A Low-Cost AI-Empowered Stethoscope and a Lightweight Model for Detecting Cardiac and Respiratory Diseases from Lung and Heart Auscultation Sounds.

Cardiac and respiratory diseases are the primary causes of health problems. If we can automate anomalous heart and lung sound diagnosis, we can improve the early detection of disease and enable the screening of a wider population than possible with manual screening. We propose a lightweight yet powerful model for simultaneous lung and heart sound diagnosis, which is deployable in an embedded low-cost device and is valuable in remote areas or developing countries where Internet access may not be available. We trained and tested the proposed model with the ICBHI and the Yaseen datasets. The experimental results showed that our 11-class prediction model could achieve 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and 99.72% F1 score. We designed a digital stethoscope (around USD 5) and connected it to a low-cost, single-board-computer Raspberry Pi Zero 2W (around USD 20), on which our pretrained model can be smoothly run. This AI-empowered digital stethoscope is beneficial for anyone in the medical field, as it can automatically provide diagnostic results and produce digital audio records for further analysis.

StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

Use of Digital Stethoscope to Measure Heart Rate in Birds: Comparison of Different Counting Methods Using Phonocardiograms.

The high cardiac contractility of birds poses a challenge to traditional cardiac auscultation, particularly for the accurate determination of heart rate (HR). The objectives of this study were to 1) evaluate the feasibility of using phonocardiograms of adequate length and quality to assess HR in different avian species with a commercially available digital stethoscope, 2) compare 5 counting methods, including 2 direct reading methods (manual counting and using a semiautomatic computerized algorithm as a reference method) and 3 listening methods (progressive mental counting, counting by 10s, and counting with a smartphone application tap counter), and 3) obtain the HR in selected birds and identify a correlation between body weight and HR in different avian species. An inverse correlation on a logarithmic scale was identified between the mean body weight and HR in 60 different bird species (n = 211; R = -0.72, P < 0.0001). Manual reading of phonocardiograms was the most reliable method and had the highest agreement with the reference method; this was followed by the counting by 10s method, the tapping method, and the progressive mental counting method, which was the least reliable. The agreement levels for the different methods were comparable for HRs <200 beats per minute (bpm) in birds weighing >1 kg. For HRs >500 bpm in birds weighing <150 g, only the reading method maintained a good agreement level. A digital stethoscope can be a useful tool for accurately determining the HR in birds, including very small species with high HRs.

The coming era of a new auscultation system for analyzing respiratory sounds.

Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.

Tools to assess lung aeration in neonates with respiratory distress syndrome.

AIM: Respiratory distress syndrome is a common condition among preterm neonates, and assessing lung aeration assists in diagnosing the disease and helping to guide and monitor treatment. We aimed to identify and analyse the tools available to assess lung aeration in neonates with respiratory distress syndrome. METHODS: A systematic review and narrative synthesis of studies published between January 1, 2004, and August 26, 2019, were performed using the OVID Medline, PubMed, Embase and Scopus databases. RESULTS: A total of 53 relevant papers were retrieved for the narrative synthesis. The main tools used to assess lung aeration were respiratory function monitoring, capnography, chest X-rays, lung ultrasound, electrical impedance tomography and respiratory inductive plethysmography. This paper discusses the evidence to support the use of these tools, including their advantages and disadvantages, and explores the future of lung aeration assessments within neonatal intensive care units. CONCLUSION: There are currently several promising tools available to assess lung aeration in neonates with respiratory distress syndrome, but they all have their limitations. These tools need to be refined to facilitate convenient and accurate assessments of lung aeration in neonates with respiratory distress syndrome.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring.

One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

Evaluation of a digital stethoscope for electrocardiographic recording in donkeys: Preliminary results.

The digital stethoscope (DS) is a cost-effective single-lead digital stethoscope that allows simultaneous electrocardiographic (ECG) and phonocardiographic recordings on a smartphone. Despite its application in small animals and horses, there are currently no studies on its use in donkeys. The aim of this study was to evaluate the use of a new smartphone-based DS device in recording ECG tracings in donkeys. Standard base-apex lead ECG (sECG) and single-lead DS ECG (dECG) were simultaneously recorded for at least 30 s. Both sECG and dECG tracings were analysed by the same operator, recording heart rate, ECG waves and intervals, and the presence and duration of artefacts. Thirty-seven donkeys were included. The dECG tracings were interpretable in all the animals (100 %). The results showed perfect agreement between the sECG and dECG data for the classification of heart rhythm and P-wave polarity. Strong agreement was found in the evaluation of heart rate calculated manually and automatically by the smartphone app, QRS complex polarity, T wave polarity, and duration of the PR interval. However, no agreement was found in the evaluation of P wave duration, QRS complex duration and amplitude, and T wave duration and amplitude. In conclusion, although this is only a preliminary study, the DS was a valid, practical, and easy to use electrocardiographic tool for recording good-quality ECG tracings to assess the ECGs of donkeys in the field.

Newborn heart rate monitoring methods at birth and clinical outcomes: A systematic review.

Aim: Compare heart rate assessment methods in the delivery room on newborn clinical outcomes. Methods: A search of Medline, SCOPUS, CINAHL and Cochrane was conducted between January 1, 1946, to until August 16, 2023. (CRD 42021283438) Study Selection was based on predetermined criteria. Reviewers independently extracted data, appraised risk of bias and assessed certainty of evidence. Results: Two randomized controlled trials involving 91 newborns and 1 nonrandomized study involving 632 newborns comparing electrocardiogram (ECG) to auscultation plus pulse oximetry were included. No studies were found that compared any other heart rate measurement methods and reported clinical outcomes. There was no difference between the ECG and control group for duration of positive pressure ventilation, time to heart rate ≥ 100 beats per minute, epinephrine use or death before discharge. In the randomized studies, there was no difference in rate of tracheal intubation [RR 1.34, 95% CI (0.69-2.59)]. No participants received chest compressions. In the nonrandomized study, fewer infants were intubated in the ECG group [RR 0.75, 95% CI (0.62-0.90)]; however, for chest compressions, benefit or harm could not be excluded. [RR 2.14, 95% (CI 0.98-4.70)]. Conclusion: There is insufficient evidence to ascertain clinical benefits or harms associated with the use of ECG versus pulse oximetry plus auscultation for heart rate assessment in newborns in the delivery room.

Fast and accurate newborn heart rate monitoring at birth: A systematic review.

Aim: To examine speed and accuracy of newborn heart rate measurement by various assessment methods employed at birth. Methods: A search of Medline, SCOPUS, CINAHL and Cochrane was conducted between January 1, 1946, to until August 16, 2023. (CRD 42021283364) Study selection was based on predetermined criteria. Reviewers independently extracted data, appraised risk of bias and assessed certainty of evidence. Results: Pulse oximetry is slower and less precise than ECG for heart rate assessment. Both auscultation and palpation are imprecise for heart rate assessment. Other devices such as digital stethoscope, Doppler ultrasound, an ECG device using dry electrodes incorporated in a belt, photoplethysmography and electromyography are studied in small numbers of newborns and data are not available for extremely preterm or bradycardic newborns receiving resuscitation. Digital stethoscope is fast and accurate. Doppler ultrasound and dry electrode ECG in a belt are fast, accurate and precise when compared to conventional ECG with gel adhesive electrodes. Limitations: Certainty of evidence was low or very low for most comparisons. Conclusion: If resources permit, ECG should be used for fast and accurate heart rate assessment at birth. Pulse oximetry and auscultation may be reasonable alternatives but have limitations. Digital stethoscope, doppler ultrasound and dry electrode ECG show promise but need further study.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation.

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Evaluation of a new smartphone-based digital stethoscope featuring phonocardiography and electrocardiography in dogs and cats.

This study assessed a new smartphone-based digital stethoscope (DS) featuring simultaneous phonocardiographic and one-lead electrocardiogram (ECG) recording in dogs and cats. The audio files and ECG traces obtained by the device were compared with conventional auscultation and standard ECG. A total of 99 dogs and nine cats were prospectively included. All cases underwent conventional auscultation using an acoustic stethoscope, standard six-lead ECG, standard echocardiography and recordings with the DS. All the audio recordings, phonocardiographic files and ECG traces were then blind reviewed by an expert operator. The agreement between methods was assessed using Cohen's kappa and the Bland-Altman test. Audio recordings were considered interpretable in 90% animals. Substantial agreement was found in the diagnosis of heart murmur (κ = 0.691) and gallop sound (k = 0.740). In nine animals with an echocardiographic diagnosis of heart disease, only the DS detected a heart murmur or gallop sound. ECG traces recorded with the new device were deemed interpretable in 88 % animals. Diagnosis of heart rhythm showed moderate agreement in the identification of atrial fibrillation (k = 0.596). The detection of ventricular premature complexes and bundle branch blocks revealed an almost perfect agreement (k = 1). Overall, the DS showed a good diagnostic accuracy in detecting heart murmurs, gallop sounds, ventricular premature complexes and bundle branch blocks. A clinically relevant overdiagnosis of atrial fibrillation was found but without evidence of false negatives. The DS could represent a useful screening tool for heart sound abnormalities and cardiac arrhythmias..

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.

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds.

In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN-LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN-LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification.

A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm.

Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.

Digital devices for teaching cardiac auscultation - a randomized pilot study.

BACKGROUND: Competent cardiac auscultation is a declining skill. Digital stethoscopes and hand-held echocardiography (HHE) are modern devices which may improve the accuracy of heart murmur recognition and diagnosis. Their incremental value compared to conventional examination has not been evaluated in depth. OBJECTIVES: Our aim was to quantify the utility of digital stethoscopes and HHE as teaching aids to improve medical students' diagnostic accuracy in the evaluation of heart murmurs using a novel clinically weighted scoring system. DESIGN: This pilot study involved eight medical students and eight patients with heart murmurs. Four patients were examined at 2 sessions, 1 week apart. Medical students were randomised into two groups: the 'intervention group' examined patients with a standard and digital stethoscope, and then received demonstration of the valvular lesion with HHE to illustrate the diagnosis. The 'control group' used a standard stethoscope only and were taught using traditional methods. Students' scores were compared to a 'gold standard' derived from a consensus of auscultation findings of three cardiologists. RESULTS: Overall the mean percent correct of total possible score was 65.4% (SD8.4). Using a mixed models ANOVA approach to repeated measures, the mean [95% CI] increase from training to validation period for the control group was 2.5% [-11.5, 16.5] P(Tukey) = 0.95 and 15.8% [1.7,29.8] P(Tukey) = 0.027 for the intervention group. Between the validation and training sessions for both groups, there was an increase of 9.1% [1.82, 16.4] in scores (p = 0.018). The mean [95% CI] difference in scores of the control and intervention groups was 1.9% [-5.4, 9.2] (p = 0.59). The Cohen's effect size estimate was 0.9. CONCLUSION: Digital stethoscopes and hand-held echo may be useful devices for teaching cardiac auscultation. This pilot study provides a novel study design, a heart murmur grading system, and data that will help develop definitive studies to assess new teaching techniques for cardiac auscultation using digital technology.

Tarjeta de adquisición de datos para el sistema integrado de estetoscopio digital y ECGAR del proyecto SEDAR / Data acquisition card for integrated system of digital stethoscope and ECGAR of SEDAR Project

El artículo presenta el desarrollo de una tarjeta de adquisición de datos (TAD) para uso biomédico. Este diseño forma parte de un sistema que permite realizar un análisis comparativo entre los sonidos cardiopulmonares (SC) y el electrocardiograma de alta definición (ECGAR). La TAD reportada digitaliza simultáneamente tres señales adquiridas. Los dos primeros canales digitalizan las señales correspondientes a las derivaciones dI y dIII del ECGAR. El tercer canal digitaliza la señal captada del SC. El instrumento consta de dos partes, una etapa de hardware para adquirir la señal y un software para la manipulación de datos en la computadora. El hardware está compuesto por un microcontrolador de alto rendimiento, una interfaz de comunicación con la computadora vía USB y los circuitos de seguridad eléctrica inherentes a un equipo médico. El software permite la adquisición de las señales transmitidas desde el hardware, su visualización gráfica y el almacenamiento de la información en una base de datos. Las pruebas de funcionamiento demostraron errores inferiores al 0,1 % en las mediciones de amplitud y no se registró perdida de información en la comunicación con la computadora

The paper reports the development of a data acquisition card (TAD) for biomedical use. This design is part of a system that allows a comparative analysis between cardiopulmonary sounds (SC) and the high-definition electrocardiogram (ECGAR). The reported TAD simultaneously digitizes three acquired signals. The channels 1 and 2 digitize the ECGAR signals corresponding to leads dI and dIII. The channel 3 digitizes to SC captured signal. The instrument consists of two parts, a hardware for acquire the signal and a software for data manipulation in the computer. The hardware consists of a high performance microcontroller, a USB communication interface with the computer and the electrical safety circuits inherent to medical equipment. The software allows the signals acquisition transmitted from the hardware, its graphic visualization and the information storage in a database. The performance tests showed errors less than 0.1 % in amplitude measurements and no loss of information in the communication with the computer

Integrating a hand held computer and stethoscope into a fetal monitor.

This article presents procedures for modifying a hand held computer or personal digital assistant (PDA) into a versatile device functioning as an electronic stethoscope for fetal monitoring. Along with functioning as an electronic stethoscope, a PDA can provide a useful information source for a medical trainee. Feedback from medical students, residents and interns suggests the device is well accepted by medical trainees.