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.

A novel methodology for evaluation of S2 wide split via estimated parameters.

BACKGROUND AND OBJECTIVE: Aimed at the shortcomings of using time interval ( [Formula: see text] ) between the sounds produced by the aortic valve closure (A2) and the pulmonary valve closure (P2) to detect the wide splitting of the second heart sound (S2), which are the [Formula: see text] easily influenced by the heartbeat and not easily distinguished from the fixed splitting of S2 without considering the entire respiratory phase, and from the third heart sound (S3), this study proposes a novel methodology to detect the wide splitting of S2 using an estimated split coefficient of S2 ( [Formula: see text] ) combined with an adaptive number (NAda) of S2. METHODOLOGY: The methodology is orderly summarized as follows: Stage 1 describes the segmentation-based S2 automatic location and extraction. A Gaussian mixture model (GMM)-based regression model for S2 is proposed to estimate the positions of A2 and P2, then an overlapping rate (OLR)-based [Formula: see text] and the [Formula: see text] are estimated, and finally, a NAda-S2 is automatically determined to calculate the statistics of [Formula: see text] and [Formula: see text] . In stage 3, based on the combination of estimated features, the detection of wide splitting of S2 is determined. RESULTS: The performance is evaluated using a total of 3350-period heart sounds from 72 patients, with an overall accuracy of 100%, F1=1 and a Cohen's kappa value (κ) of 1. DISCUSSION: The significant contributions are highlighted: A novel GMM-based efficient methodology is proposed for estimating the characteristics of A2 and P2. A novel OLR-based [Formula: see text] is defined to replace the current state-of-the-art criterion for evaluating the split degree of S2. Considering respiration phases combined with CR are proposed for the high-precision diagnosis of S2 wide split.

Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram.

OBJECTIVE: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. METHODS: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. RESULTS: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. CONCLUSIONS: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. SIGNIFICANCE: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.

Correlation between Murmurs and Echocardiographic Findings; From an Imaging Cardiologist Point of View.

A heart murmur in adults is a common reason for referral for echocardiography at most general cardiology clinics in Europe. A murmur may indicate either a mild age-related valvular calcification or regurgitation, or represent a significant heart valve disease requiring valvular intervention. Generally, the correlation between murmurs by auscultation and severity of heart valve disease by echocardiography is poor. Particularly, the severity and characterization of diastolic murmurs by auscultation may poorly correlate with echocardiographic findings. This narrative review aims to summarize the differential diagnoses of physiological and pathological murmurs, describes the current referral practice of murmur patients for echocardiography, and presents a single-center experience on the correlation of auscultation and echocardiographic findings with a particular focus on aortic and mitral valve diseases. A careful auscultation of the heart prior to the echocardiogram is mandatory and may help to predict the echocardiographic findings and their interpretation in view of the clinical information. The correlation between clinical examination, point of care ultrasound and standard echocardiography is a matter of continued exploration.

Real-world evaluation of the Stemoscope electronic tele-auscultation system.

BACKGROUND: With the spread of COVID-19, telemedicine has played an important role, but tele-auscultation is still unavailable in most countries. This study introduces and tests a tele-auscultation system (Stemoscope) and compares the concordance of the Stemoscope with the traditional stethoscope in the evaluation of heart murmurs. METHODS: A total of 57 patients with murmurs were recruited, and echocardiographs were performed. Three cardiologists were asked to correctly categorize heart sounds (both systolic murmur and diastolic murmur) as normal vs. abnormal with both the Stemoscope and a traditional acoustic stethoscope under different conditions. Firstly, we compared the in-person auscultation agreement between Stemoscope and the conventional acoustic stethoscope. Secondly, we compared tele-auscultation (recorded heart sounds) agreement between Stemoscope and acoustic results. Thirdly, we compared both the Stemoscope tele-auscultation results and traditional acoustic stethoscope in-person auscultation results with echocardiography. Finally, ten other cardiologists were asked to complete a qualitative questionnaire to assess their experience using the Stemoscope. RESULTS: For murmurs detection, the in-person auscultation agreement between Stemoscope and the acoustic stethoscope was 91% (p = 0.67). The agreement between Stemoscope tele-auscultation and the acoustic stethoscope in-person auscultation was 90% (p = 0.32). When using the echocardiographic findings as the reference, the agreement between Stemoscope (tele-auscultation) and the acoustic stethoscope (in-person auscultation) was 89% vs. 86% (p = 1.00). The system evaluated by ten cardiologists is considered easy to use, and most of them would consider using it in a telemedical setting. CONCLUSION: In-person auscultation and tele-auscultation by the Stemoscope are in good agreement with manual acoustic auscultation. The Stemoscope is a helpful heart murmur screening tool at a distance and can be used in telemedicine.

The robustness of Random Forest and Support Vector Machine Algorithms to a Faulty Heart Sound Segmentation.

Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).

Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders.

Body auscultation is a frequent clinical diagnostic procedure used to diagnose heart problems. The key advantage of this clinical method is that it provides a cheap and effective solution that enables medical professionals to interpret heart sounds for the diagnosis of cardiac diseases. Signal processing can quantify the distribution of amplitude and frequency content for diagnostic purposes. In this experiment, the use of signal processing and wavelet analysis in screening cardiac disorders provided enough evidence to distinguish between the heart sounds of a healthy and unhealthy heart. Real-time data was collected using an IoT device, and the noise was reduced using the REES52 sensor. It was found that mean frequency is sufficiently discriminatory to distinguish between a healthy and unhealthy heart, according to features derived from signal amplitude distribution in the time and frequency domain analysis. The results of the present study indicate the adequate discrimination between the characteristics of heart sounds for automatic detection of cardiac problems by signal processing from normal and abnormal heart sounds.

Automatic pediatric congenital heart disease classification based on heart sound signal.

Congenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that effective cooperation of the patient is required for the acquisition of useable heart sounds by electronic stethoscopes, yet the quality of heart sounds in pediatric is poor compared to adults due to the factors such as crying and breath sounds. This paper presents a novel pediatric CHD intelligent auscultation method based on electronic stethoscope. Firstly, a pediatric CHD heart sound database with a total of 941 PCG signal is established. Then a segment-based heart sound segmentation algorithm is proposed, which is based on PCG segment to achieve the segmentation of cardiac cycles, and therefore can reduce the influence of local noise to the global. Finally, the accurate classification of CHD is achieved using a majority voting classifier with Random Forest and Adaboost classifier based on 84 features containing time domain and frequency domain. Experimental results show that the performance of the proposed method is competitive, and the accuracy, sensitivity, specificity and f1-score of classification for CHD are 0.953, 0.946, 0.961 and 0.953 respectively.

A novel intelligent system based on adjustable classifier models for diagnosing heart sounds.

A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). The innovations of this system are primarily reflected in the automatic segmentation and extraction of the first complex sound [Formula: see text] and second complex sound [Formula: see text]; the automatic extraction of the secondary envelope-based diagnostic features [Formula: see text], [Formula: see text], and [Formula: see text] from [Formula: see text] and [Formula: see text]; and the adjustable classifier models that correspond to the confidence bounds of the Chi-square ([Formula: see text]) distribution and are adjusted by the given confidence levels (denoted as [Formula: see text]). The three stages of the proposed system are summarized as follows. In stage 1, the short time modified Hilbert transform (STMHT)-based curve is used to segment and extract [Formula: see text] and [Formula: see text]. In stage 2, the envelopes [Formula: see text] and [Formula: see text] for periods [Formula: see text] and [Formula: see text] are obtained via a novel method, and the frequency features are automatically extracted from [Formula: see text] and [Formula: see text] by setting different threshold value ([Formula: see text]) lines. Finally, the first three principal components determined based on principal component analysis (PCA) are used as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based component objective function [Formula: see text] is generated. Then, the [Formula: see text] distribution for component k is determined by calculating the Mahalanobis distance from [Formula: see text] to the class mean [Formula: see text] for component k, and the confidence region of component k is determined by adjusting the optimal confidence level [Formula: see text] and used as the criterion to diagnose HSs. The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other state-of-the-art methods, and the highest classification accuracies of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 99.67[Formula: see text] and 99.91[Formula: see text] in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting [Formula: see text] to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively.

