Electronic Stethoscope Research Articles

DeepRespNet: A deep neural network for classification of respiratory sounds

Respiratory sounds convey significant information about the pulmonary status. This study proposes a deep learning-based framework to create an automatic, non-invasive, diagnostic method of categorizing pulmonary sounds. A labelled database of pulmonary sounds has been collected using an electronic stethoscope and audio recording instrument. Two deep learning architectures, 1D DeepRespNet and 2D DeepRespNet are proposed in this work that were trained and evaluated with normalised 1-D time series and 2-D spectrograms of acoustic signals of six types of lung sounds, respectively. The models were highly optimized to yield superior performance on the considered dataset. Experimental results demonstrate that the 2D DeepRespNet model trained with spectrogram-based representations yields higher accuracy of 95.2% on the test data as compared to the 1D DeepRespNet trained on the time-series data. The proposed model may be deployed on a single board computer or integrated into a smartphone to develop a standalone diagnostic tool to accurately and objectively classify abnormal lung sounds with low time complexity.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

IntroductionMitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for...

A Building Blocks Approach to Implementing a Telehealth Clinic Model to Improve Primary Care Access in the Philippines: A Large-scale Pilot Project

A limited number of healthcare workers and cost constraints limit primary care access in the Philippines. To test telehealth as a low-cost and effective solution, a large-scale digital health clinic was implemented in the selected urban and rural communities. In collaboration with local authorities, our pilot telemedicine clinic was implemented for 3 days in January 2023. Patients were registered and their vitals were assessed before they were seen by a remote United States-based licensed physician who provided recommendations for treatment. Medical devices with real-time streaming capabilities, such as electronic stethoscopes, were utilized to enhance remote examinations. A total of 322 patients were treated at the telehealth clinic, of which 218 completed a population health survey. A large proportion of patients had a doctor visit more than 12 months prior (39.2%) and had not received a dental examination (86.3%) or an eye examination within the past year (84.4%). Most patients had access to a smartphone (69.7%). A lower proportion had access to an internet connection that was sufficient for video calls (57.9%) or a laptop (8.5%). Our clinic demonstrated that telehealth is a feasible solution to improve primary care access for disadvantaged communities in the Philippines.

Construction and validation of a method for automated time label segmentation of heart sounds.

Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be imperceptible. Hence, obtaining heart sound information in an efficient and accurate manner will be helpful for the prediction and diagnosis of heart disease. To obtain heart sound information, we designed an audio data analysis tool to segment the heart sounds from single heart cycle, and validated the heart rate using a finger oxygen meter. The results from our validated technique could be used to realize heart sound segmentation. Our robust algorithmic platform was able to segment the heart sounds, which could then be compared in terms of their difference from the background. A combination of an electronic stethoscope and artificial intelligence technology was used for the digital collection of heart sounds and the intelligent identification of the first (S1) and second (S2) heart sounds. Our approach can provide an objective basis for the auscultation of heart sounds and visual display of heart sounds and murmurs.

Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs.

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disorders and a shortened lifespan. Despite its severity, the disease is commonly missed by owners or disregarded by veterinary practitioners. A key clinical sign of BOAS is stertor, a low-frequency snoring sound. In recent years, a functional grading scheme has been introduced to semi-objectively grade BOAS based on the presence of stertor and other abnormal signs. However, correctly grading stertor requires significant experience and adding an objective component would aid accuracy and repeatability. This study proposes a recurrent neural network model to automatically detect and grade stertor in laryngeal electronic stethoscope recordings. The model is developed using a novel dataset of 665 labelled recordings taken from 341 dogs with diverse BOAS clinical signs. Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.

