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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., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

The electronic stethoscope combined with artificial intelligence (AI) technology has realized the digital acquisition of heart sounds and intelligent identification of congenital heart disease, which provides objective basis for heart sound auscultation and improves the accuracy of congenital heart disease diagnosis. At the present stage, the AI based cardiac auscultation technique mainly focuses on the research of AI algorithms, and the researchers have designed and summarized a variety of effective algorithms based on the characteristics of cardiac audio data, among which the mel-frequency cepstral coefficients (MFCC) is the most effective one, and widely used in the cardiac auscultation. However, the current cardiac sound analysis techniques are based on specific data sets, and have not been validated in clinic, so the performance of algorithms need to be further verified. The lack of heart sound data, especially the high-quality, standardized, publicly available heart sound database with disease labeling, further restricts the development of heart sound diagnostic analysis and its application in screening. Therefore, expert consensus is necessary in establishing an authoritative heart sound database and standardizing the heart sound auscultation screening process for congenital heart disease. This paper provides an overview of the research and application status of auscultation algorithm and hardware equipment based on AI in auscultation screening of congenital heart disease, and puts forward the problems to be solved in clinical application of AI auscultation screening technology.

Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure.

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma-delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal-oxide-semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.

Hamman's sign refers to an unusual click that occurs in synchrony with heart sounds, and is pathognomonic for left-sided pneumothorax and spontaneous mediastinum. In this case, a 17-year-old man living in a rural area used his smartphone to record an audible clicking sound emanating from his thorax. This occurred following coughing episodes secondary to an upper respiratory tract infection. Initially, this prompted a request for an echocardiogram to exclude structural cardiac anomalies; however, Hamman's sign was also considered. This facilitated the timely diagnosis of pneumothorax to be made via a simple chest radiograph, one of the only imaging modalities available at the patient's rural health service. To promote awareness of this rare clinical phenomenon, this report also presents the patient's own sound recording of Hamman's sign and corresponding chest radiographs., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and objectives: As the prevalence of obesity is increasing in a population, diagnostics becomes more problematic. Our aim was to compare the 3M Littmann 3200 Electronic Stethoscope and 3M Littman Cardiology III Mechanical Stethoscope in the auscultation of obese patients. Methods . A total of 30 patients with body mass index >30 kg/m² were auscultated by a cardiologist and a resident physician: 15 patients by one cardiologist and one resident and 15 patients by another cardiologist and resident using both stethoscopes. In total, 960 auscultation data points were verified by an echocardiogram. Sensitivity and specificity data were calculated. Results. Sensitivity for regurgitation with valves combined was higher when the electronic stethoscope was used by the cardiologist (60.0% vs. 40.9%, p = 0.0002) and the resident physician (62.1% vs. 51.5%, p = 0.016); this was also the same when stenoses were added (59.4% vs. 40.6%, p = 0.0002, and 60.9% vs. 50.7%, p = 0.016, respectively). For any lesion, there were no significant differences in specificity between the electronic and acoustic stethoscopes for the cardiologist (92.4% vs. 94.2%) and the resident physician (93.6% vs. 94.7%). The detailed analysis by valve showed one significant difference in regurgitation at the mitral valve for the cardiologist (80.0% vs. 56.0%, p = 0.031). No significant difference in specificity between the stethoscopes was found when all lesions, valves and both physicians were combined (93.0% vs. 94.4%, p = 0.30), but the electronic stethoscope had higher sensitivity than the acoustic (60.1% vs. 45.7%, p < 0.0001). The analysis when severity of the abnormality was considered confirmed these results. Conclusions. There is an indication of increased sensitivity using the electronic stethoscope. Specificity was high using the electronic and acoustic stethoscope., Competing Interests: The research was sponsored by 3M and two co-authors are employees of the company.

Heart murmur evaluation is the most common cause of referral to cardiology, and auscultation of heart sounds with a stethoscope remains a key component of the initial cardiovascular exam. Adoption of telecardiology has been limited by challenges in teleauscultation. We set out to compare in-person auscultatory findings with heart sounds recorded by the Core stethoscope (Eko, Berkeley, CA) in patients with normal heart sounds, innocent heart murmurs, and a variety of pathologic findings. Our study demonstrates that Eko recordings had a high percent of agreement with in-person auscultation findings and echocardiogram findings, with moderate inter-rater reliability. It was useful in identifying patients with pathologic murmurs who would benefit from further assessment. It was able to discern major types of pathological murmurs. Certain qualitative differences in the recorded sounds as compared to in-person auscultation were identified by the reading cardiologists. They were able to acclimate to these subtle differences. The system was felt to be easy to use, and most cardiologists in the study would consider using it in clinical settings. The Eko Core system may be a useful screening tool for murmur evaluation.

