Detection of mouse cough based on sound monitoring and respiratory airflow waveforms.
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ABSTRACT:
SUBMITTER: Chen L
PROVIDER: S-EPMC3605448 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The binary approach to the diagnosis of Chronic Bronchitis (CB) is a major barrier to the study of the disease. We investigated whether severity of productive cough can be graded using symptoms and presence of fixed airflow obstruction (FAO), and whether the severity correlates with health status, exposures injurious to the lung, biomarkers of inflammation, and measures of airway wall thickening. Findings from a cross-sectional sample of 1,422 participants from the Lovelace Smokers Cohort (LSC) were validated in 4,488 participants from the COPDGene cohort (COPDGene). Health status was based on the St. George's Respiratory Questionnaire, and Medical Outcomes Study 36-Item Short Form Health Survey. Circulating CC16 levels were quantified by ELISA (LSC), and airway wall thickening was measured using computed tomography (COPDGene). FAO was defined as postbronchodilator FEV1/FVC <0.7. The presence and duration of productive cough and presence of FAO or wheeze were graded into Healthy Smokers, Productive Cough (PC), Chronic PC, PC with Signs of Airflow Obstruction, and Chronic PC with Signs of Airflow Obstruction. In both cohorts, higher grade of severity correlated with lower health status, greater frequency of injurious exposures, greater airway wall thickening, and lower circulating CC16 levels. Further, longitudinal follow-up suggested that disease resolution can occur at every grade of severity but is more common in groups of lower severity and least common once airway remodeling develops. Therefore, severity of productive cough can be graded based on symptoms and FAO and early intervention may benefit patients by changing the natural history of disease.
| S-EPMC6239864 | biostudies-literature
Project description:Despite the importance of respiration and metabolism measurement in daily life, they are not widely available to ordinary people because of sophisticated and expensive equipment. Here, we first report a straightforward and economical approach to monitoring respiratory function and metabolic rate using a wearable piezoelectric airflow transducer (WPAT). A self-shielded bend sensor is designed by sticking two uniaxially drawn piezoelectric poly l-lactic acid films with different cutting angles, and then the bend sensor is mounted on one end of a plastic tube to engineer the WPAT. The airflow sensing principle of the WPAT is theoretically determined through finite element simulation, and the WPAT is calibrated with a pulse calibration method. We prove that the WPAT has similar accuracy (correlation coefficient >0.99) to a pneumotachometer in respiratory flow and lung volume assessment. We demonstrate metabolism measurement using the WPAT and the relationship between minute volume and metabolic rates via human wear trials. The mean difference of measured metabolic rates between the WPAT and a Biopac indirect calorimeter is 0.015 kcal/min, which shows comparable performance. Significantly, unlike the Biopac indirect calorimeter with an airflow sensor, an oxygen gas sensor, and a carbon dioxide gas sensor, we merely use the simple-structured WPAT to measure metabolism. Thus, we expect the WPAT technology to provide a precise, convenient, and cost-effective respiratory and metabolic monitoring solution for next-generation medical home care applications and wearable healthcare systems.
| S-EPMC9425556 | biostudies-literature
Project description:Viral respiratory tract infection is the most frequent cause of acute cough and is reported at onset in about one third of patients with chronic cough. Persistent infection is therefore one possible explanation for the cough reflex hypersensitivity and pulmonary inflammation reported in chronic cough patients.Bronchoscopic endobronchial biopsies and bronchoalveolar lavage cell counts were obtained from ten healthy volunteers and twenty treatment resistant chronic cough patients (10 selected for lavage lymphocytosis). A screen for known respiratory pathogens was performed on biopsy tissue. Chronic cough patients also underwent cough reflex sensitivity testing using citric acid.There was no significant difference in incidence of infection between healthy volunteers and chronic cough patients (p?=?0.115) or non-lymphocytic and lymphocytic groups (p?=?0.404). BAL cell percentages were not significantly different between healthy volunteers and chronic cough patients without lymphocytosis. Lymphocytic patients however had a significantly raised percentage of lymphocytes (p?<?0.01), neutrophils (p?<?0.05), eosinophils (p?<?0.05) and decreased macrophages (p?<?0.001) verses healthy volunteers. There was no significant difference in the cough reflex sensitivity between non-lymphocytic and lymphocytic patients (p?=?0.536).This study indicates latent infection in the lung is unlikely to play an important role in chronic cough, but a role for undetected or undetectable pathogens in either the lung or a distal site could not be ruled out.Current Controlled Trials ISRCTN62337037 & ISRCTN40147207.