Diagnosis of Respiratory Sounds using Deep Neural Networks

Abstract: Respiratory diseases are on the rise around the world and the impact of COVID-19 has highlighted the need for early and better diagnosis of respiratory diseases. The growing air pollution from vehicles, wildfires, coal-burning power plants and other natural and non-natural causes, particularly in the developing world is leading to more deaths due to respiratory problems leaving many in need of diagnosis and treatment. The use of machine learning for preliminary analysis and diagnosis of diseases is evolving rapidly, particularly in the area of analysis of medical images to help sort through and analyze hundreds of X-ray, CT-Scan or MRI images to highlight the area affected by a potential medical condition and suggest a possible diagnosis. This has allowed timely detection of potentially life-threatening diseases, reduced diagnosis time, improved efficiency and better coverage. These medical aids are also being integrated into the medical scanning equipment and related software to further improve the diagnostic process. This paper attempts to expand the use of deep learning as a method of assisting in the early diagnosis of respiratory diseases using audio recordings. The paper describes an approach for analyzing lung sounds captured using an electronic stethoscope from different parts of a patient’s chest wall. The paper describes the processes used to extract features from the sound signals, dataset preparation, neural network architectures evaluated and the prediction results.

Penggunaan Stetoskop Elektronik pada Kelainan Katup Jantung

This study aims to see the effectiveness of using an electronic stethoscope compared to a conventional one in patients with heart valve disorders. The method used in this writing is a systematic review through several databases, namely Pubmed, ClinicalKey, Proquest, Science Direct, and Scopus. The article analysis process uses the PRISMA method. The research results show that electronic stethoscopes are more expensive because many parts and applications/software are added. Still, they are very efficient in accurately diagnosing specific heart sound abnormalities and have more advantages than conventional stethoscopes. In conclusion, electronic stethoscopes are very effective and efficient in increasing the speed and accuracy of diagnosing specific heart sound abnormalities, so they have more benefits.
 Keywords: Heart Valves, Electronic Stethoscope

Automated differential diagnostics of respiratory diseases using an electronic stethoscope

Abstract Introduction: The outbreak of the coronavirus infection, which has escalated into a pandemic, has worsened the already unfavourable situation with respiratory system diseases in Ukraine. The burden on doctors has significantly increased, necessitating the exploration of simplified and expedited methods for conducting routine respiratory examinations. The research aims to describe a model for creating an automated differential diagnosis of respiratory noise using an electronic stethoscope, combining medical and clinical information about the types of respiratory noise characterizing the normal or pathological state of the respiratory system with a means of its information and technical processing. Material and methods: The research methods were analysis of theoretical information about the types of respiratory noise, analysis of technical information for choosing an information technology tool for processing biological signals; synthesis of the results; modelling. Results: The research resulted in a model of automated differential diagnosis based on the principle of auscultation, which includes the process of extracting the sound of air movement inside and outside the lungs and the classification of the extracted sounds. Automation of this process concerned only the classification of the extracted sounds since the principle of extraction itself was the same for both mechanical and automatic implementations. Conclusions: The automatic classification process was intended to reduce the time of the procedure and reduce the influence of the human factor, eliminating the possibility of medical error. To implement the process, a deep machine learning method was used, the array of information for which was to be a created phonotheque of acoustic signals of the respiratory system, which would include all types of respiratory noise concerning normal or pathological processes in the body.

BrachySound: machine learning based assessment of respiratory sounds in dogs

The early and accurate diagnosis of brachycephalic obstructive airway syndrome (BOAS) in dogs is pivotal for effective treatment and enhanced canine well-being. Owners often do underestimate the severity of BOAS in their dogs. In addition, traditional diagnostic methods, which include pharyngolaryngeal auscultation, are often compromised by subjectivity, are time-intensive and depend on the veterinary surgeon’s experience. Hence, new fast, reliable assessment methods for BOAS are required. The aim of the current study was to use machine learning techniques to bridge this scientific gap. In this study, machine learning models were employed to objectively analyze 366 audio samples from 69 Pugs and 79 other brachycephalic breeds, recorded with an electronic stethoscope during a 15-min standardized exercise test. In classifying the BOAS test results as to whether the dog is affected or not, our models achieved a peak accuracy of 0.85, using subsets from the Pugs dataset. For predictions of the BOAS results from recordings at rest in Pugs and various brachycephalic breeds, accuracies of 0.68 and 0.65 were observed, respectively. Notably, the detection of laryngeal sounds achieved an F1 score of 0.80. These results highlight the potential of machine learning models to significantly streamline the examination process, offering a more objective assessment than traditional methods. This research indicates a turning point towards a data-driven, objective, and efficient approach in canine health assessment, fostering standardized and objective BOAS diagnostics.

HEART SOUND NOISE SEPARATION FROM LUNG SOUND BASED ON ENHANCED VARIATIONAL MODE DECOMPOSITION FOR DIAGNOSING PULMONARY DISEASES

Lung sound (LS) signals are vital for diagnosing pulmonary disorders. However, heart sound (HS) interferes with the analysis of LS, leading to the misdiagnosis of lung disorders. To address this issue, we propose an Enhanced Variational Mode Decomposition (E-VMD) technique to remove HS interference from LSs effectively. The E-VMD method automatically determines the mode number for signal decomposition based on the characteristics of variational mode functions (VMFs) such as normalized permutation entropy, kurtosis index, extreme frequency domain, and energy loss coefficient. The performance of the proposed denoising technique was evaluated using six performance metrics: Signal-to-noise ratio (SNR), root mean square error (RMSE), normalized mean absolute error (nMAE), correlation coefficient factor (CCF), CPU[Formula: see text], and CPU[Formula: see text]. In comparison to other denoising methods such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), singular spectrum analysis (SSA), and variational mode decomposition (VMD), the new E-VMD method demonstrates superior denoising outcome. The proposed method was evaluated using LS recorded from the outpatient department of Thoracic Medicine at Thanjavur Medical College and Hospital, Thanjavur. The obtained performance measures are as follows: RMSE: 0.02103 ± 0.00054, SNR: 28.52464 ± 0.00253, nMAE: 0.00009 ± 0.00056, CCF: 0.9962, CPU[Formula: see text]: 34.586, and CPU[Formula: see text]: 0.452 s. These results affirm the adaptability and robustness of the proposed method, even in the existence of HS noise. This method improves denoising accuracy and computational efficacy, making it a useful tool for improving the analysis of LS signals and assisting in medical diagnostics. This technique utilizes an electronic stethoscope, a common clinical device used by healthcare professionals for detecting lung disease.

Exploring Microphone Technologies for Digital Auscultation Devices.

The aim of this work is to present a preliminary study for the design of a digital auscultation system, i.e., a novel wearable device for patient chest auscultation and a digital stethoscope. The development and testing of the electronic stethoscope prototype is reported with an emphasis on the description and selection of sound transduction systems and analog electronic processing. The focus on various microphone technologies, such as micro-electro-mechanical systems (MEMSs), electret condensers, and piezoelectronic diaphragms, intends to emphasize the most suitable transducer for auscultation. In addition, we report on the design and development of a digital acquisition system for the human body for sound recording by using a modular device approach in order to fit the chosen analog and digital mics. Tests were performed on a designed phantom setup, and a qualitative comparison between the sounds recorded with the newly developed acquisition device and those recorded with two commercial digital stethoscopes is reported.

Research on electronic stethoscope system and signal processing algorithm

Stethoscopes have an important role in non-invasive diagnosis of cardiovascular and respiratory diseases, digestive diseases, and other kinds of diseases. The emergence of high-end diagnostic devices and new diagnostic methods have caused the status of the stethoscope to decline. However, stethoscope has the advantages of simple operation, mature auscultation theory and low cost, and thus is still widely used in medical diagnosis. This paper first introduces the design and application of electronic stethoscope solutions based on contact sensors and air coupling sensors, and then introduces advanced algorithms for digital signal processing for the diagnosis and treatment of different diseases, including heart sound noise reduction algorithm, heart sound segmentation algorithm and heart sound feature extraction and recognition algorithm. Finally, this paper summarizes the application of the electronic stethoscope system in medical testing, and its future development direction. In summary, the electronic stethoscope system is a reliable medical testing tool, which can convert sound signals into digital signals through complex signal processing algorithms for more accurate detection of human physiological parameters. The research of this paper will be of great value to the research and application of electronic stethoscopes.

