Respiratory sound analysis as a diagnosis tool for breathing disorders

This paper provides an overview of respiratory sound analysis (RSA) and its functionality as a diagnostic tool for breathing disorders. A number of respiratory conditions and the techniques used to diagnose them, including sleep apnoea, lung sound analysis (LSA), wheeze detection and phase estimation are discussed. The technologies used, from multi-channel bespoke recording systems to using a smart phone application are explained. A new study that focusses on developing a non-invasive tool for the detection and characterisation of inducible laryngeal obstruction (ILO) is presented. ILO is a debilitating condition, caused by malfunctioning structures of the upper airway, commonly triggered by exertion, leaving children feeling out of breath and unable to exercise normally. In rare cases it can lead to critical laryngeal obstruction and admission to intensive care for endotracheal intubation. The current definitive method of diagnosis is by inserting a camera through the nose while the person is exercising. This approach is invasive, uncomfortable (in particular for young children) subjective and relies on the consultant's expertise. There are only a handful of consultants with the appropriate level of expertise in the UK to diagnose this condition

Recommendations Related To Wheeze Sound Data Acquisition

In the field of computerized respiratory sounds,a reliable data set with a sufficient number of subjects is required for the development of wheeze detection algorithm or for further analysis.Validated and accurate data is a critical issue in the field of research.In this study,the protocol related to wheeze sound data acquisition is discussed.Previously,most articles focused on wheeze detection or its parametric analysis,but no consideration was given to data acquisition.Second major purpose of this study is to exhibit particulars of our dataset which was attained for future analysis.We compile a database with a sufficient and reliable number of cases with all essential details,in contrast to commercially available wheeze sound data used for research,freely available online data on websites and data used to train medical students for auscultation

Computerized respiratory sounds in paediatrics: a systematic review

Background Diagnosing and monitoring of children with respiratory disorders is often challenging. Respiratory sounds (RS) are simple, non-invasive and universally available measures that are directly related to movement of air, within the tracheobronchial tree. Thus, RS may be valuable indicators of respiratory health, their characteristics in the paediatric population are scattered in the literature and not systematized. Aim Systematically review the different acoustic RS properties in healthy children and in children with different respiratory disorders. Methods: MEDLINE, EMBASE, AMED and CINHAL databases were searched on Sept 2020. One author extracted data and two independently assessed the quality of the articles using the National Heart Lung and Blood Institute quality assessment tool. Results Twenty-eight studies were included with a total 2032 participants (44% with a respiratory condition, such as asthma, bronchiolitis, cystic fibrosis, presence of wheezing and non-specified low respiratory tract infections). A high heterogeneity in the procedures, outcomes and outcome measures used was found. Healthy participants showed lower values of F50 (from 194 ± 26 to 521 ± 18Hz) than those with asthma (from 140 ± 8 to 769 ± 85Hz) or bronchiolitis (from 100 to 80Hz). F50 tend to increase with provocation tests (136 ± 9 to 909 ± 81Hz) and decrease with treatments (128 ± 6 to 781 ± 57Hz). Wheeze rates ranged from 0 to 24.7 ± 25% on asthmatic participants. Crackles findings ranged from 6% on people with recurrent wheezing to 30.8% in middle lobe atelectasis. Conclusion RS show different acoustic properties in healthy children vs with different respiratory disorders and thus may be useful in the diagnostic and monitoring on paediatrics.publishe

Distinguishing Between Asthma and Pneumonia Through Automated Lung Sound Analysis

This project attempts to distinguish between two pulmonary disorders, asthma and pneumonia, using automated analysis of lung sounds. Such an approach minimizes the subjectivity of diagnosis inherent to current practices by physicians. Breath sounds are recorded by a physiological microphone and hardware acquisition system, and then analyzed in software using a two stage algorithm. The first stage detects abnormal lung sounds and second stage makes a diagnosis. A clinical trial was conducted at a pediatric clinic to validate the system

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit