Electronic Stethoscope Research Articles

Abstract 4118342: Acoustic Analysis For The Detection Of Leaflet Thrombosis After Transcatheter Aortic Valve Replacement

Introduction: Following trans-catheter aortic valve replacement (TAVR), patients may develop aortic valve leaflet thrombosis, a condition associated with an increased risk of embolic events. Currently, the diagnosis of post-TAVR leaflet thrombosis is based on echocardiography surveillance or cardiac tomography (CCT) scan, both require specialized team and equipment, and may also expose the patients to radiation and contrast. Acoustic analysis is a non-invasive reproducible method, which incorporates the recording of cardiac sounds and analyzing them via machine learning algorithms. Here, we investigated the diagnostic abilities of acoustic analysis in the detection of post-TAVR leaflet thrombosis. Methods: Using an electronic stethoscope, heart sounds were prospectively recorded at 2 time points: 24-hours and 6-months post-TAVR. CCT scan was performed 6-months following TAVR to detect leaflet thrombosis. Acoustic sounds were analyzed, using a Gaussian support vector machine (SVM) algorithm, to identify features associated with leaflet thrombosis. Results: Of 19 consecutive patients, 3 were found to have high-grade leaflet thrombosis based on CCT results. The SVM algorithm effectively classified heart sounds into "normal" and "thrombotic", with a sensitivity of 100% and specificity of 97.2%. Following anticoagulation therapy, all 3 patients showed no signs of leaflet thrombosis on follow-up CCT scans and were also classified as "normal" by the algorithm. Notably, at the time of leaflet thrombosis, Doppler echocardiographies showed normal pressure gradients across the affected valves. Conclusions: These initial results suggest that acoustic analysis, combined with machine learning algorithms, may identify post-TAVR leaflet thrombosis, providing a non-invasive, cost-effective, and radiation-free alternative to traditional diagnostic methods.

DIGITAL STETHOSCOPE – NEW ERA OF AUSCULTATION

This review is devoted to electronic and digital stethoscopes. We performed analysis of publications within last 10 years in databases e-LIBRARY, PubMed, Google Scholar. Key words for the search were «auscultation», «electronic stethoscope», «digital stethoscope», «telemedicine». Review contains an information about new possibilities in auscultation by using digital stethoscopes. We provided an information about brief characteristics of the products of the most popular manufacturers on the Market. Increasing of functionality and versatility (the ability to analyze heart, lungs and intestine sounds) of digital stethoscope and also increasing of noise reduction and sound filtration will make these devices more attractable for everyday use. When these tasks will be solved, the digital stethoscope will definitely become an indispensable tool in the diagnosis, monitoring and treatment of diseases, as well as patients’ self-monitoring. Contemporary models of digital stethoscopes can be used in telemonitoring of the patients with cardiovascular and respiratory diseases. In this case it is possible to estimate the results of auscultation in dynamics, during long term follow up. This can contribute to early evaluation of complications and decompensations in patients with chronic non-infectious diseases such as chronic obstructive pulmonary diseases, asthma, myocardial infarction, etc.

Use of respiratory signal analysis to assess severity of Brachycephalic Obstructive Airway Syndrome (BOAS) in dogs

Brachycephalic Obstructive Airway Syndrome (BOAS) is a potentially life-threatening condition that can be challenging to diagnose and grade objectively. The aim of this study was to investigate the use of respiratory signal analysis to assess severity of BOAS in dogs. Hundred and seventeen client-owned dogs of brachycephalic and non-brachycephalic breeds were enrolled. Respiratory sounds were recorded using an electronic stethoscope before and after a 3-minute exercise test (ET). Dogs were assigned a BOAS severity grade (BOAS 0–3) using a validated respiratory functional grading scheme. Signal analysis techniques were used to identify seven sound variables. Analysis of variance (ANOVA) was used to investigate associations between variables and BOAS severity and receiver operating characteristic (ROC) curves to assess the diagnostic efficacy of each sound variable. For each sound variable, there was a significant association with BOAS grade. An increase in BOAS grade resulted in greater sound magnitude in the frequency spectrum (0–1000 Hz), and in a greater contribution of lower frequencies (170–260 Hz). The variable “Peak 1” had the best performance in predicting BOAS negative (BOAS 0 +1) versus BOAS positive dogs (BOAS 2 + 3) before the ET; area under the curve (AUC) = 76.6 % (95 % confidence interval 67.4–85.8 %), whereas the variable “Valley 1” had the highest predictive value after the ET; AUC = 87.8 % (95 % confidence interval 81.4–94.3 %). Respiratory signal analysis has good potential for assessing BOAS severity and could be valuable for clinicians in clinical decision processes and for breeders when selecting suitable breeding dogs.

A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs.

The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise. Assess if a machine-learning algorithm can be trained to accurately detect and grade heart murmurs in dogs and detect cardiac disease in electronic stethoscope recordings. Dogs (n = 756) with and without cardiac disease attending referral centers in the United Kingdom. All dogs received full physical and echocardiographic examinations by a cardiologist to grade any murmurs and identify cardiac disease. A recurrent neural network algorithm, originally trained for heart murmur detection in humans, was fine-tuned on a subset of the dog data to predict the cardiologist's murmur grade from the audio recordings. The algorithm detected murmurs of any grade with a sensitivity of 87.9% (95% confidence interval [CI], 83.8%-92.1%) and a specificity of 81.7% (95% CI, 72.8%-89.0%). The predicted grade exactly matched the cardiologist's grade in 57.0% of recordings (95% CI, 52.8%-61.0%). The algorithm's prediction of loud or thrilling murmurs effectively differentiated between stage B1 and B2 preclinical MMVD (area under the curve [AUC], 0.861; 95% CI, 0.791-0.922), with a sensitivity of 81.4% (95% CI, 68.3%-93.3%) and a specificity of 73.9% (95% CI, 61.5%-84.9%). A machine-learning algorithm trained on humans can be successfully adapted to grade heart murmurs in dogs caused by common cardiac diseases, and assist in differentiating preclinical MMVD. The model is a promising tool to enable accurate, low-cost screening in primary care.

Lung Sounds Anomaly Detection with Respiratory Cycle Segmentation

Employing machine learning algorithms in the medical field has proven successful for some time now. Mostly computer vision techniques have been applied to medical images, while medical sound data has been somewhat overlooked. By using electronic stethoscopes, it is now possible to process both heartbeats and lung sounds. While some products are available for detecting anomalies in heartbeats, addressing lung-related anomalies presents a more intricate challenge. Applying a deep learning approach is hindered by insufficient data. Although some datasets do exist, the size and diversity of the data are too small for comprehensive analysis. This paper introduces a novel technique for detecting anomalies in lung sounds: first by combining two datasets, second by automatically segmenting each sound into respiratory cycles, and third by employing GFCCs as sound features.

Intrapulmonary electronic auscultation for asthma and chronic obstructive pulmonary disease

The aim of the study was to develop and perform clinical testing of the intrapulmonary electronic auscultation method for audio and video recording at various levels of the tracheobronchial tree for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD).Methods. We examined 31 patients with moderate (n = 17) and severe (n = 14) COPD with severe symptoms outside of exacerbation and 35 patients with moderate (n = 19) and severe (n = 16) partially controlled asthma. Traditional bronchoscopy was performed, including intrabronchial recording of acoustic noise with a microphone (introduced into the instrumental channel of the bronchoscope) at various levels of the tracheobronchial tree. Video recording in various modes revealed the causes of wheezing. At the same time, pulmonary sounds were recorded in the interscapular region of the chest at the projection points of the bronchoscope in the tracheobronchial tree using a 3M™ Littmann® 3200 electronic stethoscope (USA).Results. The greatest intergroup differences between patients with moderate and severe COPD were demonstrated by intrapulmonary sound parameters (frequency and duration of wheezing, р < 0.0001) and frequency response (amplitude-frequency characteristics) of wheezing in the lumen of the 4th order bronchi (р < 0.0001), significantly exceeded the corresponding sound phenomena during auscultation from the surface of the chest (р < 0.01). The most significant intergroup differences in the groups of BA patients with moderate and severe disease were observed in the frequency response of wheezing during intrapulmonary auscultation in the lumen of the 3rd order (р < 0.0001) and 4th order (р < 0.0001) bronchi; smaller differences were observed in the frequency response during auscultation from the chest surface (р < 0.001).Conclusion. Intrapulmonary auscultation is a modern auscultatory method which can improve the quality and efficiency of diagnosis of asthma and COPD in pulmonology.

Comparison of remote and in-person respiratory function grading of brachycephalic dogs.

To compare the reliability of respiratory function grading (RFG) scores assigned in-person and remotely via video and electronic stethoscope recordings, evaluated by novice and expert graders. Prospective study. Fifty-seven brachycephalic dogs. Dogs were evaluated in person by expert graders and RFG scores were assigned. Audio and video recordings were made during the in-person evaluations. Four expert and four novice graders evaluated the recordings and assigned an RFG score to each dog. Agreement between in-person and remote RFG scores was assessed using Cohen's kappa statistic. Interobserver reliability was assessed using Fleiss' kappa statistic. The median RFG score from the in-person assessment was 1 (range, 0-3). Distribution of RFG scores included 12 grade 0 scores, 19 grade 1 scores, 25 grade 2 scores, and 1 grade 3 score. The raw percentage agreements between remote and in-person scores were 68.4%, 59.6%, 64.9%, and 61.4% for the four experts, and 52.6%, 64.9%, 50.9%, and 42.1% for the four novices. Reliability between remote and in-person RFG scores was poor to moderate both for the experts (Cohen's kappa: .48, .37, .46, .41) and novices (Cohen's kappa: .28, .47, .28, .21). Interobserver reliability was moderate among the experts (Fleiss' kappa: .59) and poor among the novices (Fleiss' kappa: .39). Remote RFG scores had poor to moderate interassessment and interobserver reliability. Novice evaluators performed worse than experts for remote or in-person RFG evaluations. Remote RFG, as measured in this study, is not reliable for assigning RFG scores. Modifications could be made to remote evaluation to improve reliability. Based upon the performance of novice evaluators, training of evaluators is justified.

Classification and Recognition of Lung Sounds Using Artificial Intelligence and Machine Learning: A Literature Review

This review explores the latest advances in artificial intelligence (AI) and machine learning (ML) for the identification and classification of lung sounds. The article provides a historical overview from the invention of the electronic stethoscope to the auscultation of lung sounds, emphasizing the importance of the rapid diagnosis of lung diseases in the post-COVID-19 era. The review classifies lung sounds, including wheezes and stridors, and explores their pathological relevance. In addition, the article deeply explores feature extraction strategies, measurement methods, and multiple advanced machine learning models for classification, such as deep residual networks (ResNets), convolutional neural networks combined with long short-term memory networks (CNN–LSTM), and transformer models (transformer). The article discusses the problems of insufficient data and replicating human expert experience and proposes future research directions, including improved data utilization, enhanced feature extraction, and classification using spectrograms. Finally, the article emphasizes the expanding role of AI and ML in lung sound diagnosis and their potential for further development in this field.

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification

Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.

Background: Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) algorithms for AS screening.Methods: We conducted a thorough search of six databases. Several evaluation parameters, such as sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and area under the curve (AUC) value were employed in the diagnostic meta-analysis of AI-based algorithms for AS screening. The AI algorithms utilized diverse data sources including electrocardiograms (ECG), chest radiographs, auscultation audio files, electronic stethoscope recordings, and cardio-mechanical signals from non-invasive wearable inertial sensors.Results: Of the 295 articles identified, 10 studies met the inclusion criteria. The pooled estimates for AI-based algorithms in diagnosing AS were as follows: sensitivity 0.83 (95% CI: 0.81-0.85), specificity 0.81 (95% CI: 0.79-0.84), PLR 4.78 (95% CI: 3.12-7.32), NLR 0.20 (95% CI: 0.13-0.28), and DOR 27.11 (95% CI: 14.40-51.05). The AUC value was 0.909 (95% CI: 0.889-0.929), indicating outstanding diagnostic accuracy. Subgroup and meta-regression analyses showed that continent, type of AS, data source, and type of AI-based method constituted sources of heterogeneity. Furthermore, we demonstrated proof of publication bias for DOR values analyzed using Egger's regression test (P = 0.002) and a funnel plot.Conclusion: Deep learning approaches represent highly sensitive, feasible, and scalable strategies to identify patients with moderate or severe AS.