Assessment of Hemodialysis Arteriovenous Shunt (AV Shunt) Sounds by Using a Novel Electronic Stethoscope and Machine Learning Techniques

Assessment of Hemodialysis Arteriovenous Shunt (AV Shunt) Sounds by Using a Novel Electronic Stethoscope and Machine Learning Techniques

Swallowing sound index analysis using electronic stethoscope and artificial intelligence for patients with Parkinson's disease

Background and purposeSeveral noninvasive tools assess swallowing disorders, including electronic stethoscope artificial intelligence (AI) analysis for remote diagnosis, with the potential for telemedicine. This study investigated the swallowing sound index in patients with Parkinson's disease (PD). MethodsThis single-arm, open-label trial assessed the impact of cervical percutaneous interferential current stimulation on swallowing in patients with PD classified as Hoehn–Yahr stages 2–4. Stimulation was conducted for 8 weeks. Baseline data were used to examine the link between the swallowing sound index and indicators such as videofluoroscopy (VF). Furthermore, we examined changes in the swallowing sound index after the intervention. ResultsTwenty-five patients were included. The swallowing sound index in patients with PD was higher than that in those with amyotrophic lateral sclerosis but considerably lower than that in healthy controls. The number of patients with normal EAT-10 scores positively correlated with the swallowing sound index, whereas elevated C-reactive protein levels were negatively correlated with the swallowing sound index. However, the index displayed no correlation with other indicators, including the VF results. Despite the intervention, the index remained unchanged throughout the study. ConclusionIn patients with PD, a decrease in the swallowing sound index suggests a potential association between swallowing disorders and the risk of aspiration pneumonia.Trial Registration number: jRCTs062220013.

Analysis of respiratory sounds based on electronic stethoscope and its application in pediatric respiratory diseases: current status and future prospects

Analysis of respiratory sounds based on electronic stethoscope and its application in pediatric respiratory diseases: current status and future prospects

Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2

Auscultation, a traditional clinical examination method using a stethoscope to quickly assess airway abnormalities, remains valuable due to its real-time, non-invasive, and easy-to-perform nature. Recent advancements in computerized respiratory sound analysis (CRSA) have provided a quantifiable approach for recording, editing, and comparing respiratory sounds, also enabling the training of artificial intelligence models to fully excavate the potential of auscultation. However, existing sound analysis models often require complex computations, leading to prolonged processing times and high calculation and memory requirements. Moreover, the limited diversity and scope of available databases limits reproducibility and robustness, mainly relying on small sample datasets primarily collected from Caucasians. In order to overcome these limitations, we developed a new Chinese adult respiratory sound database, LD-DF RSdb, using an electronic stethoscope and mobile phone. By enrolling 145 participants, 9,584 high quality recordings were collected, containing 6,435 normal sounds, 2,782 crackles, 208 wheezes, and 159 combined sounds. Subsequently, we utilized a lightweight neural network architecture, MobileNetV2, for automated categorization of the four types of respiratory sounds, achieving an appreciable overall performance with an AUC of 0.8923. This study demonstrates the feasibility and potential of using mobile phones, electronic stethoscopes, and MobileNetV2 in CRSA. The proposed method offers a convenient and promising approach to enhance overall respiratory disease management and may help address healthcare resource disparities.

Comparison of simultaneous auscultation and ultrasound for clinical assessment of bowel peristalsis in neonates.

Assessment of bowel health in ill preterm infants is essential to prevent and diagnose early potentially life-threatening intestinal conditions such as necrotizing enterocolitis. Auscultation of bowel sounds helps assess peristalsis and is an essential component of this assessment. We aim to compare conventional bowel sound auscultation using acoustic recordings from an electronic stethoscope to real-time bowel motility visualized on point-of-care bowel ultrasound (US) in neonates with no known bowel disease. This is a prospective observational cohort study in neonates on full enteral feeds with no known bowel disease. A 3M™ Littmann® Model 3200 electronic stethoscope was used to obtain a continuous 60-s recording of bowel sounds at a set region over the abdomen, with a concurrent recording of US using a 12l high-frequency Linear probe. The bowel sounds heard by the first investigator using the stethoscope were contemporaneously transferred for a computerized assessment of their electronic waveforms. The second investigator, blinded to the auscultation findings, obtained bowel US images using a 12l Linear US probe. All recordings were analyzed for bowel peristalsis (duration in seconds) by each of the two methods. We recruited 30 neonates (gestational age range 27-43 weeks) on full enteral feeds with no known bowel disease. The detection of bowel peristalsis (duration in seconds) by both methods (acoustic and US) was reported as a percentage of the total recording time for each participant. Comparing the time segments of bowel sound detection by digital stethoscope recording to that of the visual detection of bowel movements in US revealed a median time of peristalsis with US of 58%, compared to 88.3% with acoustic assessment (p < 0.002). The median regression difference was 26.7% [95% confidence interval (CI) 5%-48%], demonstrating no correlation between the two methods. Our study demonstrates disconcordance between the detection of bowel sounds by auscultation and the detection of bowel motility in real time using US in neonates on full enteral feeds and with no known bowel disease. Better innovative methods using artificial intelligence to characterize bowel sounds, integrating acoustic mapping with sonographic detection of bowel peristalsis, will allow us to develop continuous neonatal bowel sound monitoring devices.

