Usefulness of a new sound spectral averaging technique to distinguish an innocent systolic murmur from that of aortic stenosis

Evaluation of a digitally enhanced cardiac auscultation learning method: a controlled study

BACKGROUND

Cardiac auscultation remains an efficient and accessible diagnostic tool, especially in resource-limited countries where modern diagnostic devices like cardiac ultrasound are expensive and difficult to access. However, cardiac auscultation skills of medical students and physicians are declining, mainly because of an ineffective teaching method for this technique. The objective of this study is to evaluate the effect of a digitally enhanced cardiac auscultation learning method on participants' theoretical knowledge and auscultation skills.

METHODS

This will be a controlled study with two parallel arms (1:1). Participants (fourth-year medical students) will be divided into two groups: an intervention group (receiving additional lectures, clinical internship and audio listening sessions) and a control group (receiving additional lectures and clinical internship). At the beginning of the study, all participants will undergo a pre-test that consist of two parts: a knowledge assessment based on multiple-choice questions and a skills assessment based on recognition of cardiac sounds from audio files. Thereafter, three specific additional lectures on cardiac auscultation will be delivered and all participants will take part in their official clinical internship. During these clinical internships (eight weeks), participants of the intervention group will be invited to two listening sessions based on five digital recordings of heart sounds. At the end of the clinical internship, all participants will be invited to a post-test to evaluate their knowledge, skills and satisfaction according to their learning method. The main outcome will be the participants' knowledge progression. The other outcomes will be the participants' skills progression, participants' total progression and satisfaction. Data will be collected and analyzed in per protocol.

DISCUSSION

This study could contribute to the development of a learning method that takes into account the advantages of the conventional method and the contribution of digital technology. Positive results could lead to improved cardiac auscultation skills among health professionals, especially in developing countries.

TRIAL REGISTRATION

The trial is registered on the Pan-African Clinical Trials Registry ( http://www.pactr.org ) under unique identification number: PACTR202001504666847 , registered the 29 November 2019.

Digital phonocardiography of cardiac arrhythmias in dogs - Preliminary experiences

Electronic stethoscopes and digital phonocardiograms (DPCGs) can be applied when diagnosing cardiac murmurs, but their use for cardiac arrhythmias is not described in veterinary medicine. Data of 10 dogs are presented in this preliminary study, demonstrating the applicability of these techniques. Although the number of artefacts and the amount of baseline noise produced by the two digitising systems used did not differ, the Welch Allyn Meditron system or similar ones capable of simultaneous recording of electrocardiograms (ECGs) and DPCGs provide a better option for clinical research and education, whilst the 3M Littmann 3200 system might be more suitable for everyday clinical settings. A combined system with simultaneous phonocardiogram and ECG, especially with wireless transmission, might be a solution in the future.

Cardiac Auscultation in the Modern Era: Premature Requiem or Phoenix Rising?

Competent cardiac auscultation remains a most important skill for the detection of heart disease. Currently it is poorly taught and often ignored or poorly performed, resulting in inaccurate and inefficient patient assessments. This review documents that teaching can be over 90% effective with new, proven teaching methods emphasizing repetition and normal-abnormal comparisons of sounds, using computer-aided and online resources. At present, these concepts are not widely adopted by medical schools. Our current knowledge of teaching heart auscultation is critically reviewed, including traditional bedside, clinic and classroom settings, as well as computer, simulator, and multimedia-based learning. The assessment of auscultation skill in the learning process. The adoption of competence-based learning promises to integrate the assessment of auscultation skill in the learning process. Newer teaching methods, such as auditory training and repetitive listening, offer excellent murmur recognition and diagnosis learning, and hand-held ultrasound is proposed as a helpful adjunct to teaching auscultation. Although ongoing research remains important to develop better teaching methods, the adoption of proven existing concepts has great potential to improve teaching and practice of this valuable skill.

Computer-aided auscultation of murmurs in children: evaluation of commercially available software

BACKGROUND

Heart murmurs are common in children and may represent congenital or acquired cardiac pathology. Auscultation is challenging and many primary-care physicians lack the skill to differentiate innocent from pathologic murmurs. We sought to determine whether computer-aided auscultation (CardioscanTM) identifies which children require referral to a cardiologist.

METHODS

We consecutively enrolled children aged between 0 and 17 years with a murmur, innocent or pathologic, being evaluated in a tertiary-care cardiology clinic. Children being evaluated for the first time and patients with known cardiac pathology were eligible. We excluded children who had undergone cardiac surgery previously or were unable to sit still for auscultation. CardioscanTM auscultation was performed in a quiet room with the subject in the supine position. The sensitivity and specificity of a potentially pathologic murmur designation by CardioscanTM - that is, requiring referral - was determined using echocardiography as the reference standard.

RESULTS

We enrolled 126 subjects (44% female) with a median age of 1.7 years, with 93 (74%) having cardiac pathology. The sensitivity and specificity of a potentially pathologic murmur determination by CardioscanTM for identification of cardiac pathology were 83.9 and 30.3%, respectively, versus 75.0 and 71.4%, respectively, when limited to subjects with a heart rate of 50-120 beats per minute. The combination of a CardioscanTM potentially pathologic murmur designation or an abnormal electrocardiogram improved sensitivity to 93.5%, with no haemodynamically significant lesions missed.

CONCLUSIONS

Sensitivity of CardioscanTM when interpreted in conjunction with an abnormal electrocardiogram was high, although specificity was poor. Re-evaluation of computer-aided auscultation will remain necessary as advances in this technology become available.

A color spectrographic phonocardiography (CSP) applied to the detection and characterization of heart murmurs: preliminary results

Background

Although cardiac auscultation remains important to detect abnormal sounds and murmurs indicative of cardiac pathology, the application of electronic methods remains seldom used in everyday clinical practice. In this report we provide preliminary data showing how the phonocardiogram can be analyzed using color spectrographic techniques and discuss how such information may be of future value for noninvasive cardiac monitoring.

Methods

We digitally recorded the phonocardiogram using a high-speed USB interface and the program Gold Wave http://www.goldwave.com in 55 infants and adults with cardiac structural disease as well as from normal individuals and individuals with innocent murmurs. Color spectrographic analysis of the signal was performed using Spectrogram (Version 16) as a well as custom MATLAB code.

Results

Our preliminary data is presented as a series of seven cases.

Conclusions

We expect the application of spectrographic techniques to phonocardiography to grow substantially as ongoing research demonstrates its utility in various clinical settings. Our evaluation of a simple, low-cost phonocardiographic recording and analysis system to assist in determining the characteristic features of heart murmurs shows promise in helping distinguish innocent systolic murmurs from pathological murmurs in children and is expected to useful in other clinical settings as well.

Sound recording and digital phonocardiography of cardiac murmurs in dogs by using a sensor-based electronic stethoscope

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

A portable graphical representation tool for phonocardiograms

This paper describes a prototype software application to display graphical and editable representations of patient data for use in electronic medical records (EMRs). The application dynamically generates graphics of cardiac and other patient data, and displays or saves them both in graphic and in text formats. The presentation of heart and other data in a consistent, clinically familiar, graphical format is designed to reduce the time necessary for anyone to review and understand this important information. Results of preliminary testing on actual case data are encouraging.

Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique

In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on (Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71-98.9% for THVs=10-90% and 81.2-99.6% for THVs=10-50% with respect to each of SVM modules. With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders). Consequently, the proposed method showed relatively very high classification efficiency if the SVM module is designed with considering THV values. And the proposed cardiac sound murmurs classification method with autoregressive spectral analysis and multi-SVM classifiers is validated for the classification of heart valvular disorders.

Feature extraction for murmur detection based on support vector regression of time-frequency representations

This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.

