Automated cardiac auscultation for detection of pathologic heart murmurs

Evaluation of the heart sounds in children using a Doppler Phonolyser

BACKGROUND

Heart auscultation is an easy and inexpensive tool for early diagnosis of congenital heart defects. In this regard, a simple device which can be used easily by physicians for heart murmur detection will be very useful. The current study was conducted to evaluate the validity of a Doppler-based device named "Doppler Phonolyser" for the diagnosis of structural heart diseases in pediatric patients. In this cross-sectional study, 1272 patients under 16 years who were referred between April 2021 and February 2022, to a pediatric cardiology clinic in Mofid Children Hospital, Tehran, Iran, were enrolled. All the patients were examined by a single experienced pediatric cardiologist using a conventional stethoscope at the first step and a Doppler Phonolyser device at the second step. Afterward, the patient underwent trans-thoracic echocardiography, and the echocardiogram results were compared with the conventional stethoscope as well as the Doppler Phonolyser findings.

RESULTS

Sensitivity of the Doppler Phonolyser for detecting congenital heart defects was 90.5%. The specificity of the Doppler Phonolyser in detecting heart disease was 68.9% in compared with the specificity of the conventional stethoscope, which was 94.8%. Among the most common congenital heart defects in our study population, the sensitivity of the Doppler Phonolyser was 100% for detection of tetralogy of Fallot (TOF); In contrast, sensitivity of both the conventional stethoscope and the Doppler Phonolyser was relatively low for detecting atrial septal defect.

CONCLUSIONS

Doppler Phonolyser could be useful as a diagnostic tool for the detection of congenital heart defects. The main advantages of the Doppler Phonolyser over the conventional stethoscope are no need for operator experience, the ability to distinguish innocent murmurs from the pathologic ones and no effect of environmental sounds on the performance of the device.

Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial

Artificial intelligence (AI) has potential to improve the accuracy of screening for valvular and congenital heart disease by auscultation. However, despite recent advances in signal processing and classification algorithms focused on heart sounds, clinical acceptance of this technology has been limited, in part due to lack of objective performance data. We hypothesized that a heart murmur detection algorithm could be quantitatively and objectively evaluated by virtual clinical trial. All cases from the Johns Hopkins Cardiac Auscultatory Recording Database (CARD) with either a pathologic murmur, an innocent murmur or no murmur were selected. The test algorithm, developed independently of CARD, analyzed each recording using an automated batch processing protocol. 3180 heart sound recordings from 603 outpatient visits were selected from CARD. Algorithm estimation of heart rate was similar to gold standard. Sensitivity and specificity for detection of pathologic cases were 93% (CI 90-95%) and 81% (CI 75-85%), respectively, with accuracy 88% (CI 85-91%). Performance varied according to algorithm certainty measure, age of patient, heart rate, murmur intensity, location of recording on the chest and pathologic diagnosis. This is the first reported comprehensive and objective evaluation of an AI-based murmur detection algorithm to our knowledge. The test algorithm performed well in this virtual clinical trial. This strategy can be used to efficiently compare performance of other algorithms against the same dataset and improve understanding of the potential clinical usefulness of AI-assisted auscultation.

Novel algorithm to screen for heart murmurs using computer-aided auscultation in neonates: a prospective single center pilot observational study

BACKGROUND

Automated detection of heart murmurs with computer-aided auscultation is not yet in clinical routine use. Aim of this study was to test sensitivity and specificity of a novel prototype algorithm in automated detection of heart murmurs from digitally recorded phonocardiograms in neonates admitted at the Neonatal Intensive Care Unit.

METHODS

In a prospective pilot observational study from November 2012 to December 2013 auscultations by pediatricians and computer aided auscultation were performed within 12 hours of neonatal echocardiography. Echocardiography was defined as pathological when resulting in any clinical consequences or causing murmur. Phonocardiograms and auscultation were defined as pathological if a murmur was detected. Phonocardiograms were analyzed offline with a novel algorithm prototype (CSD Labs, Graz, Austria) for detection of murmurs in neonates in a first run and with an optimized algorithm in a second run and were compared with echocardiography. Sensitivity and specificity of auscultation by pediatrician and computer aided auscultation were analyzed.

