An Overview of Respiratory Airflow Estimation Techniques: Acoustic vs Non-Acoustic

Muthusamy, Priya Devi; Sundaraj, Kenneth; Manap, Nurulfajar Abd

doi:10.1109/icsipa45851.2019.8977736

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Respiratory airflow estimation using lung sounds has been investigated in a few studies (Hossain & Moussavi, 2002; Messner et al., 2017; Schudt et al., 2014). However, the quality of lung sounds can be compromised by noises in the presence of chest hair, damping effect of fat around the chest in the overweight population, and potential abnormal sounds in individuals with lung disease resulting in higher estimation errors compared to tracheal sounds (Hossain & Moussavi, 2002; Muthusamy et al., 2019; Yap & Moussavi, 2002). Tracheal sounds can reflect respiration more clearly while encompassing a wider spectral range compared with lung sounds recorded over the chest (Gavriely & Cugell, 1996).…”

Section: Introductionmentioning

confidence: 99%

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

Ghahjaverestan

Kabir

Saha

et al. 2021

Journal of Sleep Research

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

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Section: Introductionmentioning

confidence: 99%

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

Ghahjaverestan

Kabir

Saha

et al. 2021

Journal of Sleep Research

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“…The measurement method results in various requirements for the stiffness of the chamber, heat and moisture transfer, and calibration [11]. Other methods of measuring respiratory volumes result from computed tomography [12], capnography [13], acoustic monitoring [14], impedance pneumography [15], doppler radar [16], electrocardiography [17], and accelerometers [18].…”

Section: Fundamentals Of Respiratory Measurementmentioning

confidence: 99%

Depth-Based Measurement of Respiratory Volumes: A Review

Wichum

Wiede

Seidl

2022

Sensors

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Depth-based plethysmography (DPG) for the measurement of respiratory parameters is a mobile and cost-effective alternative to spirometry and body plethysmography. In addition, natural breathing can be measured without a mouthpiece, and breathing mechanics can be visualized. This paper aims at showing further improvements for DPG by analyzing recent developments regarding the individual components of a DPG measurement. Starting from the advantages and application scenarios, measurement scenarios and recording devices, selection algorithms and location of a region of interest (ROI) on the upper body, signal processing steps, models for error minimization with a reference measurement device, and final evaluation procedures are presented and discussed. It is shown that ROI selection has an impact on signal quality. Adaptive methods and dynamic referencing of body points to select the ROI can allow more accurate placement and thus lead to better signal quality. Multiple different ROIs can be used to assess breathing mechanics and distinguish patient groups. Signal acquisition can be performed quickly using arithmetic calculations and is not inferior to complex 3D reconstruction algorithms. It is shown that linear models provide a good approximation of the signal. However, further dependencies, such as personal characteristics, may lead to non-linear models in the future. Finally, it is pointed out to focus developments with respect to single-camera systems and to focus on independence from an individual calibration in the evaluation.

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Real-Time Wireless ECG-Derived Respiration Rate Estimation using an Autoencoder with a DCT Layer

Pan

Zhu

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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An Overview of Respiratory Airflow Estimation Techniques: Acoustic vs Non-Acoustic

Cited by 3 publications

References 14 publications

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

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

Depth-Based Measurement of Respiratory Volumes: A Review

Real-Time Wireless ECG-Derived Respiration Rate Estimation using an Autoencoder with a DCT Layer

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