Objective: To examine the accuracy of home blood pressure (BP) devices, on their owners, compared to auscultatory reference standard BP measurements., Methods: Eighty-five consecutive consenting subjects ≥18 years of age, who owned an oscillometric home BP device (wrist or upper-arm device), with BP levels between 80-220/50-120 mm Hg, and with arm circumferences between 25-43 cm were studied. Pregnancy and atrial fibrillation were exclusion criteria. Device measurements from each subject's home BP device were compared to simultaneous 2-observer auscultation using a mercury sphygmomanometer. Between-group mean comparisons were conducted using paired t-tests. The proportion of patients with device-to-auscultatory differences of ≥5, 10, and 15 mm Hg were tabulated and predictors of systolic and diastolic BP differences were identified using linear regression., Results: Mean age was 66.4 ± 11.0 years, mean arm circumference was 32.7 ± 3.7 cm, 54% were female and 78% had hypertension. Mean BPs were 125.7 ± 14.0/73.9 ± 10.4 mm Hg for home BP devices vs. 129.0 ± 14.7/72.9 ± 9.3 for auscultation (difference of -3.3 ± 7.3/0.9 ± 6.1; P values <0.0001 for systolic and 0.17 for diastolic). The proportion of devices with systolic or diastolic BP differences from auscultation of ≥5, 10, and 15 mm Hg was 69%, 29%, and 7%, respectively. Increasing arm circumference was a statistically significant predictor of higher systolic (parameter estimate 0.61 per cm increase; P value 0.004) and diastolic (0.38; 0.03) BP., Conclusions: Although mean differences from 2-observer auscultation were acceptable, when tested on their owners, most home BP devices were not accurate to within 5 mm Hg. Ensuring acceptable accuracy of the device-owner pairing should be prioritized., (© American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

The present study introduces a detailed methodology which can be applied for objective evaluation and comparison of the acoustic parameters of medical stethoscopes using auscultation sounds as test signals. The described approach allows taking into account the acoustic coupling between the body of an auscultated patient and the chest piece of a stethoscope. Information obtained from additional, synchronized electrocardiography measurements is used to extract short, specific fragments of recordings, defined as acoustic events. Analysis of the spectral characteristics of many acoustic events allows us to compare the acoustic properties of various stethoscopes and to estimate the measurement uncertainty. The exemplary results of the comparative evaluation of acoustic properties of bell and diaphragm-type chest pieces of a single stethoscope are presented. The results show that the frequency characteristics of the signals obtained using both examined chest pieces under the conditions of the performed examinations are very similar.

Background: Auscultation is one of the basic techniques for the diagnosis of heart disease. However, the interpretation of heart sounds and murmurs is a highly subjective and difficult skill., Objectives: To assist the auscultation skill at the bedside, a handy phonocardiogram was developed using a smartphone (Samsung Galaxy J, Android OS 4.4.2) and an external microphone attached to a stethoscope., Methods and Results: The Android app used Java classes, "AudioRecord," "AudioTrack," and "View," that recorded sounds, replayed sounds, and plotted sound waves, respectively. Sound waves were visualized in real-time, simultaneously replayed on the smartphone, and saved to WAV files. To confirm the availability of the app, 26 kinds of heart sounds and murmurs sounded on a human patient simulator were recorded using three different methods: a bell-type stethoscope, a diaphragm-type stethoscope, and a direct external microphone without a stethoscope. The recorded waveforms were subjectively confirmed and were found to be similar to the reference waveforms., Conclusions: The real-time visualization of the sound waves on the smartphone may help novices to readily recognize and learn to distinguish the various heart sounds and murmurs in real-time., (© 2017 S. Karger AG, Basel.)

The objective of the present paper is to provide a detailed review of the most recent developments in instrumentation and signal processing of digital phonocardiography and heart auscultation. After a short introduction, the paper presents a brief history of heart auscultation and phonocardiography, which is followed by a summary of the basic theories and controversies regarding the genesis of the heart sounds. The application of spectral analysis and the potential of new time-frequency representations and cardiac acoustic mapping to resolve the controversies and better understand the genesis and transmission of heart sounds and murmurs within the heart-thorax acoustic system are reviewed. The most recent developments in the application of linear predictive coding, spectral analysis, time-frequency representation techniques, and pattern recognition for the detection and follow-up of native and prosthetic valve degeneration and dysfunction are also presented in detail. New areas of research and clinical applications and areas of potential future developments are then highlighted. The Final section is a discussion about a multidegree of freedom theory on the origin of the heart sounds and murmurs, which is completed by the authors' conclusion.