RESULTS

Thirty-six neonates (gestational age: 36±3 weeks) were included. Twenty-three (64%) neonates had pathological or murmur causing findings in echocardiography (positive echocardiography). Sensitivity and specificity of auscultation by pediatrician were 17% and 100%, respectively. In comparison to auscultation by pediatrician sensitivity of first run and second run were significantly higher with 70% and 83%, respectively. Specificity of first run and second run were 77% and 85%, respectively.

CONCLUSIONS

Phonocardiogram analysis using the novel algorithm prototype had a higher sensitivity than auscultation by pediatrician in detecting positive echocardiography findings in neonates.

In defence of auscultation: a glorious future?

Auscultation of the heart using a simple stethoscope continues to be a central aspect of the cardiovascular examination despite declining proficiency and availability of competing technologies such as hand-held ultrasound. In the ears and mind of a trained cardiologist, heart sounds can provide important information to help screen for certain diseases such as valvar lesions and many congenital defects. Using emerging technology, auscultation is poised to undergo a transformation that will simultaneously improve the teaching and evaluation of this important clinical skill and create a new generation of smart stethoscopes, capable of assisting the clinician in quickly and confidently screening for heart disease. These developments have important implications for global health, screening of athletes and recognition of congenital heart disease.

Automated Identification of Innocent Still's Murmur in Children

OBJECTIVE

Still's murmur is the most common innocent heart murmur in children. It is also the most commonly misdiagnosed murmur, resulting in a high number of unnecessary referrals to pediatric cardiologist. The purpose of this study was to develop a computer algorithm for automated identification of Still's murmur that may help reduce unnecessary referrals.

METHODS

We first developed an accurate segmentation algorithm to locate the first and the second heart sounds. Once these sounds were identified, we extracted signal features specific to Still's murmur. Subsequently, machine learning-based classifiers, artificial neural network and support vector machine, were used to identify Still's murmur.

RESULTS

We evaluated our classifiers using the jackknife method using 87 Still's murmurs and 170 non-Still's murmurs. Our algorithm identified Still's murmur accurately with 84-93% sensitivity and 91-99% specificity.

CONCLUSION

We have achieved accurate automated identification of Still's murmur while minimizing false positives. The performance of our algorithm is comparable to the rate of murmur identification by auscultation by pediatric cardiologists.

SIGNIFICANCE

To our knowledge, our solution is the first murmur classifier that focuses singularly on Still's murmur. Following further refinement and testing, the presented algorithm could reduce the number of children with Still's murmur referred unnecessarily to pediatric cardiologists.

Paediatric heart sound signal analysis towards classification using multifractal spectra

Healthy versus unhealthy heart sound computer-aided classification tools are very popular for supporting clinical decisions. In this paper a new method is proposed for the classification of heart sound recordings from a statistical standpoint without detection and localization of fundamental heart sounds (S1, S2). This study analyzes the possibility of detecting healthy heart sound signal from a large set of measurements, corresponding to different pathologies, such as aortic regurgitation, mitral regurgitation, aortic stenosis and ventricular septal defects. The proposed method employs singularity spectra analysis and long-term dependency of irregular structures. Healthy signals are firstly separated from the rest of the recordings. In the second step, the signals with a click syndrome, used here as a reference, are detected in the unhealthy group. Innocent murmurs have not been considered in this paper. Each auscultatory recording is classified into one of the following classes: healthy; click syndrome; and other heart dysfunctions. The results of the proposed method provided high recall and precision values for each of the three classes. Since the presence of additive noise may affect the classification, we also analyzed the possibility of classifying signals in such circumstances. The method was tested, verified and showed high accuracy.

Use of health information technology to reduce diagnostic errors

BACKGROUND

Health information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.

METHODS

A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.

RESULTS

HIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.

CONCLUSIONS

Diagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

Evaluation of cardiac auscultation skills in pediatric residents

Auscultation skills are in decline, but few studies have shown which specific aspects are most difficult for trainees. We evaluated individual aspects of cardiac auscultation among pediatric residents using recorded heart sounds to determine which elements pose the most difficulty.

METHODS

Auscultation proficiency was assessed among 34 trainees following a pediatric cardiology rotation using an open-set format evaluation module, similar to the actual clinical auscultation description process.

RESULTS

Diagnostic accuracy for distinguishing normal from abnormal cases was 73%. Findings most commonly correctly identified included pathological systolic and diastolic murmurs and widely split second heart sounds. Those least likely to be identified included continuous murmurs and clicks. Accuracy was low for identifying specific diagnoses.

CONCLUSIONS

Given time constraints for clinical skills teaching, this suggests that focusing on distinguishing normal from abnormal heart sounds and murmurs instead of making specific diagnoses may be a more realistic goal for pediatric resident auscultation training.

Automated heartsound analysis/computer-aided auscultation: a cardiologist's perspective and suggestions for future development

Heart disease is a major cause of worldwide morbidity and mortality. Properly performed, the cardiac auscultatory examination (listening to the heart with a stethoscope) is an inexpensive, widely available tool in the detection and management of heart disease. Unfortunately, accurate interpretation of heartsounds by primary care providers is fraught with error, leading to missed diagnosis of disease and/or excessive costs associated with evaluation of normal variants. Therefore, automated heartsound analysis, also known as computer aided auscultation (CAA), has the potential to become a cost-effective screening and diagnostic tool in the primary care setting. A cardiologist's suggestions for CAA system design and algorithmic development are provided.

Utility of store-and-forward pediatric telecardiology evaluation in distinguishing normal from pathologic pediatric heart sounds

Because pediatric cardiologists can accurately diagnose innocent murmurs by physical exam alone, the authors developed a system for remote cardiac auscultation. They hypothesized that their system could accurately classify auscultatory findings as normal/innocent or pathologic. Patients undergoing evaluation underwent examination, echocardiography, and heart sound recording. Pediatric cardiologists evaluated the heart sounds and classified the case as either normal/innocent or pathologic. They reviewed103 heart sound data sets; 85% of the cases were accurately classified as either normal/innocent or pathologic, with a sensitivity of 82% and specificity of 86%. However, when accounting for clinical diagnosis, reviewer uncertainty, and ECG abnormalities, the sensitivity and specificity improved to 91% and 88% (accuracy 89%), respectively. Degree of certainty with the telecardiology diagnosis correlated with correct interpretation (P < .005). Digital heart sound recordings evaluated via telemedicine can distinguish normal/innocent murmurs from pathologic ones. Such a system could improve the use of pediatric cardiology services.

The impact of computer-assisted auscultation on physician referrals of asymptomatic patients with heart murmurs

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.

Cardiovascular symptoms and signs in evaluating cardiac murmurs in children

BACKGROUND

The aim of the present study was to determine the usefulness of cardiovascular symptoms and signs in the recognition of significant congenital heart lesions that required surgical or catheter interventions in different pediatric age groups.

METHODS

A retrospective chart review was carried out of 110 patients with significant heart anomalies that required surgical or catheter interventions (group I) and 113 children, presenting with cardiac murmurs, with congenital heart conditions not requiring any interventions. (group II).

RESULTS

Clinical symptoms or signs were significantly more common in group I than in group II subjects (85% vs 32%, P < 0.0001). The odds of having significant lesions requiring interventions in the presence of either cardiovascular symptoms or signs were 37.5 (95% confidence interval [CI]: 6.5-218.1) for neonates, 14.5 (95%CI: 4.7-51.7) for infants, and 8.0 (95%CI: 3.3-19.2) for children and adolescents. In neonates, the negative predictive values of the absence of symptoms or signs in isolation were relatively low at 64% and 65%, respectively. In children beyond infancy, the positive predictive values of the presence of symptoms or signs in isolation were also low at 62% and 68%, respectively.

CONCLUSIONS

Clinical assessment of cardiovascular symptoms and signs remains useful in the evaluation of the significance of pathological cardiac murmurs in children in the present era of technology. Nonetheless, the predictive values vary with different pediatric age groups.

Computer-aided auscultation of the heart: from anatomy and physiology to diagnostic decision support

There is a clear and present need for computer-aided auscultation of the heart which arises from the highly informative nature of heart sounds, the inherent difficulty of auscultation and increasing pressure in healthcare for rapid, accurate, objective, documented and cost-effective patient evaluation and diagnostic decision making. There are advanced signal processing technologies that hold promise for developing computer-aided auscultation solutions that are intuitive, efficient, informative and accurate. Computer-aided auscultation offers an objective, quantitative and cost-effective tool for acquiring and analyzing heart sounds, providing archival records that support the patient evaluation and referral decision as well as serial comparisons for patient monitoring. There is the further promise of new quantitative acoustic measures and auscultatory findings that have more precise correlation with underlying physiological parameters. These solutions are being developed with the benefits of a rich literature of clinical studies in phonocardiography, the added insights derived from echocardiography, and advances in signal processing technology.

Detection of cardiac pathology: time intervals and spectral analysis

AIM

To develop an objective diagnostic method that facilitates detection of noncyanotic congenital heart diseases.

METHODS

Heart sounds and murmurs were recorded from 60 healthy children and 173 children with noncyanotic congenital heart disease. Time intervals were measured and spectrum of the systolic murmurs analyzed. Stepwise logistic regression analysis was used to distinguish physiological from pathological signals. The receiver operating characteristic (ROC) curve was plotted to show the classification performance of the model and the area under the curve (AUC) was calculated. The probability cut-off points for calculation of sensitivities and specificities were estimated.

RESULTS

The distinguishing variables were the interval from the end of the first heart sound (S(1)) and the beginning of the systolic murmur, respiratory variation of the splitting of the second heart sound, intensity of the systolic murmur, and standard deviation of the interval from the end of the S(1) to the maximum intensity of the murmur. The AUC was 0.95, indicating an excellent classification performance of the model. The sensitivity of 95% and specificity of 72% was achieved at a probability cut-off point of 0.45. Significant cardiac defects were correctly classified.

CONCLUSION

Interval measurements and spectral analysis can be used to confirm significant noncyanotic congenital heart diseases. Further development of the method is necessary to detect also insignificant heart defects.

Phono-spectrographic analysis of heart murmur in children

BACKGROUND

More than 90% of heart murmurs in children are innocent. Frequently the skills of the first examiner are not adequate to differentiate between innocent and pathological murmurs. Our goal was to evaluate the value of a simple and low-cost phonocardiographic recording and analysis system in determining the characteristic features of heart murmurs in children and in distinguishing innocent systolic murmurs from pathological.

METHODS

The system consisting of an electronic stethoscope and a multimedia laptop computer was used for the recording, monitoring and analysis of auscultation findings. The recorded sounds were examined graphically and numerically using combined phono-spectrograms. The data consisted of heart sound recordings from 807 pediatric patients, including 88 normal cases without any murmur, 447 innocent murmurs and 272 pathological murmurs. The phono-spectrographic features of heart murmurs were examined visually and numerically. From this database, 50 innocent vibratory murmurs, 25 innocent ejection murmurs and 50 easily confusable, mildly pathological systolic murmurs were selected to test whether quantitative phono-spectrographic analysis could be used as an accurate screening tool for systolic heart murmurs in children.

RESULTS

The phono-spectrograms of the most common innocent and pathological murmurs were presented as examples of the whole data set. Typically, innocent murmurs had lower frequencies (below 200 Hz) and a frequency spectrum with a more harmonic structure than pathological cases. Quantitative analysis revealed no significant differences in the duration of S1 and S2 or loudness of systolic murmurs between the pathological and physiological systolic murmurs. However, the pathological murmurs included both lower and higher frequencies than the physiological ones (p < 0.001 for both low and high frequency limits). If the systolic murmur contained intensive frequency components of over 200 Hz, or its length accounted for over 80 % of the whole systolic duration, it was considered pathological. Using these criteria, 90 % specificity and 91 % sensitivity in screening were achieved.

CONCLUSION

Phono-spectrographic analysis improves the accuracy of primary heart murmur evaluation and educates inexperienced listener. Using simple quantitative criterias a level of pediatric cardiologist is easily achieved in screening heart murmurs in children.