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Project description:IntroductionThe COVID-19 pandemic has radically disrupted traditional models of medical education, forcing rapid evolution in the delivery of clinical training. As a result, clinical educators must quickly transition away from in-person sessions and develop effective virtual learning opportunities instead. This virtual resource was designed to replace a clinical simulation session for the physical examination course for medical students in the preclinical years.MethodsWe designed an online interactive module in three sections for preclinical (first- or second-year) medical students who had not yet learned the respiratory physical exam. The first section incorporated demonstration and practice of the components of the respiratory physical exam that could be effectively taught via videoconferencing software. Following this, students conducted a telemedicine encounter with a standardized patient and received patient-centered feedback evaluating their communication skills. The final segment involved a case discussion and clinical reasoning component.ResultsThese sessions were implemented for 122 first-year medical students. The module was well received by the students. A majority felt that it helped improve their telemedicine communication skills (93%), interpretation of physical exam findings (84%), development of differential diagnosis (95%), and correlation of clinical and basic science content (93%).DiscussionOur pilot educational session demonstrates that this virtual instruction method is an effective tool for teaching basic clinical skills during medical school. Virtual learning resources allow remote instruction to take place and can be a supplement when face-to-face clinical teaching is not possible.
| S-EPMC7751326 | biostudies-literature
Project description:BackgroundDespite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need.ObjectivesIn light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection).MethodsThis work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count.ResultsSound collection: the application demonstrated the ability to continuously record sounds using the phone's internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data.ConclusionsSound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.
| S-EPMC7011715 | biostudies-literature
Project description:Wearing surgical or N95 masks is effective in reducing the infection risks of airborne infectious diseases. However, in the literature there are no detailed boundary conditions for airflow from a cough when a surgical or N95 mask is worn. These boundary conditions are essential for accurate prediction of exhaled particle dispersion by computational fluid dynamics (CFD). This study first constructed a coughing manikin with an exhalation system to simulate a cough from a person. The smoke visualization method was used to measure the airflow profile from a cough. To validate the setup of the coughing manikin, the results were compared with measured data from subject tests reported in the literature. The validated coughing manikin was then used to measure the airflow boundary conditions for a cough when a surgical mask was worn and when an N95 mask was worn, respectively. Finally, this study applied the developed airflow boundary conditions to calculate person-to-person particle transport from a cough when masks are worn. The calculated exhaled particle patterns agreed well with the smoke pattern in the visualization experiments. Furthermore, the calculated results indicated that, when the index person wore a surgical and a N95 mask, the total exposure of the receptor was reduced by 93.0% and 98.8%, respectively.
| S-EPMC9538929 | biostudies-literature
Project description:An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.
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Project description:Flexible respiratory monitoring devices have become available for outside-hospital application scenarios attributable to their improved system wearability. However, the complex fabrication process of such flexible devices results in high prices, limiting their applications in real-life scenarios. This study proposes a flexible, low-cost, and easy-processing paper-based humidity sensor for sleep respiratory monitoring. A paper humidity sensing model was established and sensors under different design parameters were processed and tested, achieving high sensitivity of 5.45 kΩ/%RH and good repeatability with a matching rate of over 85.7%. Furthermore, the sensor patch with a dual-channel 3D structure was designed to distinguish between oral and nasal breathing from origin signals proved in the simulated breathing signal monitoring test. The sensor patch was applied in the sleep respiratory monitoring of a healthy volunteer and an obstruct sleep apnea patient, demonstrating its ability to distinguish between different respiratory patterns as well as various breathing modes.
| S-EPMC9503997 | biostudies-literature
Project description:IntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.MethodsThe training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.ResultsThe final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source.ConclusionThis novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.
| S-EPMC9306830 | biostudies-literature
Project description:Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
| S-EPMC8950004 | biostudies-literature