Auscultation is a routine exam and the first line of screening in heart pathologies. The objective of this study was to assess if using a new data collection system, the DigiScope Collector, with a guided and automatic annotation of heart auscultation, different levels of expertise/experience users could collect similar digital auscultations. Data were collected within the Heart Caravan Initiative (Paraíba, Brasil). Patients were divided into two study groups: Group 1 evaluated by a third year medical student (User 1), and an experienced nurse (User 2); Group 2 evaluated by User 2 and an Information Technology professional (User 3). Patients were auscultated sequentially by the two users, according to the randomization. Features extracted from each data set included the length (HR) of the audio files, the number of repetitions per auscultation area, heart rate, first (S1) and second (S2) heart sound amplitudes, S2/S1, and aortic (A2) and pulmonary (P2) components of the second heart sound and relative amplitudes (P2/A2). Features extracted were compared between users using paired-sample test Wilcoxon test, and Spearman correlations (P<;0.05 considered significant). Twenty-seven patients were included in the study (13 Group 1, and 14 Group 2). No statistical significant differences were found between groups, except in the time of auscultation (User 2 consistently presented longer auscultation time). Correlation analysis showed significant correlations between extracted features from both groups: S2/S1 in Group 1, and S1, S2, A2, P2, P2/A2 amplitudes, and HR in Group 2. Using the DigiScope Collector, we were able to collect similar digital auscultations, according to the features evaluated. This may indicate that in sites with limited access to specialized clinical care, auscultation files may be acquired and used in telemedicine for an expert evaluation.

Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.

This study pertains to a six-channel acoustic monitoring system for use in patient monitoring during or after surgery. The base hardware consists of a USB data acquisition system, a custom-built six-channel amplification system, and a series of microphones of various designs. The software is based on the MATLAB platform with data acquisition drivers installed. The displayed information includes: time domain signals, frequency domain signals, and tools to aid in the detection of endobronchial intubation. We hypothesize that the above mentioned arrangement may be helpful to the anesthesiologist in recognizing clinical conditions like wheezing, bronchospasm, endobronchial intubation, and apnea. The study also evaluated various types of microphone designs used to transduce breath sounds. The system also features selectable band-pass filtering using MATLAB algorithms as well as a collection of recordings obtained with the system to establish what respiratory acoustic signals look like under various conditions.

The use of left ventricular assist devices (LVADs), implantable pumps used to supplement cardiac output, has become an increasingly common and effective treatment for advanced heart failure. Although modern continuous-flow LVADs improve quality of life and survival more than medical management of heart failure, device malfunction remains a common concern. Improved noninvasive methods for assessment of LVAD function are needed to detect device complications. An electronic stethoscope was used to record sounds from the HeartMate II axial flow pump in vitro and in vivo. The data were then uploaded to a computer and analyzed using two types of acoustic analysis software. Left ventricular assist device acoustics were quantified and were related to pump speed, acoustic environment, and inflow and outflow graft patency. Peak frequency values measured in vivo were found to correlate strongly with both predicted values and in vitro measurements (r > 0.999). Plots of the area under the acoustic spectrum curve, obtained by integrating over 50 Hz increments, showed strong correlations between in vivo and in vitro measurements (r > 0.966). Device thrombosis was found to be associated with reduced LVAD acoustic amplitude in two patients who underwent surgical device exchange.

Cardiac examination has evolved over centuries. The goal of cardiac evaluation, regardless the era, is to "see" inside the heart to diagnose congenital and acquired intra-cardiac structural and functional abnormalities. This article briefly reviews the history of cardiac examination and discusses contemporary best, evidence-based methods of cardiac inspection.

In an effort to decrease the spread of hospital-acquired infections, many hospitals currently use disposable plastic stethoscopes in patient rooms. As an alternative, this study examines a prototype electronic stethoscope that does not break the isolation barrier between clinician and patient and may also improve the diagnostic accuracy of the stethoscope exam. This study aimed to investigate whether the new prototype electronic stethoscope improved auscultation of heart sounds compared to the standard conventional isolation stethoscope. In a controlled, non-blinded, cross-over study, clinicians were randomized to identify heart sounds with both the prototype electronic stethoscope and a conventional stethoscope. The primary outcome was the score on a 10-question heart sound identification test. In total, 41 clinicians completed the study. Subjects performed significantly better in the identification of heart sounds when using the prototype electronic stethoscope (median = 9 [7-10] vs. 8 [6-9] points, p value <0.0001). Subjects also significantly preferred the prototype electronic stethoscope. Clinicians using a new prototype electronic stethoscope achieved greater accuracy in identification of heart sounds and also universally favoured the new device, compared to the conventional stethoscope.

Heart sound characteristics are linked to blood pressure, and its interpretation is important for detection of cardiovascular disease. In this study, heart sounds' auscultation, acquired from children patients (27 patients, 10.2±3.9 years, 35.7±20.8 kg, 132.3±25.5 cm), were automatically segmented to extract the two main components: the first sound (S1) and the second sound (S2). Following, a set of time, frequency, and wavelet based features, were extracted from the S2, and analyzed in relation to the noninvasive cuff-based measures of blood pressure (mean blood pressure of 78±8.8 mmHg). A multivariate regression analysis was performed for each S2 feature set to determine which features better related to the blood pressure measurements. The best results, in the leave-one-out evaluation, were obtained using the frequency features set, with a MAE of 6.08 mmHg, a MAPE of 7.85%, and a ME of 0.31 mmHg, in the estimation of the mean blood pressure.

Esophageal stethoscope is less invasive and easy to handling. And it gives a lot of information. The purpose of this study is to investigate the correlation of blood pressure and heart sound as measured by esophageal stethoscope. Four male beagles weighing 10 to 12 kg were selected as experimental subjects. After general anesthesia, the esophageal stethoscope was inserted. After connecting the microphone, the heart sounds were visualized and recorded through a self-developed equipment and program. The amplitudes of S1 and S2 were monitored real-time to examine changes as the blood pressure increased and decreased. The relationship between the ratios of S1 to S2 (S1/S2) and changes in blood pressure due to ephedrine was evaluated. The same experiment was performed with different concentration of isoflurane. From S1 and S2 in the inotropics experiment, a high correlation appeared with change in blood pressure in S1. The relationship between S1/S2 and change in blood pressure showed a positive correlation in each experimental subject. In the volatile anesthetics experiment, the heart sounds decreased as MAC increased. Heart sounds were analyzed successfully with the esophageal stethoscope through the self-developed program and equipment. A proportional change in heart sounds was confirmed when blood pressure was changed using inotropics or volatile anesthetics. The esophageal stethoscope can achieve the closest proximity to the heart to hear sounds in a non-invasive manner.

Detection of murmurs and gallops may help to identify cats with heart disease. However, auscultatory findings may be subject to clinically relevant observer variation. The objective of this study was to evaluate an electronic stethoscope (ES) in cats. We hypothesized that the ES would perform at least as well as a conventional stethoscope (CS) in the detection of abnormal heart sounds. One hundred and fifty consecutive cats undergoing echocardiography were enrolled prospectively. Cats were ausculted with a CS (WA Tycos Harvey Elite) by two observers, and heart sounds were recorded digitally using an ES (3M Littmann Stethoscope Model 3200) for off-line analysis. Echocardiography was used as the clinical standard method for validation of auscultatory findings. Additionally, digital recordings (DRs) were assessed by eight independent observers with various levels of expertise, and compared using interclass correlation and Cohen's weighted kappa analyses. Using the CS, a heart murmur (n = 88 cats) or gallop sound (n = 17) was identified in 105 cats, whereas 45 cats lacked abnormal heart sounds. There was good total agreement (83-90%) between the two observers using the CS. In contrast, there was only moderate agreement (P <0.001) between results from the CS and the DRs for murmurs, and poor agreement for gallops. The CS was more sensitive compared with the DRs with regard to murmurs and gallops. Agreement among the eight observers was good-to-excellent for murmur detection (81%). In conclusion, DRs made with the ES are less sensitive but comparably specific to a CS at detecting abnormal heart sounds in cats.

