Remote Auscultation Research Articles

Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review.

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient-doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance.

Automatic wheeze segmentation using harmonic-percussive source separation and empirical mode decomposition.

Wheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a pressing need during recent years. Automatic respiratory sound analysis is required to reliably perform remote auscultation. In this work we propose a method for wheeze segmentation. Our method starts by decomposing a given audio excerpt into intrinsic mode frequencies using empirical mode decomposition. Then, we apply harmonic-percussive source separation to the resulting audio tracks and get harmonic-enhanced spectrograms, which are processed to obtain harmonic masks. Subsequently, a series of empirically derived rules are applied to find wheeze candidates. Finally, the candidates stemming from the different audio tracks are merged and median filtered. In the evaluation stage, we compare our method to three baselines on the ICBHI 2017 Respiratory Sound Database, a challenging dataset containing various noise sources and background sounds. Using the full dataset, our method outperforms the baselines, achieving an F1 of 41.9%. Our method's performance is also better than the baselines across several stratified results focusing on five variables: recording equipment, age, sex, body-mass index, and diagnosis. We conclude that, contrary to what has been reported in the literature, wheeze segmentation has not been solved for real life scenario applications. Adaptation of existing systems to demographic characteristics might be a promising step in the direction of algorithm personalization, which would make automatic wheeze segmentation methods clinically viable.

Body surface registration considering individual differences with non-rigid iterative closest point.

In telemedicine such as remote auscultation, patients themselves or non-medical people such as patient's parents need to place the stethoscope on their body surface in appropriate positions instead of the physicians. Meanwhile, as the position depends on the individual difference of body shape, there is a demand for the efficient navigation to place the medical equipment. In this paper, we have proposed a non-rigid iterative closest point (ICP)-based registration method for localizing the auscultation area considering the individual difference of body surface. The proposed system provides the listening position by applying the body surface registration between the patient and reference model with the specified auscultation area. Our novelty is that selecting the utilized reference model similar to the patient body among several types of the prepared reference model increases the registration accuracy. Simulation results showed that the registration error increases due to deviations of the body shape between the targeted models and reference model. Experimental results demonstrated that the proposed non-rigid ICP registration is capable of estimating the auscultation area with average error 5-19mm when selecting the most similar reference model. The statistical analysis showed high correlation between the registration accuracy and similarity of the utilized models. The proposed non-rigid ICP registration is a promising new method that provides accurate auscultation area takes into account the individual difference of body shape. Our hypothesis that the registration accuracy depends on the similarity of both body surfaces is validated through simulation study and human trial.

The utility of phonocardiograms in real-time remote cardiac auscultation using an internet-connected electronic stethoscope: Open-label randomized controlled pilot trial.

We have shown classical cardiac auscultation was superior to remote auscultation. We developed a phonocardiogram system to visualize sounds in remote auscultation. This study aimed to evaluate the effect of phonocardiograms on the diagnostic accuracy in remote auscultation using a cardiology patient simulator. In this open-label randomized controlled pilot trial, we randomly assigned physicians to the real-time remote auscultation group (control group) or the real-time remote auscultation with the phonocardiogram group (intervention group). Participants attended a training session in which they auscultated 15 sounds with the correct classification. After that, participants attended a test session where they had to classify 10 sounds. The control group auscultated the sounds remotely using an electronic stethoscope, an online medical program and a 4-K TV speaker without watching the TV screen. The intervention group performed auscultation like the control group but watched the phonocardiogram on the TV screen. The primary and secondary outcomes were the total test scores and each sound score, respectively. A total of 24 participants were included. The total test score in the intervention group (80/120, 66.7%) was higher than that in the control group (66/120, 55.0%), although the difference was statistically insignificant (P = .06). The correct answer rates of each sound were not different. Valvular/irregular rhythm sounds were not misclassified as normal sounds in the intervention group. Using a phonocardiogram improved the total correct answer rate by more than 10% in remote auscultation, although statistically insignificant. The phonocardiogram could help physicians screen valvular/irregular rhythm sounds from normal sounds. UMIN-CTR UMIN000045271; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051710.

Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial.

The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p > 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p < 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.

Towards fully automated robotic platform for remote auscultation.

Since most developed countries are facing an increase in the number of patients per healthcare worker due to a declining birth rate and an ageing population, relatively simple and safe diagnosis tasks may need to be performed using robotics and automation technologies, without specialists and hospitals. This study presents an automated robotic platform for remote auscultation, which is a highly cost-effective screening tool for detecting abnormal clinical signs. The developed robotic platform is composed of a 6-degree-of-freedom cooperative robotic arm, LiDAR camera, and a spring-based mechanism holding an electric stethoscope. The platform enables autonomous stethoscope positioning based on external body information acquired using the LiDAR camera-based multi-way registration; the platform also ensures safe and flexible contact, maintaining the contact force within a certain range through the passive-actuated mechanism. Our preliminary results confirm that the robotic platform enables estimation of the landing positions required for cardiac examinations based on the depth and landmark information of the body surface. It also handles the stethoscope while maintaining the contact force without relying on the push-in displacement by the robotic arm. The developed robotic platform enables the estimation of the landing positions and handling the stethoscope while maintaining the contact force, which promises the potential of automatic remote auscultation.

Unwrapping aortic valve dysfunction through complex network analysis: A biophysics approach

The development of sensitive and low-cost techniques for identifying valve dysfunction has become inevitable in the context of increasing death due to cardiac diseases. The present work attempts to propose a novel technique for cardiac auscultation based on graph theory. The sixty heart sound signals from normal heart (NMH) and with aortic stenosis (ASH) are subjected to Fast Fourier Transform (FFT) and complex network analyses. The murmur signals, a time-series signal, carry information about the blood flow through the heart, which gets exposed in the graph constructed and its features. The finer details of the murmur signal from the defective aortic valve and the normal aortic valve are reflected as the increased number of frequency components in FFT and as interconnected clusters without uncorrelated nodes in the graph of ASH. The distinction in graph features forms the basis of classification based on machine learning techniques (MLTs). When the unsupervised MLT-principal component analysis gives 86.8% total variance, the supervised MLTs-K nearest neighbor (KNN), support vector machine, and KNN subspace ensemble classifiers give 100%, 95.6%, and 90.9% prediction accuracy, suggesting its potential in remote auscultation in rural health centers.

Remote Auscultation of Heart and Lungs as an Acceptable Alternative to Legacy Measures in Quarantined COVID-19 Patients-Prospective Evaluation of 250 Examinations.

The COVID-19 pandemic accelerated the assimilation of telemedicine platforms into medical practice. Nevertheless, research-based evidence in this field is still accumulating. This was a prospective, cross-sectional comparative assessment of a remote physical examination device used mainly for heart and lung digital auscultation. We analyzed usage patterns, user (physician) subjective appreciation and compared it to legacy measures. Eighteen physicians (median age 36 years (IQR 32–45): two interns, seven residents and nine senior physicians; eleven internists, five geriatricians and two pediatricians) executed over 250 remote physical examinations. Their median work duration with quarantined patients was 60 days (IQR 45–60). The median number of patients examined by a single physician was 17 (IQR 10–34). Regarding overall estimation, all participants tended to prefer the remote examination in the setting of quarantined patients (median 6, IQR 3.75–8), while no statistically significant difference was demonstrated compared to the indifference value (p = 0.122). Internists preferred tele-medical examination over non-internists, with significant differences between groups regarding heart auscultation, (median 7, (IQR 3–7) vs. median 2, (IQR 1–5, respectively)), p = 0.044. In the setting of quarantined patients, from the physicians’ perspective, a digital platform for remote auscultation of heart and lungs was considered as an acceptable alternative to legacy measures.

Bioacoustic signal analysis through complex network features

The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.