ZCHSound: Open-Source ZJU Paediatric Heart Sound Database With Congenital Heart Disease.

From 2020 to 2022, we collaborated with experienced cardiac surgeons from three general children's hospitals to collect heart sound signals from 1259 participants using electronic stethoscopes. To ensure the accuracy of the labels, the labels for all data were confirmed by two cardiac experts. To establish the baseline of ZCHsound, we extracted 84 features and used machine learning models to evaluate the performance of the classification task. The ZCHSound database was divided into two datasets: one is a high-quality, filtered clean heart sound dataset, and the other is a low-quality, noisy heart sound dataset. In the evaluation of the high-quality dataset, our random forest ensemble model achieved an F1 score of 90.3% in the classification task of normal and pathological heart sounds. This study has successfully established a large-scale, high-quality, rigorously standardized pediatric CHD sound database with precise disease diagnosis. This database not only provides important learning resources for clinical doctors in auscultation knowledge but also offers valuable data support for algorithm engineers in developing intelligent auscultation algorithms.

Development of an Electronic Stethoscope and an Integrated Artificial Neural Network Classifier for the Identification of Aortic Stenosis

Development of an Electronic Stethoscope and an Integrated Artificial Neural Network Classifier for the Identification of Aortic Stenosis

Fully Convolutional Hybrid Fusion Network with Heterogeneous Representations for Identification of S1 and S2 from Phonocardiogram.

Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized algorithm for PCG analysis can aid in detecting abnormal signal patterns and support the clinical use of auscultation. It is important to detect fundamental components, such as the first and second heart sounds (S1 and S2), to accurately diagnose heart abnormalities. In this study, we developed a fully convolutional hybrid fusion network to identify S1 and S2 locations in PCG. It enables timewise, high-level feature fusion from dimensionally heterogeneous features: 1D envelope and 2D spectral features. For the fusion of heterogeneous features, we proposed a novel convolutional multimodal factorized bilinear pooling approach that enables high-level fusion without temporal distortion. We experimentally demonstrated the benefits of the comprehensive interpretation of heterogeneous features, with the proposed method outperforming other state-of-the-art PCG segmentation methods. To the best of our knowledge, this is the first study to interpret heterogeneous features through a high level of feature fusion in PCG analysis.

Digital Stethoscope Utility in NICU, SCN, and LDRP Patients

Introduction: To this day, abnormal auscultatory findings on acoustic examination of the cardiovascular system remains the most common reason for referral to a cardiologist. Three limitations exist with acoustic stethoscopes: 1) high selective sensitivity requirement is needed; 2) easy disruption by ambient environmental noise; 3) subjectivity of interpretation based on the provider’s experience, skills, and auditory proficiency. The introduction of digital stethoscopes offered a solution to the limitations of the traditional acoustic stethoscope while at the same time broadening the diagnostic potential of such devices: 1) improvement in sound quality; 2) amplification of the acoustic signal; 3) reduction of ambient noise. The objectives were: 1. To determine the accuracy of the digital stethoscope when used in neonatal intensive care unit (NICU), Special Care Nursery (SCN), and Labor & Delivery Recovery Postpartum (LDRP) patients; and 2. To determine if its use leads to improved detection rates of cardiac pathologies compared to the use of a classic acoustic stethoscope. Methods: This is a single center, prospective study of neonates in the NICU, SCN, and LDRP at Ascension St. John Children’s Hospital. After obtaining informed consent, infants were examined using both acoustic and electronic stethoscopes by trained residents. Data from chart review and echocardiography were also collected. Results from the two stethoscopes were compared to each other and to echocardiographic findings. Data were analyzed using Student’s t-test, the χ 2 test and the McNemar χ 2 test. Results: The study included 15 infants, mean of 33.3 ± 3.8 weeks’ gestation at birth, 46.7% female (7/15) and 66.7% (10/15) black. Of the 15 patients evaluated, for the differential diagnosis and the ECHO results, there was 56% (5/9) better agreement with the electronic stethoscope, 33% (3/9) equivocal agreement between the electronic and acoustic stethoscopes, and 11% (1/9) worse agreement between the electronic stethoscope differential diagnosis and the actual ECHO results. Conclusions: There is a trend towards better agreement between the electronic stethoscope differential diagnosis and the actual ECHO results. A larger sample size is needed to determine true statistical significance of the data and probability.

Voice reduction in cardiac auscultation sounds with reference signals measured from vocal resonators.

This study proposes the use of vocal resonators to enhance cardiac auscultation signals and evaluates their performance for voice-noise suppression. Data were collected using two electronic stethoscopes while each study subject was talking. One collected auscultation signal from the chest while the other collected voice signals from one of the three voice resonators (cheek, back of the neck, and shoulder). The spectral subtraction method was applied to the signals. Both objective and subjective metrics were used to evaluate the quality of enhanced signals and to investigate the most effective vocal resonator for noise suppression. Our preliminary findings showed a significant improvement after enhancement and demonstrated the efficacy of vocal resonators. A listening survey was conducted with thirteen physicians to evaluate the quality of enhanced signals, and they have received significantly better scores regarding the sound quality than their original signals. The shoulder resonator group demonstrated significantly better sound quality than the cheek group when reducing voice sound in cardiac auscultation signals. The suggested method has the potential to be used for the development of an electronic stethoscope with a robust noise removal function. Significant clinical benefits are expected from the expedited preliminary diagnostic procedure.

Rural palliative care in cancer: Using telemedicine to bridge the gap.

e13807 Background: Despite the rapid growth in palliative care (PC) services, many rural regions remain without access to specialty palliative care. Community-based telemedicine may offer solutions to underserved populations from rural areas within the United States. Methods: This is a retrospective review of the creation of 2 separate rural clinics attached to the VCU Massey Comprehensive Cancer Center at CMH in South Hill, VA in 2018 (with interruption related to the Covid-19 pandemic) and in Tappahannock, VA in 2021. In addition to operational descriptions a chart review was conducted of all encounters for these clinics. Care provided was coordinated by the University PC service and the oncology clinics, including nurses and oncologists. Regulatory, legal, Information Technology (IT), and systems logistics were developed in partnership for 6-9 months prior to each pilot. There were over 100 encounters from the two periods of care for CMH (2018 until paused for pandemic towards the end of 2020; and March of 2021 to current) and greater than 50 at Tappahannock. All visits were conducted in the rural oncology clinics with assistance of oncology nursing during the encounter. Results: The average patient age was 57, and over 90% had solid tumors. On average, patients had 1-2 telemedicine visits. The most common reason for referral was symptom management, predominantly pain. Edmonton Symptom Assessment scales were collected at all visits by oncology nursing. Physical exams were completed with electronic stethoscopes and supported by oncology nursing. Medications were prescribed to rural pharmacies electronically directly by the PC physician; for the periods of clinic over the course of the Covid-19 public health emergency this included prescriptions for controlled substances (almost exclusively long and short acting opioids). For patients requiring advance care planning documents were completed electronically or by trained interdisciplinary team members in clinic in partnership with the PC physician. Technological issues occurred rarely (less than 1% of visits) and resolved without IT involvement. Conclusions: Our pilot program integrated specialist palliative care into two rural oncology clinics providing supportive care, including symptom management and goals of care discussions. Further research should define optimal integration of PC telemedicine into rural oncology.