Spontaneously beating human pluripotent stem cell-derived cardiomyocytes clusters (CMCs) represent an excellent in vitro tool for studies of human cardiomyocyte function and for pharmacological cardiac safety assessment. Such testing typically requires highly trained operators, precision plating, or large cell quantities, and there is a demand for real-time, label-free monitoring of small cell quantities, especially rare cells and tissue-like structures. Array formats based on sensing of electrical or optical properties of cells are being developed and in use by the pharmaceutical industry. A potential alternative to these techniques is represented by the quartz crystal microbalance with dissipation monitoring (QCM-D) technique, which is an acoustic surface sensitive technique that measures changes in mass and viscoelastic properties close to the sensor surface (from nm to μm). There is an increasing number of studies where QCM-D has successfully been applied to monitor properties of cells and cellular processes. In the present study, we show that spontaneous beating of CMCs on QCM-D sensors can be clearly detected, both in the frequency and the dissipation signals. Beating rates in the range of 66-168 bpm for CMCs were detected and confirmed by simultaneous light microscopy. The QCM-D beating profile was found to provide individual fingerprints of the hPS-CMCs. The presented results point towards acoustical assays for evaluation cardiotoxicity., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The feasibility of detecting heart sounds (HS) from an accelerometer sensor enclosed within an implantable cardioverter defibrillator (ICD) pulse generator (PG) was explored in a noninvasive pilot study on heart failure (HF) patients with audible third HS (S3)., Methods: Accelerometer circuitry enhanced for HS was incorporated into non-functional ICDs. A study was conducted on 30 HF patients and 10 normal subjects without history of cardiac disease. The devices were taped to the skin surface over both left and right pectoral regions to simulate subcutaneous implants. A lightweight reference accelerometer was taped over the cardiac apex. Waveforms were recorded simultaneously with a surface electrocardiogram for 2 minutes. Algorithms were developed to perform off-line automatic detection of HS and HS time intervals (HSTIs)., Results: S1, S2, and S3 vibrations were detected in all accelerometer locations for all 40 subjects, including 16 subjects without an audible S3. A substantial proportion of S3 energy was infrasonic (<20 Hz). Extending the signal bandwidth accordingly increased HS amplitudes and the ability of S3 to separate HF patients from the normal subgroup. HSTIs also separated the subgroups and were less susceptible to patient-dependent acoustic propagation properties than amplitude measures., Conclusion: HS, including S3 amplitude and HSTIs, may be measured using PG-embedded circuitry at implant sites without special purpose leads. Further study is warranted to determine if relative changes in heart sounds measurements can be effective in applications such as remote ambulatory monitoring of HF progression and the detection of the onset of HF decompensation., (©2012, The Authors. Journal compilation ©2012 Wiley Periodicals, Inc.)

Physical cardiovascular examination, particularly cardiac auscultation, is one of the most difficult clinical skills for students during their medical training. Studies suggest that the use of technologies such as digital stethoscope increase the accuracy of clinical examination, however, its impact on the teaching of cardiac auscultation for undergraduate students of medicine is not known. The objective is to demonstrate the usefulness of the digital stethoscope compared to traditional methods as a tool in the teaching of auscultatory skills. nterventional, longitudinal, controlled, unicenter and randomized study. Thirty-eight medicine students were enrolled for a cardiovascular semiology course lasting eight weeks. The course program included lectures and bedside practice in Cardiology wards. In the practical lessons, the students were randomized into two groups: 1) (n = 21) digital stethoscope (Littmann® Model 3200, 3M); and 2) (n = 17) conventional stethoscopes. A pre-training evaluation was conducted through a test using the software Heart Sounds®, which was repeated after the course. The average scores were compared by paired T test and unpaired T test. It is observed that, at the end of the course, there was a significantly greater improvement in the group that used the digital stethoscope (51.9%) compared to the group using the conventional stethoscope (29.5%). Short-term interventions for cardiac semiology teaching are able to contribute significantly to improving proficiency in the identification of heart sounds. The use of digital stethoscope proved to be a positive factor in teaching these skills.

Auscultation is widely applied in clinical activity, nonetheless sound interpretation is dependent on clinician training and experience. Heart sound features such as spatial loudness, relative amplitude, murmurs, and localization of each component may be indicative of pathology. In this study we propose a segmentation algorithm to extract heart sound components (S1 and S2) based on it's time and frequency characteristics. This algorithm takes advantage of the knowledge of the heart cycle times (systolic and diastolic periods) and of the spectral characteristics of each component, through wavelet analysis. Data collected in a clinical environment, and annotated by a clinician was used to assess algorithm's performance. Heart sound components were correctly identified in 99.5% of the annotated events. S1 and S2 detection rates were 90.9% and 93.3% respectively. The median difference between annotated and detected events was of 33.9 ms.

