Review

Authors: Edin Begic, Zijo Begic

INTRODUCTION

Accidental murmurs occur in anatomically and physiologically normal heart. Accidental (innocent) murmurs have their own clearly defined clinical characteristics (asymptomatic, they require minimal follow-up care).

AIM

To point out the significance of auscultation of the heart in the differentiation of heart murmurs and show clinical characteristics of accidental heart murmurs.

MATERIAL AND METHODS

Article presents review of literature which deals with the issue of accidental heart murmurs in the pediatric cardiology.

RESULTS

In the group of accidental murmurs we include classic vibratory parasternal-precordial Stills murmur, pulmonary ejection murmur, the systolic murmur of pulmonary flow in neonates, venous hum, carotid bruit, Potaine murmur, benign cephalic murmur and mammary souffle.

CONCLUSION

Accidental heart murmurs are revealed by auscultation in over 50% of children and youth, with a peak occurrence between 3-6 years or 8-12 years of life. Reducing the frequency of murmurs in the later period can be related to poor conduction of the murmur, although the disappearance of murmur in principle is not expected. It is the most common reason of cardiac treatment of the child, and is a common cause of unreasonable concern of parents.

Topics: Asymptomatic Diseases; Diagnosis, Differential; Heart; Heart Auscultation; Heart Murmurs; Heart Sounds; Humans

PubMed: 28974851
DOI: 10.5455/medarh.2017.71.284-287

Authors: Caroline Piette, Pierre Mélon, Patrizio Lancellotti...

Cardiac erethism is a state of hyperexcitability of the heart. It results in hyperpulsatility, which is characterized by an ample pulse, an accentuation of heart sounds on auscultation, and an exaggeration of heart movements on echocardiography. While it can be very troublesome, cardiac erethism has no pathological significance. However, care must be taken to exclude any underlying cardiac or extracardiac pathology before confirming the diagnosis. No treatment is usually considered except to reassure the patient and avoid contributing factors. Beta-blockers are effective and should be prescribed on a case-by-case basis.

Topics: Humans; Heart; Echocardiography; Auscultation

PubMed: 37350212
DOI: No ID Found

Authors: Pratham N Soni, Siyu Shi, Pranav R Sriram...

Data labeling is often the limiting step in machine learning because it requires time from trained experts. To address the limitation on labeled data, contrastive learning, among other unsupervised learning methods, leverages unlabeled data to learn representations of data. Here, we propose a contrastive learning framework that utilizes metadata for selecting positive and negative pairs when training on unlabeled data. We demonstrate its application in the healthcare domain on heart and lung sound recordings. The increasing availability of heart and lung sound recordings due to adoption of digital stethoscopes lends itself as an opportunity to demonstrate the application of our contrastive learning method. Compared to contrastive learning with augmentations, the contrastive learning model leveraging metadata for pair selection utilizes clinical information associated with lung and heart sound recordings. This approach uses shared context of the recordings on the patient level using clinical information including age, sex, weight, location of sounds, etc. We show improvement in downstream tasks for diagnosing heart and lung sounds when leveraging patient-specific representations in selecting positive and negative pairs. This study paves the path for medical applications of contrastive learning that leverage clinical information. We have made our code available here: https://github.com/stanfordmlgroup/selfsupervised-lungandheartsounds.

PubMed: 35079716
DOI: 10.1016/j.patter.2021.100400

Authors: Liuyang Han, Weijin Liang, Qisen Xie...

Real-time monitoring of vital sounds from cardiovascular and respiratory systems via wearable devices together with modern data analysis schemes have the potential to reveal a variety of health conditions. Here, a flexible piezoelectret sensing system is developed to examine audio physiological signals in an unobtrusive manner, including heart, Korotkoff, and breath sounds. A customized electromagnetic shielding structure is designed for precision and high-fidelity measurements and several unique physiological sound patterns related to clinical applications are collected and analyzed. At the left chest location for the heart sounds, the S1 and S2 segments related to cardiac systole and diastole conditions, respectively, are successfully extracted and analyzed with good consistency from those of a commercial medical device. At the upper arm location, recorded Korotkoff sounds are used to characterize the systolic and diastolic blood pressure without a doctor or prior calibration. An Omron blood pressure monitor is used to validate these results. The breath sound detections from the lung/ trachea region are achieved a signal-to-noise ration comparable to those of a medical recorder, BIOPAC, with pattern classification capabilities for the diagnosis of viable respiratory diseases. Finally, a 6×6 sensor array is used to record heart sounds at different locations of the chest area simultaneously, including the Aortic, Pulmonic, Erb's point, Tricuspid, and Mitral regions in the form of mixed data resulting from the physiological activities of four heart valves. These signals are then separated by the independent component analysis algorithm and individual heart sound components from specific heart valves can reveal their instantaneous behaviors for the accurate diagnosis of heart diseases. The combination of these demonstrations illustrate a new class of wearable healthcare detection system for potentially advanced diagnostic schemes.

PubMed: 37607132
DOI: 10.1002/advs.202301180

Authors: Yoshimi Sato, Tatsuya Kawasaki, Sakiko Honda...

BACKGROUND

The 4th heart sound (S4) is commonly heard in patients with hypertrophic cardiomyopathy (HCM). The 3rd heart sound (S3) is also audible in HCM patients regardless of the presence or absence of heart failure. These extra heart sounds may be associated with myocardial fibrosis because myocardial fibrosis has been suggested to affect left ventricular compliance.

METHODS AND RESULTS

The present retrospective study evaluated 53 consecutive HCM patients with sinus rhythm who had no symptoms of heart failure and underwent an initial assessment including phonocardiography, echocardiography, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI). S3 was detected on phonocardiography in 13% of all patients, and S4 was recorded in 75% of patients. Patients with S3 had a higher incidence of LGE and larger LGE volumes (86% and 11.5±2.4 g/cm, respectively) than patients without S3 (33% and 2.5±0.8 g/cm, respectively; P=0.02 and P=0.002). The presence of S4 was not associated with MRI findings, including the incidence of LGE and LGE volume. The diagnostic value of S3 for the detection of LGE was highly specific (97%), with a low sensitivity (29%).

CONCLUSIONS

Myocardial fibrosis, as assessed by LGE, was associated with S3 but not with S4 in patients with HCM. These results may contribute to the risk stratification of patients with HCM.

Topics: Adult; Aged; Aged, 80 and over; Cardiomyopathy, Hypertrophic; Female; Fibrosis; Gadolinium; Heart Sounds; Humans; Magnetic Resonance Imaging, Cine; Male; Middle Aged; Myocardium; Retrospective Studies; Risk Assessment

PubMed: 28924076
DOI: 10.1253/circj.CJ-17-0650

Authors: Alastair J Rankin, Stephen H Rankin, Andrew C Rankin...

BACKGROUND

Doctors are taught to auscultate with the stethoscope applied to the skin, but in practice may be seen applying the stethoscope to the gown.

OBJECTIVES

To determine how often doctors auscultate heart and breath sounds through patients' gowns, and to assess the impact of this approach on the quality of the sounds heard.

METHODS

A sample of doctors in the west of Scotland were sent an email in 2014 inviting them to answer an anonymous questionnaire about how they auscultated heart and breath sounds. Normal heart sounds from two subjects were recorded through skin, through skin and gown, and through skin, gown and dressing gown. These were played to doctors, unaware of the origin of each recording, who completed a questionnaire about the method and quality of the sounds they heard.

RESULTS

206 of 445 (46%) doctors completed the questionnaire. 124 (60%) stated that they listened to patients' heart sounds, and 156 (76%) to patients' breath sounds, through patients' gowns. Trainees were more likely to do this compared with consultants (OR 3.39, 95% CI 1.74 to 6.65). Doctors of all grades considered this practice affected the quality of the sounds heard. 32 doctors listened to the recorded heart sounds. 23 of the 64 (36%) skin and 23 of the 64 (36%) gown recordings were identified. The majority of doctors (74%) could not differentiate between skin or gown recordings, but could tell them apart from the double layer recordings (p=0.02). Trainees were more likely to hear artefactual added sounds (p=0.04).

CONCLUSIONS

Many doctors listen to patients' heart and breath sounds through hospital gowns, at least occasionally. In a short test, most doctors could not distinguish between sounds heard through a gown or skin. Further work is needed to determine the impact of this approach to auscultation on the identification of murmurs and added sounds.

Topics: Clinical Competence; Heart Auscultation; Heart Sounds; Humans; Internship and Residency; Reproducibility of Results; Respiratory Sounds; Scotland; Stethoscopes; Surveys and Questionnaires

PubMed: 26183342
DOI: 10.1136/postgradmedj-2015-133321

Review

Authors: W Frank Peacock, Alex Harrison, Alan S Maisel...

Acoustic cardiography is an exciting, new, easy-to-use, modernized technology that incorporates already proven techniques of phonocardiography. Application of acoustic cardiography to clinical practice can improve diagnosis and management of heart failure patients. Its clinical use should help address some of the need for robust, inexpensive, and widely accessible technology for proactive heart failure diagnosis and management. Acoustic cardiographically recorded measurements have been correlated with both cardiac catheterization and echocardiographically determined hemodynamic parameters. Heart sounds captured by acoustic cardiograms have proven to assist clinicians in assessing dyspneic patients in the emergency department by utilizing the strong specificity of an S3 for detecting acute decompensated heart failure. Acoustic cardiography offers a cost-efficient, easy-to-use method to optimize the devices used in cardiac resyncronization therapy. The rapidly and easily obtainable information gathered by acoustic cardiography should foster its more widespread use in diagnosis and treatment of heart failure, including cardiac resyncronization therapy device optimization.

Topics: Auscultation; Echocardiography; Electrocardiography; Female; Heart Failure, Systolic; Heart Murmurs; Heart Sounds; Humans; Male; Phonocardiography; Sensitivity and Specificity; Systole

PubMed: 16894267
DOI: 10.1111/j.0889-7204.2006.05766.x

Authors: Ali Harimi, Yahya Majd, Abdorreza Alavi Gharahbagh...

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images. In the proposed approach, the output image is, therefore, the projection of the reconstructed phase space representation of the phonocardiogram (PCG) signal on three coordinate planes. This has two major benefits: (1) it makes possible to apply deep convolutional neural networks to heart sounds and (2) it is also possible to employ a transfer learning scheme by converting a heart sound signal to an image. The performance of the proposed approach was verified on the PhysioNet dataset. Due to the imbalanced data on this dataset, it is common to assess the results quality using the average of sensitivity and specificity, which is known as score, instead of accuracy. In this study, the best results were achieved using the InceptionV3 model, which achieved a score of 88.06%.

Topics: Humans; Heart Sounds; Neural Networks, Computer; Heart Diseases; Signal Processing, Computer-Assisted; Machine Learning

PubMed: 36559937
DOI: 10.3390/s22249569

Authors: A W SLOAN

Topics: Heart Murmurs; Heart Sounds; Humans; Phonocardiography

PubMed: 14849852
DOI: No ID Found