A Framework for the Analysis of Acoustical Cardiac Signals

Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.

Automated Pediatric Cardiac Auscultation

Most of the relevant and severe congenital cardiac malfunctions can be recognized in the neonatal period of a child's life. Misclassification of a congenital heart defect may have serious consequences on the long-term outcome of the affected child. Experienced cardiologists can usually evaluate heart murmurs with secure confidence, whereas nonspecialists, with less clinical experience, may have more difficulty. There is an acute shortage of physicians in South Africa and many rural clinics are run by nurses. Automated screening based on electronic auscultation at clinic level could therefore be of great benefit. This paper describes an automated artificial neural network as well as a direct ratio and a wavelet analysis technique, to discriminate between pathological and nonpathological heart sounds. To test the performance of the three techniques, auscultation data and electrocardiogram (ECG)-data of 163 patients, aged between 2 mo and 16 yr, were digitized. The neural network achieved a sensitivity and specificity of 90% and 96.5%, respectively, when tested with the Jack-knife method. Statistical analysis of the input to the final sigmoid function shows that a better than 99% sensitivity and specificity can be achieved if sufficient training data are available.

Comparison of two educational interventions on pediatric resident auscultation skills

OBJECTIVE

Multiple cross-sectional physician surveys have documented poor cardiac auscultation skills. We evaluated the impact of 2 different educational interventions on pediatric resident auscultation skills.

METHODS

The auscultation skills of all first-year (PGY1; n = 20) and second-year pediatric residents (PGY2; n = 20) were evaluated at the beginning and end of the academic year. Five patient recordings were presented: atrial septal defect, ventricular septal defect, pulmonary valve stenosis, bicuspid aortic valve with insufficiency, and innocent murmur. Residents were asked to classify the second heart sound, identify a systolic ejection click, describe the murmur, and provide a diagnosis. All PGY1 and most PGY2 (14 of 20) participated on the inpatient cardiology service for 1 month. PGY2 on the cardiology service also attended outpatient clinic. PGY1 did not attend outpatient clinic but were allotted 2 hours/week to use a self-directed cardiac auscultation computer teaching program.

RESULTS

Resident auscultation skills on initial evaluation were dependent on training level (PGY1: 42 +/- 15% correct; PGY2: 53 +/- 13% correct), primarily as a result of better classification of second heart sound (PGY1: 45%; PGY2: 63%) and diagnosis of an innocent murmur (PGY1: 35%; PGY2: 65%). There was no difference in the ability to identify correctly a systolic ejection click (20% vs 23%) or to arrive at the correct diagnosis (35% vs 40%). At the end of the academic year, the PGY1 scores improved by 21%, primarily as a result of improved diagnostic accuracy of the innocent murmur (35% to 65%). PGY2 scores remained unchanged (53% vs 51%), regardless of participation in a cardiology rotation (cardiology rotation: 50%; no cardiology rotation: 51%). Combined, diagnostic accuracy was best for ventricular septal defect (55%) and innocent murmur (60%) and worst for atrial septal defect (18%) and pulmonary valve stenosis (15%). However, 40% identified the innocent murmur as pathologic and 21% of pathologic murmurs were diagnosed as innocent.

CONCLUSIONS

Pediatric resident auscultation skills were poor and did not improve after an outpatient cardiology rotation. Auscultation skills did improve after the use of a self-directed cardiac auscultation teaching program. These data have relevance given the American College of Graduate Medical Education's emphasis on measuring educational outcomes and documenting clinical competencies during residency training.

Cardiac risk factors and participation guidelines for youth sports

Accurate assessment of the cardiac system in pediatric and adolescent youth is important. The hemodynamic demands associated with exercise, training, and sport participation are usually positive and beneficial; however, when an underlying cardiac problem exists, it is imperative that such cardiac problems be identified. Safe sport-related cardiac participation guidelines should be provided for young athletes and their families and coaches. This chapter provides a physician perspective on the recognition and current cardiac management considerations for young athletes participating in both static and dynamic types of sports. The most recent guidelines for hypertension in youth are also provided.