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification.

Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.

Murmurs other than the early diastolic murmur in aortic dissection.

The purpose of this review is to draw attention to the presence and significance of murmurs other than the murmur of aortic regurgitation, in patients with aortic dissection. For that purpose, a literature search was conducted using Pubmed and Googlescholar. The search terms were "dissecting aneurysm of the aorta", "systolic murmurs", "ejection systolic murmurs", "holosystolic" murmurs, "continuous murmurs", and "Austin-Flint" murmur. Murmurs other than the murmur of aortic regurgitation, which were associated with aortic dissection, fell into the categories of systolic murmurs, some of which were holosystolic, and continuous murmurs, the latter attributable to fistulae between the dissecting aneurysm and the left atrium, right atrium, and the pulmonary artery, respectively. Mid-diastolic murmurs were also identified, and these typically occurred in association with both the systolic and the early diastolic murmurs. Among patients with systolic murmurs clinical features which enhanced the pre-test probability of aortic dissection included back pain, stroke, paraplegia, unilateral absence of pulses, interarm differences in blood pressure, hypertension, shock, bicuspid aortic valve, aortic coarctation, Turner's syndrome, and high D-dimer levels, respectively. In the absence of the murmur of aortic regurgitation timely diagnosis of aortic dissection could be expedited by increased attention to parameters which enhance pretest probability of aortic dissection. That logic would apply even if the only murmurs which were elicited were systolic murmurs.

[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.

Assessing the clinical significance of echocardiograms in determining treatment of patent ductus arteriosus in neonates.

BACKGROUND: To evaluate the utility of echocardiogram (ECHO) in detection and treatment of patent ductus arteriosus (PDA) and hemodynamically significant PDA (hsPDA) in preterm neonates. METHODS: This was a retrospective case-control study of all preterm infants born or admitted to the level III Neonatal Intensive Care Unit in McMaster Children's Hospital from January 2009 to January 2013. These cases were further classified into the following sub-groups: group A) hsPDA confirmed on ECHO; and the control, group B) PDA (but not hemodynamically significant) confirmed on ECHO. Patients without an ECHO were excluded from all analyses. The primary outcome was incidence of treatment for PDA. RESULTS: PDA treatment was administered in 83.3% and 11.2% of patients in groups A and B respectively (P < 0.05). Among patients with a hsPDA within group A, 17% did not receive treatment, while 11% of patients with non-hemodynamically significant PDA received treatment for the PDA. Within the cohort of patients who received treatment for a hsPDA, gestational age below 35 weeks as well as murmurs heard on auscultation were both found to be predictors of treatment. CONCLUSION: While the ECHO remains the gold standard for detecting pathological PDA, there is evidence that other traditional clinical measures continue to guide clinical practice and treatment decisions. Further research is required to gain an understanding of how clinical measures and ECHO may be used in conjunction to optimize resource utilization.

Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease.

Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

A Review of Computer-Aided Heart Sound Detection Techniques.

Cardiovascular diseases have become one of the most prevalent threats to human health throughout the world. As a noninvasive assistant diagnostic tool, the heart sound detection techniques play an important role in the prediction of cardiovascular diseases. In this paper, the latest development of the computer-aided heart sound detection techniques over the last five years has been reviewed. There are mainly the following aspects: the theories of heart sounds and the relationship between heart sounds and cardiovascular diseases; the key technologies used in the processing and analysis of heart sound signals, including denoising, segmentation, feature extraction and classification; with emphasis, the applications of deep learning algorithm in heart sound processing. In the end, some areas for future research in computer-aided heart sound detection techniques are explored, hoping to provide reference to the prediction of cardiovascular diseases.

Maternity Providers' Perspectives on Barriers to Utilization of Intermittent Fetal Monitoring: A Qualitative Study.

There are 2 approaches to fetal assessment during labor: continuous electronic fetal monitoring (EFM) and intermittent auscultation (IA). The vast majority of healthy labors in the United States use EFM, despite professional organization recommendations against its use for low-risk pregnancies. This qualitative investigation explores maternity care team members' perspectives on why EFM is the dominant approach to fetal assessment instead of IA. Focus groups comprised of nurses, midwives, and physicians were conducted using a semistructured interview guide. Transcripts were analyzed using directed content analysis to identify themes related to clinical and nonclinical factors influencing the type of fetal assessment employed during labor. Seven focus groups with a total of 41 participants were completed. Seven themes were identified: clinical environment; technology; policies, procedures, and evidence-based protocols; patient-centered influences; fear of liability; providers as members of healthcare team; and deflection of responsibility. All maternity care team members had knowledge of the evidence base supporting IA use for low-risk care. Nurses identified unique challenges in having agency over monitoring decision making and executing best practices. Improved communication among team members can facilitate evidence-based approaches to IA use, facilitating increased utilization for low-risk labor care.

A Markov-Switching Model Approach to Heart Sound Segmentation and Classification.

OBJECTIVE: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs), which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features. METHODS: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration-dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding. RESULTS: Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR-SLDS approach significantly outperforms the hidden semi-Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature-based HSMM. The segmented labels were then used to train Gaussian-mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings. CONCLUSION: The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset. SIGNIFICANCE: It is potentially useful in developing automated heart monitoring systems for pre-screening of heart pathologies.

The effects of training with simulation on knowledge, skill and anxiety levels of the nursing students in terms of cardiac auscultation: A randomized controlled study.

BACKGROUND: The use of simulation methods in nursing education is important in terms of decreasing anxiety of students in a safe and realistic environment due to the improvement of knowledge and skills of students in terms of cardiac auscultation and their attitudes to prepare for clinical applications. OBJECTIVES: The aim of this study is to compare the effectiveness of high-fidelity simulator and traditional teaching method on nursing students' knowledge and skill development in terms of cardiac auscultation and their anxiety levels. DESIGN: Randomized controlled study. SETTING: The study was conducted in the simulation laboratory of the Nursing Department in the Health College and in the inpatient clinics of the Medicine Faculty Hospital. PARTICIPANTS: 72 first-year nursing students (simulation group = 36, control group = 36). METHODS: The students were randomly distributed to the simulation and control groups. The students in the simulation group received a cardiac auscultation training by using a high-fidelity simulator while the students in the control group received training with the traditional teaching method. After the training sessions, all students practiced their skills in the laboratory and on real patients in clinical setting under the supervision of the researcher. The data were collected by using the Demographic Information Form, Knowledge Assessment Form for Cardiac Auscultation, Skill Evaluation Form for Cardiac Auscultation and State Anxiety Inventory (SAI). RESULTS: High-fidelity simulators and traditional teaching method were found to be effective in increasing the students' knowledge and skill levels in terms of cardiac auscultation. However, it was found that the high-fidelity simulator method was more effective than the traditional teaching method to increase the students' knowledge (p = 0.001) and skill (p < 0.001) levels; this increase was significant. In addition, it was found that the students in the high-fidelity simulator group showed a significant decrease in anxiety scores compared to the students who were trained with traditional education method (p < 0.001). CONCLUSIONS: The results showed that the use of high-fidelity simulator in nursing education was more effective than traditional method in terms of improving the students' knowledge, skill levels for cardiac auscultation and reducing their anxiety.