Design and Implementation of an Integrated Electronic Stethoscope System

Design and Implementation of an Integrated Electronic Stethoscope System

MEASUREMENT OF BASIC HEMODYNAMICS DURING MULTIPOST TRANSCUTANEOUS EXTERNAL EAR STIMULATION: A TWO-CASE STUDY

The objectives of the investigation were to assess the short-term responses of the cardio-vascular system on the multipost transcutaneous stimulation (tANS) of the external ear (EE). The scope was to measure the forefinger photopleths (FPPG), toe photopleths (TPPG), aortic phonocardiogram (APCG), mitral phonocardiogram (MPCG) and brachial arterial blood pressure (BABP), assuming that they could be altered with the tANS. For the tANS, stimulator, two silicone ear plugs with four platinum electrodes each, and a large common electrode (CE), were used. Trials were carried out with two female volunteers, aged 25 years and 28 years. To capture the heart sounds, two customized electronic stethoscopes (transducers) were used. BABP was measured using a pressure transducer and blood-pressure appliance. To measure the FPPGs, a pulse oximeter and SpO2 finger clip were used. To measure the TPPGs, another pulse oximeter and customized SpO2 foot clip was used. Signals were gathered using a high-performance data-acquisition system. An offline analysis was made just before and just after the start of the tANS. To record the pulsations in the right brachial artery, the 2D ultrasound mode (B) of the ultrasound device was used before and during the tANS. Then, diagrams showing the vein cross-sectional area over time were constructed. The results show that vascular time intervals between heart sounds S1 and S2, captured as APCG and systolic peaks of the FPPG in the second volunteer, were slightly larger during the separate tANS of the left white (LW) and (RW) EE post than before the tANS. Furthermore, vascular time intervals between the heart sounds S1 and S2, captured as MPCG and systolic peaks of the TPPG in the second volunteer, were slightly smaller during the separate tANS of LW and RW EE post than before the tANS. Finally, tANS of the LW post elicited a slightly lower heart rate than the one measured when LW was not stimulated. In contrast, the tANS of the RW post elicited a slightly higher heart rate than the one measured when RW was not stimulated. It was also shown that the pattern of the vein cross-sectional area over time with tANS was different compared to both the FPPG and TPPG.

#540 Investigation of vascular sound visualization system using electronic stethoscope

Abstract Background and Aims The routine evaluation of VA involves visual, palpatory, and auscultatory examinations, as well as monitoring of venous pressure and other parameters. Surveillance indices include Flow Volume (FV) and Resistance Index (RI) obtained by Doppler ultrasound (DU) and Access Blood Flow (Qa) obtained by the Ultrasound Dilution Method (UDM) are well known. On the other hand, the MSS-U11C electronic stethoscope (Fig. 1) and dedicated application (Fig. 2) developed by Pioneer Inc. converts vascular sound into digital data and calculates the INDEX value obtained from the sound intensity (dB) analyzed by the vascular sound visualization system. In this study, we examined the correlation between INDEX values and conventional surveillance indices. Method 1. Eighty-three cases in which INDEX, FV, RI, and Qa were simultaneously measured in hemodialysis patients with AVF at the Artificial Kidney Department of Osaka Metropolitan University Hospital were included. 2. The measurement site for INDEX was just above the anastomosis of the AVF. 3. FV and RI were measured using a KONICA MINOLTA SONIMAGE HS2 diagnostic ultrasound system with the brachial artery as the measurement site. 4. Measurements were expressed in Median [IQR], and for statistical examination, Pearson's correlation coefficient was used for the correlation of each measurement, transformed to the natural logarithm to satisfy the assumption of normality of the residuals. The significance level for all hypothesis tests was 5%. For statistical analysis, R version 4.2.1 (https://www.r-project.org/foundation/) with the “rms” and “RcmdrPlugin.EZR ” packages were used. 5. This study protocol was conducted in accordance with the Principles of the Declaration of Helsinki. The ethics review boards of Osaka Metropolitan University approved the study (No. 2021-243). Opt-out consent was obtained instead of written informed consent. Results INDEX showed a significant positive correlation with Qa and FV (R = 0.449, P = 0.002 / R = 0.484, P < 0.001, respectively), INDEX showed a significant negative correlation with RI (R = −0.336, P = 0.002). Conclusion INDEX measurement using an electronic stethoscope is a monitoring index and showed a positive correlation with FV and Qa, which are established surveillance indices, and a weak negative correlation with RI. In Japan, FV and RI are commonly used as surveillance indices, but it has been reported that these indices can be underestimated or overestimated depending on the skill of the examiner, and that Qa requires specialized equipment and cannot be measured depending on the puncture site. We believe that monitoring by stethoscope is influenced by subjective factors of staff members, which may lead to differences in evaluation, but INDEX could become a more quantitative and standardized indicator. In addition, the electronic stethoscope could be used without any difference compared to the stethoscope currently in clinical use.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis.

Electronic stethoscope used to detect cardiac sounds that contain essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electromechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, it is found that triboelectric sensor features superior advantages over piezoelectric one for microquantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215mV Pa-1) than the piezoelectric counterpart (21mV Pa-1) in the sound pressure range of 50-80dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis.