Respiratory Sound Analysis for the Evidence of Lung Health

Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds

Pulmonary Auscultation using Mobile Devices - Feasibility Study in Respiratory Diseases

A auscultação pulmonar convencional é essencial no controlo das doenças respiratórias. Contudo, a deteção de sons adventícios fora do ambiente hospitalar continua a ser um desafio. Nós estudámos a exequibilidade de realizar auscultação com o microfone incorporado de um smartphone em contexto clínico. Noventa e cinco pacientes (mediana[intervalo interquartil] 16[11-24] anos; 52% mulheres; 42 fibrose quística, 24 asma, 17 outras doenças respiratórias e 12 sem doença respiratória) foram recrutados nos serviços de Pediatria e Pneumologia de um hospital terciário. Os clínicos realizaram auscultação convencional em 4 locais (traqueia, peito anterior direito e bases pulmonares direita e esquerda), documentando quaisquer sons adventícios. A auscultação com o smartphone foi gravada nos mesmos locais. As gravações (n=738) foram classificadas por dois investigadores e o acordo calculado (%; kappa de Cohen(IC95%)). Foram obtidas gravações com qualidade em 88% dos participantes e 69% das gravações (91%; k=0.80(IC95% 0.75-0.85)), com uma proporção de qualidade superior na traqueia (79%) e inferior no grupo da asma (52%). Foram encontrados sons adventícios em apenas 27% dos participantes e 12% das gravações (91%; k=0.57(IC95% 0.46-0.68)), o que poderá ter contribuído para o acordo razoável entre a auscultação convencional e a auscultação com o smartphone (86%; k=0.25(IC95% 0.13-0.37)). Os nossos resultados demonstram que a auscultação com o smartphone foi exequível, mas que é necessária mais investigação para melhorar o seu acordo com a auscultação convencional.Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone's embedded microphone in real-world clinical practice. Ninety-five patients (median[interquartile range] 16[11-24]y; 52% female; 42 cystic fibrosis, 24 asthma, 17 other respiratory diseases and 12 no respiratory diseases) were re-cruited at Pediatrics and Pulmonology departments of a tertiary hospital. Clinicians performed conventional auscultation at 4 locations (trachea, right anterior chest, right and left lung bases), documenting any adventitious sounds. Smartphone auscultation was recorded in the same loca-tions. The recordings (n=738) were classified by two annotators and agreement calculated (%; Cohen's k(95%CI)). Recordings with quality were obtained in 88% of the participants and 69% of the recordings (91%; k=0.80(95%CI 0.75-0.85)), with the quality proportion being higher at the trachea (79%) and lower in the asthma group (52%). Adventitious sounds were present in only 27% of the participants and 12% of the recordings (91%; k=0.57(95%CI 0.46-0.68)), which may have contributed to the fair agreement between conventional and smartphone auscultation (86%; k=0.25(95%CI 0.13-0.37)). Our results show that smartphone auscultation was feasible, but further investigation is required to improve its agreement with conventional auscultation

ILSA 2017 in Tromsø : proceedings from the 42nd annual conference of the International Lung Sound Association

Edited by Hasse Medbye, med bidrag fra flere.<brThe usefulness of lung auscultation is changing. It depends on how well practitioners understand the generation of sounds. It also depends on their knowledge on how lung sounds are associated with lung and heart diseases, as well as with other factors such as ageing and smoking habits. In clinical practice, practitioners need to give sufficient attention to lung auscultation, and they should use the same terminology, or at least understand each other’s use of terms. Technological innovations lead to an extended use of lung auscultation. Continuous monitoring of lung sounds is now possible, and computers can extract more information from the complex lung sounds than human hearing is capable of. Learning how to carry out lung auscultation and to interpret the sounds are essential skills in the education of doctors and other health professionals. Thus, new computer based learning tools for the study of recorded sounds will be helpful. In this conference there will be focus on all these determinants for efficient lung auscultation. In addition to free oral presentations, we have three symposia: on computerized analysis based on machine learning, on diagnostics, and on learning lung sounds, including the psychology of hearing. The symposia include extended presentations from invited speakers. The 42nd conference is the first in history arranged by a research unit for general practice. Primary care doctors are probably the group of health professionals that put the greatest emphasis on lung auscultation in their clinical work. Many patients with chest symptoms consult without a known diagnosis, and several studies have shown that general practitioners pay attention to crackles and wheezes when making decisions, for instance when antibiotics are prescribed to coughing patients. In hospital, the diagnosis of lung diseases is more strongly influenced by technologies such as radiography and blood gas analysis. Since lung auscultation holds a strong position in the work of primary care doctors, I think it is just timely, that the 42nd ILSA conference is hosted by General Practice Research Unit in Tromsø. I hope all participants will find presentations of importance, and that the stay in Tromsø will be enjoyable

Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study

Introduction: WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries. Methods: This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children\u27s hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis. Discussion: This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte

IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS

Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations. Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds. The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme. Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact