Acoustic Heart Sound Recording and Computer Analysis

Number: 0692

Table Of Contents

Applicable CPT / HCPCS / ICD-10 Codes


Scope of Policy

This Clinical Policy Bulletin addresses the following: auscultation jacket, ballistocardiography, optical vibrocardiography, phonocardiography,  vectorcardiography, and acoustic heart sound recording and computer analysis.

  1. Experimental and Investigational

    Aetna considers the following interventions experimental and investigational because the effectiveness of these approaches has not been established:

    1. Acoustic heart sound recording, computer analysis and interpretation because of a lack of clinical studies demonstrating that this technology improves clinical outcomes;
    2. Auscultation jacket, ballistocardiography, optical vibrocardiography, phonocardiography, and vectorcardiography because their clinical value has not been established.


CPT Codes / HCPCS Codes / ICD-10 Codes

Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

HCPCS codes not covered for indications listed in the CPB:

S3902 Ballistocardiogram

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

I00 - I99.9 Diseases of the circulatory system
R00.8 - R00.9 Other and unspecified abnormalities of heart beat
R01.0 - R01.2 Cardiac murmurs and other cardiac sounds
R09.89 Other specified symptoms and signs involving the circulatory and respiratory systems
R93.1 Abnormal findings on diagnostic imaging of heart and coronary circulation
R93.8 Abnormal findings on diagnostic imaging of other specified body structures
R94.30 - R94.39 Abnormal results of cardiovascular function studies
Z13.6 Encounter for screening for cardiovascular disorders


Computer-Aided Electronic Auscultatory Devices

Computer-aided electronic auscultatory devices have been developed that acquire, record, and analyze the acoustic signals of the heart.  Using acoustic signal processing algorithms, a computer analyzes the recording to identify specific heart sounds that may be present, including S1, S2 and suspected murmurs.   A graphic user interface displays the results.  These computer-aided electronic auscultatory devices are intended to provide support to the physician in the evaluation of heart sounds for the identification of suspected murmurs, a potential sign of heart disease. 

Zargis Medical Corporation (Princeton, NJ) has received U.S. Food and Drug Administration (FDA) clearance to market Zargis Acoustic Cardioscan, a computer-aided electronic auscultatory device intended to support physicians in analyzing heart sounds in patients.  As this clearance was based on a 510(k) premarket notification, the manufacturer was not required to provide the evidence of efficacy that is necessary to support a premarket approval application (PMA).  The FDA indications for use states that the device is not intended as a sole means of diagnosis and that interpretation of heart sounds with the Zargis Acoustic Cardioscan are only significant when used in conjunction with physician over-read as well as consideration of all other patient data.

There is inadequate evidence of the validity of computer-aided electronic auscultatory devices, or their impact on clinical outcomes in the peer-reviewed published medical literature.  Clinical studies are necessary to determine the performance characteristics (sensitivity, specificity, and predictive values) of computer-aided electronic auscultatory devices and their impact on clinical management and patient outcomes.

Another brand of computer-aided electronic auscultatory device (correlated audioelectric cardiography device) is the Audicor System (Inovise Medical, Inc., Portland, OR).  It received marketing clearance from the FDA through the 510(k) process in 2003.  According to the product labeling, the Audicor Upgrade System, when used with Audicor Sensors in the V3 and V4 positions on the chest wall, is for use in attaining, analyzing and reporting electrocardiographic and phonocardiographic data, and to provide interpretation of the data for consideration by physicians.

Mansy et al (2005) found that computerized analysis of vascular sounds may be useful in vessel patency surveillance.  Moreover, they stated that further testing using longitudinal studies may be warranted.

Watrous and colleagues (2008) noted that as many as 50 % to 70 % of asymptomatic children referred for specialist evaluation or echocardiography because of a murmur have no heart disease.  These researchers hypothesized that computer-assisted auscultation (CAA) can improve the sensitivity and specificity of referrals for evaluation of heart murmurs.  In this study, 7 board-certified primary care physicians were evaluated both with and without the use of a computer-based decision-support system using 100 pre-recorded patient heart sounds (55 innocent murmurs, 30 pathological murmurs, and 15 without murmur).  The sensitivity and specificity of their murmur referral decisions relative to American College of Cardiology/American Heart Association guidelines, and sensitivity and specificity of murmur detection and characterization (innocent versus pathological) were measured.  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).  The authors concluded that CAA appears to be a promising new technology for informing the referral decisions of primary care physicians.

Computer-aided electronic auscultatory devices differ from phonocardiograms in that only the former incorporate computer analysis of the heart sounds.  The Center for Medicare and Medicare Services (CMS, 1997) has determined that phonocardiography and vectorcardiography diagnostic tests are "outmoded and of little clinical value."

Luciani and colleagues (2019) noted that acoustic cardiography (AC) is a hybrid technique that couples heart sounds recording with ECG providing insights into electrical-mechanical activity of the heart in an unsupervised, non-invasive and inexpensive manner.  During myocardial ischemia hemodynamic abnormalities appear in the first minutes and these researchers hypothesized a putative diagnostic role of acoustic cardiography for prompt detection of cardiac dysfunction for future patient management improvement.  A total of 10 female Swiss large white pigs underwent permanent distal coronary occlusion as a model of acute myocardial ischemia.  Acoustic cardiography analyses were performed prior, during and after coronary occlusion.  Pressure-volume analysis was conducted in parallel as an invasive method of hemodynamic assessment for comparison.  Similar systolic and diastolic intervals obtained with the 2 techniques were significantly correlated [Q to min dP/dt versus Q to second heart sound (r2 = 0.9583, p < 0.0001), PV diastolic filling time versus AC perfusion time (r2 = 0.9686, p < 0.0001)].  Indexes of systolic and diastolic impairment correlated with quantifiable features of heart sounds [Tau versus fourth heart sound Display Value (r2 = 0.2721, p < 0.0001) cardiac output versus third heart sound Display Value (r2 = 0.0791 p = 0.0023)].  Furthermore, acoustic cardiography diastolic time (AUC 0.675, p = 0.008), perfusion time (AUC 0.649, p = 0.024) and third heart sound Display Value (AUC 0.654, p = 0.019) emerged as possible indicators of coronary occlusion.  Finally, these 3 parameters, when joined with HR into a composite joint-index, represent the best model in these investigators’ experience for ischemia detection (AUC 0.770, p < 0.001).  The authors concluded that in the rapidly evolving setting of acute myocardial ischemia, acoustic cardiography provided meaningful insights of mechanical dysfunction in a prompt and non-invasive manner.  They stated that these findings should propel interest in resurrecting this non-invasive technique for future translational studies as well as reconsidering its re-introduction in the clinical setting.

The authors stated that this study had several drawbacks.  These researchers presented a pre-clinical study with a relatively small cohort (n = 10) composed of healthy pigs under general anesthesia.  They noted that despite the afore-mentioned limitations, their experience shed light on putative immediate mechanical alterations that would be otherwise infeasible to replicate in a real-world clinical scenario and it might represent a reference point for future studies involving acoustic cardiography with more complex and heterogenous conditions.


Ballistocardiography refers to the recording of movements of the body caused by cardiac contractions and associated blood flow.  It had been investigated for potential use in measuring cardiac output (CO) and other aspects of cardiac function.  Taylor and Sheffer (1990) stated, however, that thermodilution is the preferred method of measuring CO, and has achieved universal appeal in the clinical setting.  The ballistocardiogram and other means of detecting CO, such as the impedance-cardiogram, have been developed and are being used clinically, but the development of the flow-directed thermodilution catheter has profoundly affected the universal acceptance of the thermodilution method (Taylor and Sheffer, 1990).  Thermodilution techniques, when performed properly, are capable of obtaining accurate and reproducible results.

