Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

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.

[Artificial intelligence technology in cardiac auscultation screening for congenital heart disease: present and future].

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.

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes.

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.

Smartphone aiding in analysis of Hamman's sign.

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.

Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation.

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.

A Comparison of Electronic and Traditional Stethoscopes in the Heart Auscultation of Obese Patients.

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.

Real-World Evaluation of the Eko Electronic Teleauscultation System.

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.

An Assessment of the Accuracy of Home Blood Pressure Monitors When Used in Device Owners.

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.

Acoustic characterization of stethoscopes using auscultation sounds as test signals.

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.

Development of a Smartphone App for Visualizing Heart Sounds and Murmurs.

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.

Review: Most Recent Advancements in Digital Signal Processing of the Phonocardiogram.

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.

Why should you model time when you use Markov models for heart sound analysis.

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.

Comparison between users of a new methodology for heart sound auscultation.

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.

Acoustic Characterization of Axial Flow Left Ventricular Assist Device Operation In Vitro and In Vivo.

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.

A multi-channel acoustics monitor for perioperative respiratory monitoring: preliminary data.

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.

A Short History of Cardiac Inspection: A Quest "To See with a Better Eye".

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.