Background and Objective: Cardiac auscultation is one of the most important parts of the cardiological examination traditionally performed with acoustic stethoscopes. The aim of this study was to compare the sensitivities and the diagnostic capabilities of traditional and electronic stethoscopes in detecting canine heart murmurs., Materials and Methods: The study was performed on 21 dogs referred for cardiologic examination with suspected heart murmurs. Six out of these dogs had cardiac murmurs bilaterally. Cardiac auscultation was performed independently by a final-year veterinary student (AB=I1) and by an experienced clinician (KV=I2), both using a traditional and a Welch Allyn Meditron electronic sensor-based stethoscope. Final diagnoses were established by echocardiography and by digital phonocardiography., Results: Correct detection of a murmur was made by I1 with a traditional stethoscope in 20/27 (74.0%) of the suspected murmurs (p=0.30, kappa[κ] =0.2) and with the electronic stethoscope in 26/27 (96.3%), respectively (p=0.0013, κ=0.75). I2 correctly detected the murmurs with the traditional stethoscope in 25/27 (92.6%) cases (p=0.0013, κ=0.75) and with the electronic stethoscope in all 27/27 (100%) cases (p=0.00012, κ=1). Agreements of murmur intensity gradings between traditional and electronic stethoscopes were highly significant (I1: p=6.9´10⁻⁸; κ=0.79), (I2: p=5.2´10⁻¹¹; κ=0.92). When grading the murmurs with the traditional stethoscope, there was a significant agreement between I1 and I2 (p=2.9´10⁻⁷; κ=0.79), being even higher with the electronic stethoscope (p=1.1´10⁻¹¹; κ=0.92)., Conclusion: The electronic stethoscope was more sensitive than the traditional one in detecting and grading cardiac murmurs being especially useful for I1 with less experience. However, it can be suggested to use a traditional and an electronic stethoscopes simultaneously to optimally utilize their advantages.

This paper presents a chest-worn accelerometer with high sensitivity for continuous cardio-respiratory sound monitoring. The accelerometer is based on an asymmetrical gapped cantilever which is composed of a bottom mechanical layer and a top piezoelectric layer separated by a gap. This novel structure helps to increase the sensitivity by orders of magnitude compared with conventional cantilever based accelerometers. The prototype with a resonant frequency of 1100Hz and a total weight of 5 gram is designed, constructed and characterized. The size of the prototype sensor is 35mm×18mm×7.8mm (l×w×t). A built-in charge amplifier is used to amplify the output voltage of the sensor. A sensitivity of 86V/g and a noise floor of 40ng/√Hz are obtained. Preliminary tests for recording both cardiac and respiratory signals are carried out on human body and the new sensor exhibits better performance compared with a high-end electronic stethoscope.

Objectives: The ability to auscultate during air medical transport is compromised by high ambient noise levels. The aim of this study was to assess the capabilities of a traditional and an amplified stethoscope (which is expected to reduce background and ambient noise) to assess heart and breath sounds during medical transport in a Falcon 50 plane., Methods: A prospective, double-blind, randomized study was performed. We tested 1 model of traditional stethoscope (Littman cardiology III) and 1 model of amplified stethoscope (Littman 3100). We studied heart and lung auscultation during real medical evacuations aboard Falcon 50 (medically configured). For each, the quality of auscultation was described using a numeric rating scale (ranging from 0 to 10, with 0 corresponding to "I hear nothing" and 10 corresponding to "I hear perfectly"). Comparisons were accomplished using a t test for paired values., Results: A total of 32 comparative evaluations were performed. For cardiac auscultation, the value of the rating scale was 5.8 ± 1.5 and 6.4 ± 1.9, respectively, for the traditional and amplified stethoscope (P = .018). For lung sounds, quality of auscultation was estimated at 3.3 ± 2.4 for traditional stethoscope and at 3.7 ± 2.9 for amplified stethoscope (P = .15)., Conclusions: Practicians in Falcon 50 are more able to hear cardiac sounds with an amplified than with a traditional stethoscope, whereas there is no significant difference concerning breath sounds auscultation., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: Dyspneic emergency department (ED) patients present a diagnostic dilemma. The S3, although highly specific for acute heart failure (AHF) and predicting death and readmission, is often difficult to auscultate. The HEart failure and Audicor technology for Rapid Diagnosis and Initial Treatment (HEARD-IT) multinational trial evaluated the S3 via acoustic cardiography (Audicor). Our goal in this secondary analysis was to determine if the strength of the S3 can provide diagnostic/prognostic information in problematic heart failure subgroups., Methods: Dyspneic ED patients older than 40 years and not on dialysis were prospectively enrolled. A gold standard AHF diagnosis was determined by 2 cardiologists blinded to acoustic cardiography results. The S3 strength parameter was delineated on a scale of 0 to 10. This secondary analysis of subgroups from the HEARD-IT database used univariate/multivariate regression to determine the diagnostic/prognostic ability of the S3 strength., Results: In the 995 patients enrolled, S3 strength was a significant prognosticator in univariate analysis for adverse events but not in a multivariable model. In patients with "gray zone" B-type natriuretic peptide (BNP) levels (100-499 pg/mL), acoustic cardiography increased diagnostic accuracy of AHF from 47% to 69%. Acoustic cardiography improved S3 detection sensitivity in obese patients when compared to auscultation., Conclusion: The strength of the S3 gallop provides rapid results that assist with identification of AHF in selected populations. S3 detection complements the use of BNP in the gray zone, and its diagnostic/prognostic ability is largely unaffected by body mass index and renal function. S3 strength shows promise as a diagnostic and prognostic tool in problematic HF subgroups., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The goals of this study were to present a technique of digitalised sound recordings and phonocardiograms (dPCGs), and to analyse its diagnostic capabilities. Heart sounds of 20 dogs were auscultated in vivo (on-line) and recorded with dPCGs by two authors using a Welch Allyn Meditron Stethoscope System. Sound recordings were auscultated off-line and blindly by four different observers having various auscultatory experiences, then listened to while viewing dPCGs. The results were compared to echocardiographic diagnoses. There was a significant agreement (p < 0.001) between on-line and off-line auscultatory findings regarding the four observers, ranging from 45% to 75% (weighted kappa values: 0.72 to 0.87). The best agreement was achieved by Observer 1 having the highest experience. Significant differences (p < 0.05) were found between Observer 1 and Observer 4 (with the lowest experience) in judging the quality of the murmurs during the off-line and blind auscultation. However, there were only minimal differences (95% to 100% agreements) in dPCG analyses among the four observers regarding intensity and quality of the murmurs while simultaneously listening to and viewing the dPCGs. Significant correlations were found between the traditional '0 to 6 scale' and a new '0 to 3 scale' murmur intensity gradings by all observers (correlation coefficients 0.640 to 0.908; p < 0.01 to p < 0.001). Analysis of dPCGs might be a valuable, additional tool helping with the diagnosis of canine cardiac murmurs, especially for those with less cardiological experience.

