Do you hear what you see? Utilizing phonocardiography to enhance proficiency in cardiac auscultation

An open-access simultaneous electrocardiogram and phonocardiogram database

Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.

Acoustic biomarkers in asthma: a systematic review

OBJECTIVE

Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring.

DATA SOURCES

A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023.

STUDY SELECTION

27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound.

RESULTS

Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified.

CONCLUSIONS

This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.

AI diagnosis of heart sounds differentiated with super StethoScope

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

Utilization of Simulation to Teach Cardiac Auscultation: A Systematic Review

This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, evaluates the effectiveness of simulation-based education in teaching cardiac auscultation. A team of researchers conducted a comprehensive, systematic search of the PubMed database from 2010 to 2021, focusing on cardiac auscultation, education, proficiency, and students. After rigorous filtering, a total of 14 articles, primarily involving medical students and residents, met the inclusion criteria. The articles were categorized based on their focus areas: diagnostic accuracy, knowledge acquisition, competency, and learner satisfaction. Findings suggest that the majority of the studies (86% or 12 out of 14) reported positive outcomes of using simulation for teaching cardiac auscultation, demonstrating improvements in the identified focus areas across diverse contexts. The review underscores the need for future research to further standardize simulation teaching practices, aiming to reduce costs, improve usability, and possibly incorporate multiple simulation approaches in a universal educational process. This approach could enhance outcomes across varied fields and learning styles.

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

Background

We have shown classical cardiac auscultation was superior to remote auscultation. We developed a phonocardiogram system to visualize sounds in remote auscultation.

Objective

This study aimed to evaluate the effect of phonocardiograms on the diagnostic accuracy in remote auscultation using a cardiology patient simulator.

Methods

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.

Results

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.

Conclusions

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.

Trial registration

UMIN-CTR UMIN000045271; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051710.

Feasibility and effectiveness of cardiac auscultation training with a cardiology patient simulator for first-year medical students

BACKGROUND

The effectiveness of cardiac auscultation training with a cardiology patient simulator for medical students is still unclear. Starting such training earlier may help students improve their proficiency. We investigated whether cardiac auscultation training using a simulator for first-year students is feasible and effective.

METHODS

A total of 43 first-year medical students (5-12 in each year, 2015-2019) participated in three 1.5-hour extra-curricular classes comprising mini-lectures, facilitated training, two different auscultation tests (the second test closer to clinical setting than the first), and a questionnaire. The test results were compared with those of 556 fourth-year medical students who participated in a compulsory 3-hour cardiac auscultation class in 2016-2019.

RESULTS

The accuracy rate of all heart sounds and murmurs was higher in the first-year students than in the fourth-year students in both the first (85.8 vs. 79.4 %, p = 0.001) and second (71.3 vs. 61.2 %, p = 0.02) tests. That of second/third/fourth sounds was also higher in the first-year students than in the fourth-year students in both the first (86.0 vs. 79.7 %, p = 0.01) and second (70.9 vs. 53.9 %, p = 0.002) tests. The accuracy rate of murmurs was higher in the first-year students than in the fourth-year students in the first test (85.5 vs. 78.9 %, p = 0.04), but not in the second test (72.1 vs. 75.7 %, p = 0.58). All the first-year students and 65 % of them agreed that they had received sufficient knowledge and built sufficient skills, respectively. All the first-year students and 93 % of them agreed that they were satisfied with the program, and that the program was suitable for first-year students, respectively.

CONCLUSIONS

Although training time was different between the two groups and it is possible that only motivated first-year students participated in the program, these results suggest that our cardiac auscultation training is feasible and effective for first-year medical students.