2% specificity. Conclusions: These findings support the potential of this non-invasive system as an efficient and rapid OSA severity assessment whilst awake, offering a scalable alternative to PSG for large-scale screening and clinical triaging. Full article

Background: Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders associated with cardiovascular complications, cognitive impairments, and reduced quality of life. Early and accurate diagnosis is essential. The present gold standard, polysomnography, is expensive and resource-intensive. This work develops a non-invasive machine-learning-based framework to classify four OSA severity groups (non, mild, moderate, and severe) using tracheal breathing sounds (TBSs) and anthropometric variables. Methods: A total of 199 participants were recruited, and TBS were recorded whilst awake (wakefulness) using a suprasternal microphone. The workflow included the following steps: signal preprocessing (segmentation, filtering, and normalization), multi-domain feature extraction representing spectral, temporal, nonlinear, and morphological features, adaptive feature normalization, and a three-stage feature selection that combined univariate filtering, Shapley Additive Explanations (SHAP)-based ranking, and recursive feature elimination (RFE). The classification included training ensemble learning models via bootstrap aggregation and validating them using stratified k-fold cross-validation (CV), while preserving the OSA severity and anthropometric distributions. Results: The proposed framework performed well in discriminating among OSA severity groups. TBS features, combined with anthropometric ones, increased classification performance and reliability across all severity classes, providing proof for the efficacy of non-invasive audio biomarkers for OSA screening. Conclusions: TBS-based model’s features, coupled with anthropometric information, offer a promising alternative or supplement to PSG for OSA severity detection. The approach provides scalability and accessibility to extend screening and potentially enables earlier detection of OSA, compared to cases that might remain undiagnosed without screening. Full article

Background: Tracheal breathing sounds (TBS) have demonstrated strong potential as a non-invasive, wakefulness-based diagnostic tool for obstructive sleep apnea (OSA); yet the relationship between specific upper airway anatomical features and the resulting TBS spectra remains insufficiently understood. This study aims to enhance the diagnostic utility of TBS in OSA by investigating how the upper airway anatomy influences TBS spectral characteristics. Method: Patient-specific computational models of the upper airway were reconstructed from high-resolution CT scans of a healthy subject and an individual with OSA. Additional variants were generated with targeted constrictions at the velopharynx, oropharynx, and trachea, based on clinically reported anatomical ranges. Airflow dynamics were simulated using Large Eddy Simulation (LES), and the resulting acoustic responses were computed via Lighthill’s acoustic analogy within a hybrid aero-acoustic framework. Results: Oropharyngeal constriction generated the most spatially concentrated vorticity patterns among single-region constricted models. Airway Resistance analysis revealed that severe velopharyngeal and oropharyngeal constrictions contributed most to regional airway resistance. Spectral analysis showed that velopharyngeal narrowing produced a progressive downward shift in the third resonance peak (1000–1700 Hz), while oropharyngeal narrowing induced an upward shift of the third peak and a downward shift of the fourth peak (1700–2500 Hz). These frequency shifts were attributed to the effective role of acoustic mass and airway compliance. Conclusions: Anatomical modifications of the upper airway produce region-specific changes in both flow and acoustic responses. These findings support the use of TBS spectral analysis for non-invasive localization of airway obstructions in OSA. Full article

Biomedical signal analysis underpins modern healthcare by enabling accurate diagnosis, continuous physiological monitoring, and informed patient management. While deep learning excels at automated feature extraction and end-to-end modeling, classical ML remains essential for tasks requiring interpretability, data efficiency, and clinical transparency. This review synthesizes advances in ML methods including Support Vector Machines, Random Forests, and Decision Trees focusing on physiologically informed feature engineering, robust feature selection, and meaningful model interpretation. We provide guidelines for signal preprocessing, domain-specific feature extraction, and selection strategies across standard biomedical signals such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), Electrovestibulography (EVestG), and tracheal breathing sounds (TBSs). Reviewing TBS studies illustrates an end-to-end workflow highlighting common features and classifiers alongside practical challenges and solutions. Reported ML application performance ranges from 85 to 94% accuracy for EEG, ECG, and EMG, to 82% specificity for TBSs, emphasizing the trade-off between interpretability and predictive performance. Marginal accuracy gains alone do not constitute meaningful progress unless they enhance clinical insight, actionable decision-making, or model transparency. Finally, we compare ML with DL, discuss strengths and limitations, and provide recommendations and future directions for developing robust, interpretable, and clinically relevant biomedical ML. Full article

Obstructive sleep apnea (OSA) is a commonly underdiagnosed condition that not only increases the risk of accidents but also significantly contributes to a wide range of health complications, including heightened perioperative morbidity and mortality risks during surgeries under general anesthesia. Polysomnography (PSG), which is the diagnostic gold standard, is costly, requires skilled technicians, is time-consuming, and is not always accessible. This study presents a fast, objective, and non-invasive method for detecting OSA severity by analyzing tracheal breathing sounds (TBS) recorded during wakefulness in supine position. Features were extracted from six binary (1-vs-1) severity comparisons—Non-OSA, Mild, Moderate, and Severe—and combined with anthropometric characteristics for classification. The data of 199 subjects (74 Non-OSA, 35 Mild, 50 Moderate, and 40 Severe) were analyzed, the data of 169 and 30 was used for training and blind testing, respectively, and the training dataset was shuffled 10 times to avoid any bias during training. Multiple machine learning models were evaluated, and the best-performing model for each was saved. Across six experimental models comparing OSA severity levels, the most balanced performance was achieved by the Base Model of Non-OSA vs. Severe-OSA using the support vector machine algorithm, with 88.2% accuracy, 83.3% sensitivity, and 90.9% specificity. While Random Forests in the Base Model of Non-OSA vs. Mild-OSA achieved 100% sensitivity, its accuracy was lower (81.2%). The results confirm the reliability and robustness of the proposed approach, providing a basis for OSA severity screening in under 10 min during wakefulness. Full article

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice. Full article

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. Full article

Background: The apnea/hypopnea index (AHI) is the primary outcome of a polysomnography assessment (PSG) for determining obstructive sleep apnea (OSA) severity. However, other OSA severity parameters (i.e., total arousal index, mean oxygen saturation (SpO2%), etc.) are crucial for a full diagnosis of OSA and deciding on a treatment option. PSG assessments and home sleep tests measure these parameters, but there is no screening tool to estimate or predict the OSA severity parameters other than the AHI. In this study, we investigated whether a combination of breathing sounds recorded during wakefulness and anthropometric features could be predictive of PSG parameters. Methods: Anthropometric information and five tracheal breathing sound cycles were recorded during wakefulness from 145 individuals referred to an overnight PSG study. The dataset was divided into training, validation, and blind testing datasets. Spectral and bispectral features of the sounds were evaluated to run correlation and classification analyses with the PSG parameters collected from the PSG sleep reports. Results: Many sound and anthropometric features had significant correlations (up to 0.56) with PSG parameters. Using combinations of sound and anthropometric features in a bilinear model for each PSG parameter resulted in correlation coefficients up to 0.84. Using the evaluated models for classification with a two-class random-forest classifier resulted in a blind testing classification accuracy up to 88.8% for predicting the key PSG parameters such as arousal index. Conclusions: These results add new value to the current OSA screening tools and provide a new promising possibility for predicting PSG parameters using only a few seconds of breathing sounds recorded during wakefulness without conducting an overnight PSG study. Full article

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. Full article

Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds’ amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r² = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r² = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found. Full article