State-of-the-Art disease management for Congestive Heart Failure (CHF) patients is still based on easy-to-acquire measures such as heart rate (HR), weight and blood pressure (BP). However, these measures respond late to changes of the patient health status and provide limited information to personalize and adapt medication therapy. This paper describes our concept called "Cardiac Status Assessment" we have been investigating within the European project "HeartCycle" towards next-generation home-based disease management of CHF. In our concept we analyze non-invasive surrogate measures of the cardio-vascular function in particular systolic time intervals and pulse wave characteristics to estimate Cardiac Output (CO) and Systemic Vascular Resistance (SVR) both are established clinical measures. We discuss the underlying concept, a developed measurement system and first results.

Morbid obesity is believed to limit cardiovascular auscultation. We compared audiocardiography to senior attending physicians using conventional stethoscopes in 190 individuals with morbid obesity. Overall, there were 128 (67.4%) women and 62 (32.6%) men with mean ages of 44.9 +/- 12.3 and 51.3 +/- 10.8 , respectively (P = 0.001). The overall body mass index (BMI) was 47.3 +/- 8.5 kg m(-2). Of those with an S(3) by audiocardiography (n = 7), one had a history of coronary artery disease (CAD), none had a history of heart failure, and one had a left ventricular ejection fraction (LVEF) <45%. The mean LVEF was 58.6 +/- 9.9 versus 61.6 +/- 5.3 for those with and without an S(3) by audiocardiography (P = 0.16). By contrast, of those (n = 6) with an S(3) by stethoscope, one had a history of CAD, two had histories of heart failure, and 3 had LVEF < 45%. The mean LVEF of those with and without S(3) by stethoscope was 53.7 +/- 2.3 and 61.6 +/- 5.5%, respectively (P = 0.02). There were 40 (21.1%) patients with an S(4) (S(4) strength >5) identified by acoustic cardiography while there were 42 (22.1%) heard by the stethoscope and it was heard with both methods in nine patients (21.4% concordance). There were no significant correlations between BMI or peak oxygen consumption and S(3) or S(4) strength by audiocardiography. Acoustic cardiography performed with an electronic device was not helpful in assisting the cardiovascular examination of the morbidly obese. These data suggest the careful clinical exam with attention to traditional cardiac auscultation using a stethoscope in a quiet room should remain the gold standard.