Untangling the graph based features for lung sound auscultation

Auscultation is a historically significant and currently relevant method employed in medical diagnosis. The technological advancement joining hands with mathematical principles led to the development of sophisticated biomedical instruments. The paper proposes a novel graph-based auscultation method, incorporating the principles of machine learning techniques (MLTs), for catering the requirements of remote auscultation, which is highly relevant during the outbreak of the pandemic COVID-19. The proposed technique, delineated by analyzing the complex network constructed from the sixty bronchial and stridor breath sound signals (BS and SS), unveils the trachea and upper respiratory tract condition through the dynamics of airflow during the production of the signals. The time and frequency domain analyses of the time series signals unwraps the discontinuous and musical nature of BS and SS signal, respectively. The graph features extracted from the complex network, constructed using correlation mapping approach, throws light into the conditions of the trachea. The higher value of the number of edges, graph density and transitivity in BS, with low-frequency components and high correlation between the nodes, accounts for the airflow dynamics through the trachea of uniform and larger diameter. The lowering of these features and enhancement of degree centrality, closeness centrality and entropy in SS, with higher frequency components and low correlation between the nodes, reflects the pathological condition of constrictions in the trachea. The MLTs, principal component and linear discriminant analyses, could effectively classify and accurately predict the signals using the features of the graph, suggesting its potential in auscultation.

Graph-based complex network features for the auscultation of mitral incompetence

The paper unravels the potential of graph theory in the heart murmur auscultation by constructing a complex network from the single murmur time-series signals. In this study, forty-eight murmur signals of mitral incompetence (MI) and healthy heart (NM) are subjected to complex network and wavelet analyses. For the complex network analysis, the correlation coefficient is fixed as 0.8 and the time series is segmented based on the time delay obtained from the autocorrelation function. The signals are classified based on graph features, reflecting the haemodynamic through the mitral valve, using unsupervised principal component analysis and supervised k-nearest neighbour classifier. The appearance of many frequency components in the murmur due to MI is understood from the wavelet analysis. The graphs of NM and MI are found to show two well-defined clusters. When the graph of NM shows a large number of uncorrelated nodes due to the absence of signal in the systolic region that of MI shows interconnected nodes. The improper closing of the mitral valve and the regurgitation of blood in MI results in sound signals in the systolic region, responsible for the increased number of edges compared to NM. The present economical and sensitive graph-based method opens up the plausibility of remote auscultation in primary health centres in the context of the outbreak of pandemic COVID 19.

The Utility of Real-Time Remote Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial.

BackgroundThe urgent need for telemedicine has become clear in the COVID-19 pandemic. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown.ObjectiveThis study was conducted to assess the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using lung and cardiology patient simulators.MethodsThis was an open-label, randomized controlled trial including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. This study consisted of 2 parts: lung auscultation and cardiac auscultation. Each part contained a tutorial session and a test session. All participants attended a tutorial session, in which they listened to 15 sounds on the simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of 10 lung sounds and 10 cardiac sounds, depending on the study part. The intervention group listened to the sounds remotely using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each sound.ResultsIn total, 20 participants were included. There were no differences in age, sex, and years from graduation between the 2 groups in each part. The overall test score of lung auscultation in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%; P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%). The overall test score for cardiac auscultation in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%; P=.77). There was no cardiac sound for which the correct answer rate differed between groups.ConclusionsThe utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. This means that most of the real world’s essential cardiopulmonary sounds could be classified by a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope.Trial RegistrationUMIN-CTR UMIN000040828; https://tinyurl.com/r24j2p6s and UMIN-CTR UMIN000041601; https://tinyurl.com/bsax3j5f

Parallel source separation system for heart and lung sounds

In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on a non-negative matrix factorization (NMF) approach and a clustering strategy together with a soft-masking filtering. Furthermore, we propose an offline and online implementation of the framework which can be applied in many real-time scenarios, such as the extraction of clinical parameters, remote auscultation and breath sound analysis. Experimental results show that it is possible to achieve fast execution times, which enable a real-time behavior, combining parallel and high-performance techniques.

Artificial intelligence-assisted auscultation in detecting congenital heart disease.

Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create. Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children's Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age-2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts' face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts' face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84. The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts' face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.

Reducing the Ambulance Siren Noise for Distant Auscultation of the Lung Sound

Ambulance sirens sound very loud for transportation safety. However, loud sounds interfere with the auscultation of lung sounds. This study proposed an auscultation system that includes (1) an ACER Aspire 17 notebook as a server; (2) a smart mobile as a wireless hotspot (HwaWei Amazing A6); and (3) an ACER Aspire 5 notebook as a client. National Instruments data socket software gives read and write privileges to the IP addresses of the server and client. This real-world distant auscultation system works. The real-time adaptive filter reduced siren noise of 60 dB in power intensity. Surprisingly, a previous simulation of the adaptive filter had performed a noise reduction of 60 dB. Therefore, this real-time remote auscultation system is a reliable device for the ambulance service.

An open real-time tele-stethoscopy system

BackgroundAcute respiratory infections are the leading cause of childhood mortality. The lack of physicians in rural areas of developing countries makes difficult their correct diagnosis and treatment. The staff of rural health facilities (health-care technicians) may not be qualified to distinguish respiratory diseases by auscultation. For this reason, the goal of this project is the development of a tele-stethoscopy system that allows a physician to receive real-time cardio-respiratory sounds from a remote auscultation, as well as video images showing where the technician is placing the stethoscope on the patient’s body.MethodsA real-time wireless stethoscopy system was designed. The initial requirements were: 1) The system must send audio and video synchronously over IP networks, not requiring an Internet connection; 2) It must preserve the quality of cardiorespiratory sounds, allowing to adapt the binaural pieces and the chestpiece of standard stethoscopes, and; 3) Cardiorespiratory sounds should be recordable at both sides of the communication. In order to verify the diagnostic capacity of the system, a clinical validation with eight specialists has been designed. In a preliminary test, twelve patients have been auscultated by all the physicians using the tele-stethoscopy system, versus a local auscultation using traditional stethoscope. The system must allow listen the cardiac (systolic and diastolic murmurs, gallop sound, arrhythmias) and respiratory (rhonchi, rales and crepitations, wheeze, diminished and bronchial breath sounds, pleural friction rub) sounds.ResultsThe design, development and initial validation of the real-time wireless tele-stethoscopy system are described in detail. The system was conceived from scratch as open-source, low-cost and designed in such a way that many universities and small local companies in developing countries may manufacture it. Only free open-source software has been used in order to minimize manufacturing costs and look for alliances to support its improvement and adaptation. The microcontroller firmware code, the computer software code and the PCB schematics are available for free download in a subversion repository hosted in SourceForge.ConclusionsIt has been shown that real-time tele-stethoscopy, together with a videoconference system that allows a remote specialist to oversee the auscultation, may be a very helpful tool in rural areas of developing countries.

Implications for the design of a digital stethoscope for anaesthetic preadmission consultations

Background This research addresses the development of a digital stethoscope for use with a telehealth communications network to allow doctors to examine patients remotely (a digital telehealth stethoscope). A telehealth stethoscope would allow remote auscultation of patients who do not live near a major hospital. Travelling from remote areas to major hospitals is expensive for patients and a telehealth stethoscope could result in significant cost savings. Using a stethoscope requires great skill. To design a telehealth stethoscope that meets doctors’ expectations, the use of existing stethoscopes in clinical contexts must be examined. Method Observations were conducted of 30 anaesthetic preadmission consultations. The observations were video- taped. Interaction between doctor, patient and non-human elements in the consultation were “coded” to transform the video into data. The data were analysed to reveal essential aspects of the interactions. Results The analysis has shown that the doctor controls the interaction during auscultation. The conduct of auscultation draws heavily on the doctor’s tacit knowledge, allowing the doctor to treat the acoustic stethoscope as infrastructure – that is, the stethoscope sinks into the background and becomes completely transparent in use. Conclusion Two important, and related, implications for the design of a telehealth stethoscope have arisen from this research. First, as a telehealth stethoscope will be a shared device, doctors will not be able to make use of their existing expertise in using their own stethoscopes. Very simply, a telehealth stethoscope will sound different to a doctor’s own stethoscope. Second, the collaborative interaction required to use a telehealth stethoscope will have to be invented and refined. A telehealth stethoscope will need to be carefully designed to address these issues and result in successful use. This research challenges the concept of a telehealth stethoscope by raising questions about the ease and confidence with which doctors could use such a device.

Heart murmurs recorded by a sensor based electronic stethoscope and e-mailed for remote assessment

Background: Heart murmurs are common in children, and they are often referred to a specialist for examination. A clinically innocent murmur does not need further investigation. The referral area of...

A remote auscultation tool for advanced home health-care

A remote auscultation tool for advanced home health-care