Development of an electronic stethoscope implementing filters with OPAM for the observation of cardiac signals using a graphical interface with an AD8232 module

Development of an electronic stethoscope implementing filters with OPAM for the observation of cardiac signals using a graphical interface with an AD8232 module

Electronic Auscultation System: a Method for Measuring the Amplitude-Frequency Characteristics of Stethoscopes

The purpose of research is increasing the diversity of designs of electronic auscultation systems with measured characteristics and testing. A series of articles includes the development of a model of an electronic auscultation system, the development of an electronic stethoscope design, the manufacture of an experimental sample, the development of a method for measuring the amplitude-frequency characteristics of electronic and classical stethoscopes, testing the proposed models and methods, and analyzing auscultatory data. The article proposes a method for measuring the frequency response of classical and electronic stethoscopes. The proposed method was tested. The frequency response of classical and electronic stethoscopes was measured. The design of the head of the electronic stethoscope has been improved, the frequency response of the original and improved designs has been compared. Methods. The studies were based on methods for measuring the frequency characteristics of electroacoustic transducers proposed in interstate standards. On the basis of methods in interstate standards, a simplified method for measuring the amplitude-frequency characteristics of electronic stethoscopes has been developed. The following equipment was used in the study: Type 4227 "Artificial Mouth" emitter from Bruel &amp; Kjaer; measuring condenser microphone "Artificial ear" Type 4146 with a 2CC reference chamber from Bruel &amp; Kjaer; Type 2118 analyzer from Bruel &amp; Kjaer. Results. The amplitude-frequency characteristics of classical and electronic stethoscopes are obtained. The design of the electronic stethoscope head has been improved, the amplitude-frequency characteristics of the original and improved designs have been compared. Conclusion. A method for measuring the parameters of the frequency characteristics of the sound pressure level of classical and electronic stethoscopes is proposed. The method is as close as possible to the standardized methods for monitoring acoustic parameters, while being sufficiently simple and cheap compared to the pressure measurement method, demonstrating the qualitative similarity of the measurement results.

Swallowing sound evaluation using an electronic stethoscope and artificial intelligence analysis for patients with amyotrophic lateral sclerosis.

Non-invasive, simple, and repetitive swallowing evaluation is required to prevent aspiration pneumonia in neurological care. We investigated the usefulness of swallowing sound evaluation in patients with amyotrophic lateral sclerosis (ALS) using our new electronic stethoscope artificial intelligence (AI) analysis tool. We studied patients with ALS who provided written informed consent. We used an electronic stethoscope, placed a Bluetooth-enabled electronic stethoscope on the upper end of the sternum, performed a 3-mL water swallow three times, and remotely identified the intermittent sound components of the water flow caused at that time by AI, with the maximum value as the swallowing sound index. We examined the correlation between the swallowing sound index and patient background, including swallowing-related parameters. We evaluated 24 patients with ALS (age 64.0 ± 11.8 years, 13 women, median duration of illness 17.5 months). The median ALS Functional Rating Scale-Revised (ALSFRS-R) score was 41 (minimum 18, maximum 47). In all cases, the mean swallowing sound index was 0.209 ± 0.088. A multivariate analysis showed that a decrease in the swallowing sound index was significantly associated with a low ALSFRS-R score, an ALSFRS-R bulbar symptom score, % vital capacity, tongue pressure, a Mann Assessment of Swallowing Ability (MASA) score, and a MASA pharyngeal phase-related score. Swallowing sound evaluation using an electronic stethoscope AI analysis showed a correlation with existing indicators in swallowing evaluation in ALS and suggested its usefulness as a new method. This is expected to be a useful examination method in home and remote medical care.