In order to discriminate normal and abnormal heart sounds accurately and effectively, a new method is proposed to analyze heart sounds, namely heart sound characteristic waveform (HSCW) method. Digital stethoscope is used to collect heart sound signals. The recorded data are transmitted to a computer by USB interface for analysis based on HSCW, which is extracted from an analytical model of single degree-of-freedom (SDOF). Furthermore, a case study on the normal and abnormal cardiac sounds is demonstrated to validate the usefulness and efficiency of the proposed HSCW method. Besides, in order to test the accuracy of discriminating normal and abnormal heart sounds, 40 normal and 20 abnormal heart sounds are collected and analyzed, the accuracy performances are achieved by 92.5% and 95.0%, respectively.

This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.

The aim of this project is to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds. A commercially available electronic stethoscope was used to record the diastolic heart sounds from patients diagnosed with or without coronary artery disease (CAD) based on their coronary angiography examination. The nonlinear dynamical analysis (approximate entropy) measures of the diastolic heart sounds recorded from 30 patients with coronary occlusions and ten normal subjects were estimated. Results suggest the presence of the high nonlinear (approximate entropy) values of diastolic heart sounds associated with CAD (p < 0.05). This approach led to a sensitivity of 77%, a specificity of 80%, and an overall accuracy of 78%. As a summary, 23 out of 30 abnormal patients and eight out of ten normal patients were correctly detected.

This paper addresses the estimation of systolic time intervals, namely the pre-ejection period (PEP) and the left ventricular ejection time (LVET), using heart sound. PEP is estimated with a Bayesian approach resorting to the signal's instantaneous amplitude and typical time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, aortic valve closure is determined through the analysis of a high-frequency signature of S2. Additionally, LVET has also been estimated from a PPG signal at a peripheral site, for the sake of comparison over a subset of data. We evaluated our algorithms on a set of 658 heartbeats and achieved 10.32 msec average absolute PEP estimation error with 7.3 msec standard deviation and for LVET, 15.8 msec average estimation error with 13.6 msec standard deviation. Current results support our assumption that heart sounds can be applied to detect the onset of the aortic valve movement processes.

A brief review of the history of cardiac auscultation confirms its decline. The intervention of healthcare institutions may avoid loss of medical culture and increased costs by adequate training of medical staff.

Cardiac auscultation, long considered the centerpiece of the cardiac clinical examination, is rapidly becoming a lost art. Inadequate emphasis on the essentials of cardiac auscultation has resulted from the widespread availability of more elaborate and expensive "high-tech" diagnostic and therapeutic methods, particularly Doppler echocardiography. However, sophisticated high technology is not a substitute for a solid foundation in clinical cardiology including cardiac auscultation. When used properly, the stethoscope remains a valuable and cost-effective clinical tool that often enables many well-trained and experienced cardiac auscultators to make a rapid and accurate cardiac diagnosis with fewer, if any, additional studies. Not every patient needs every test. Accordingly, this monograph reviews the fundamental principles of the art of cardiac auscultation. Emphasis is placed on the proper use of the stethoscope and the diagnostic and prognostic significance of the myriad heart sounds and murmurs present in patients with and without symptomatic heart disease. A practical clinical overview of the common auscultatory findings encountered in a variety of cardiac disease states and conditions will also be discussed. This monograph will inspire many practitioners to pick up their stethoscope, practice their cardiac examination, perfect their auscultatory skills, and reap the rewards of rediscovering this time-honored method of evaluating the cardiovascular system.

Background: As many as 50-70% of asymptomatic children referred for specialist evaluation or echocardiography because of a murmur have no heart disease., Hypothesis: Computer-assisted auscultation (CAA) can improve the sensitivity and specificity of referrals for evaluation of heart murmurs., Methods: Seven board-certified primary care physicians were evaluated both without and with use of a computer-based decision-support system using 100 prerecorded patient heart sounds (55 innocent murmurs, 30 pathological murmurs, 15 without murmur). The sensitivity and specificity of their murmur referral decisions relative to American College of Cardiology/American Heart Association (ACC/AHA) guidelines, and sensitivity and specificity of murmur detection and characterization (innocent versus pathological) were measured., Results: Sensitivity for detection of murmurs significantly increased with use of CAA from 76.6 to 89.1% (p <0.001), while specificity remained unaffected (80.0 versus 81.0%). Computer-assisted auscultation improved sensitivity of correctly identifying pathological murmur cases from 82.4 to 90.0%, and specificity of correctly identifying benign cases (with innocent or no murmurs) from 74.9 to 88.8%. (p <0.001). Referral sensitivity increased from 86.7 to 92.9%, while specificity increased from 63.5 to 78.6% using CAA (p <0.001)., Conclusions: Computer-assisted auscultation appears to be a promising new technology for informing the referral decisions of primary care physicians., (Copyright (c) 2008 Wiley Periodicals, Inc.)