Assessment of suspected aortic stenosis by auto mutual information analysis of murmurs

Mild sclerotic thickening of the aortic valve affects 25% of the population, and the condition causes aortic valve stenosis (AS) in 2% of adults above 65 years. Echocardiography is today the clinical standard for assessing AS. However, a cost effective and uncomplicated technique that can be used for decision support in the primary health care would be of great value. In this study, recorded phonocardiographic signals were analyzed using the first local minimum of the auto mutual information (AMI) function. The AMI method measures the complexity in the sound signal, which is related to the amount of turbulence in the blood flow and thus to the severity of the stenosis. Two previously developed phonocardiographic methods for assessing AS severity were used for comparison, the murmur energy ratio and the sound spectral averaging technique. Twenty-nine patients with suspected AS were examined with Doppler echocardiography. The aortic jet velocity was used as a reference of AS severity, and it was found to correlate with the AMI method, the murmur energy ratio and the sound spectral averaging technique with the correlation coefficient R = 0.82, R = 0.73 and R = 0.76, respectively.

Time-frequency and complexity analyses for differentiation of physiologic murmurs from heart murmurs caused by aortic stenosis in Boxers

OBJECTIVE

To investigate whether time-frequency and complexity analyses of heart murmurs can be used to differentiate physiologic murmurs from murmurs caused by aortic stenosis (AS) in Boxers.

ANIMALS

27 Boxers with murmurs.

PROCEDURES

Dogs were evaluated via auscultation and echocardiography. Analyses of time-frequency properties (TFPs; ie, maximal murmur frequency and duration of murmur frequency > 200 Hz) and correlation dimension (T(2)) of murmurs were performed on phonocardiographic sound data. Time-frequency property and T(2) analyses of low-intensity murmurs in 16 dogs without AS were performed at 7 weeks and 12 months of age. Additionally, TFP and T(2) analyses were performed on data obtained from 11 adult AS-affected dogs with murmurs.

RESULTS

In dogs with low-intensity murmurs, TFP or T(2) values at 7 weeks and 12 months did not differ significantly. For differentiation of physiologic murmurs from murmurs caused by mild AS, duration of murmur frequency > 200 Hz was useful and the combination assessment of duration of frequency > 200 Hz and T(2) of the murmur had a sensitivity of 94% and a specificity of 82%. Maximal murmur frequency did not differentiate dogs with AS from those without AS.

CONCLUSIONS AND CLINICAL RELEVANCE

Results suggested that assessment of the duration of murmur frequency > 200 Hz can be used to distinguish physiologic heart murmurs from murmurs caused by mild AS in Boxers. Combination of this analysis with T(2) analysis may be a useful complementary method for diagnostic assessment of cardiovascular function in dogs.

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.

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.

Using voice-recognition technology to eliminate cardiac cycle segmentation in automated heart sound diagnosis

Advanced digital signal processing has the potential to revolutionize the stethoscope through the use of mathematical algorithms to interpret heart sound acoustic information. In this study, a novel classification algorithm that does not require cardiac cycle segmentation was used for identifying differences between normal and diseased heart sounds. The heart sound signals were not separated into systole and diastole. A recordable electronic stethoscope was used to record the heart sounds of 163 echocardiogram patients. Mel-cepstrum and Principal Components Analysis were applied to the 60 recorded heart sounds and decision spaces were developed. The algorithm was tested using 100 novel patients. The specificity of the algorithm is 72.4% and the sensitivity is 63.4%.

Cardiac auscultation: a glorious past--and it does have a future!

Cardiac auscultation remains an important part of clinical medicine. The standard acoustic stethoscope, which has been useful for more than a century, cannot process, store, and play back sounds or provide visual display, and teaching is hindered because there is no means to distribute the same sounds simultaneously to more than one listener. Modern portable and inexpensive tools are now available to provide, through digital electronic means, better sound quality with visual display and the ability to replay sounds of interest at either full or half speed with no loss of frequency representation or sound quality. Visual display is possible in both standard waveform and spectral formats. The latter format is readily available and provides certain advantages over the time-honored waveform (phonocardiographic) method. Both methods, however, can and should be used simultaneously. Sound signals obtained electronically may then be subjected to objective visual and numerical analysis, transmitted to distant sites, and stored in medical records. Signal analysis shows early promise for clinical application, such as in the assessment of severity of aortic stenosis and in the separation of innocent from organic murmurs. In addition to their clinical value, these methods provide a critical vehicle for the teaching of cardiac auscultation, a method that can and should be preserved for future generations.