McKay et al (1999) described a CO measurement using a new method to derive and analyze the long-axis ballistocardiogram that is less invasive than pulmonary artery thermodilution.  A total of 39 patients in sinus rhythm with pulmonary artery thermodilution catheters or radial artery catheters in place were studied.  The first 30 subjects were the "learning set" and the next 9 were the "test set".  A small (54-g) accelerometer was taped on the patient's chest.  Outcome measures were time and amplitude coordinates of the acceleration and radial artery pressure wave-peaks, as well as anthropometric information.  A stroke volume prediction equation was generated (R2 = 0.76) from the learning set.  This equation was applied to the test set and correlated with the pulmonary artery thermodilution-derived stroke volumes (R = 0.79).  Stroke volumes were compared using a previously described statistical method:
  1. bias (predicted greater than thermodilution) = 0.03 ml (95 % confidence interval [CI]: -4.2 to 4.8 ml);
  2. lower limit of agreement = -21 ml (95 % CI: -29 to -13 ml); and
  3. upper limit of agreement = 22 ml (95 % CI: 14 to 29 ml).

Of derived stroke volumes, 82 % were within 15 ml of pulmonary artery thermodilution-derived values.  The authors concluded that sternal acceleration ballistocardiogram combined with hemodynamic and demographic data in a probabilistic model shows promise of providing a less invasive measure of CO than thermodilution.

Morra and colleagues (2019) examined if micro-accelerometers and gyroscopes may provide useful information for the detection of breathing disturbances in further studies.  A total of 43 healthy volunteers performed a 10 s end-expiratory breath-hold, while ballistocardiography (BCG) and seismocardiography (SCG) determined changes in kinetic energy and its integral over time (iK, J · s).  BCG measures overall body accelerations in response to blood mass ejection into the main vasculature at each cardiac cycle, while SCG records local chest wall vibrations generated beat-by-beat by myocardial activity.  This minimally intrusive technology evaluates linear accelerations and angular velocities in 12 degrees of freedom to calculate iK during the whole cardiac cycle; iK produced during systole and diastole were also computed.  The iK during normal breathing was 87.1 [63.3; 132.8] µJ · s for the SCG and 4.5 [3.3; 6.2] µJ · s for the BCG.  Both increased to 107.1 [69.0; 162.0] µJ · s and 6.1 [4.4; 9.0] µJ · s, respectively, during breath-holding (p = 0.003 and p < 0.0001, respectively).  The iK of the SCG further increased during spontaneous respiration following apnea (from 107.1 [69.0; 162.0] µJ · s to 160.0 [96.3; 207.3] µJ · s, p < 0.0001).  The ratio between the iK of diastole and systole increased from 0.35 [0.24; 0.45] during apnea to 0.49 [0.31; 0.80] (p < 0.0001) during the restoration of respiration.  The authors concluded that a brief voluntary apnea generated large and distinct increases in SCG and BCG waveforms.  These researchers stated that iK monitoring during sleep may prove useful for the detection of respiratory disturbances.

Morra and associates (2020) examined if modern BCG and SCG are useful in the detection of hemodynamic changes occurring during simulated obstructive apneic events.  A total of 47 healthy volunteers performed a voluntary maximum Mueller maneuver (MM) for 10 s, and BCG and SCG signals were simultaneously taken. The kinetic energy of a set of cardiac cycles before and during the apneic episode was automatically computed from the rotational and linear channels of the SCG and BCG waveforms and iK was derived (unit of measure: microjoules per second (µJ·s)).  The estimated transmural pressure (eP TM ) was assessed as the difference between systemic blood pressure (BP) and maximal inspiratory pressure (MIP).  The Wilcoxon sign-rank test was used to evaluate differences in energy measurements between normal respiration and the loaded inspiration maneuver.  Cardiac kinetic energies and the MIP produced during the MM were compared by linear regression analysis following log transformation in order to examine the correlation between variables.  The [Formula: see text] during normal breathing increased from 1.1(0.8; 1.4) to 1.9(1.4; 4.3) µJ·s during MM (p < 0.001).  When eP TM was considered, this association became positive (r: +0.58, p < 0.001 and r:+0.60, p < 0.001.  When the iKLIN was considered as the comparative factor, correlations with the MIP and eP TM were weak and insignificant.  Men had higher values of iK than women.  The authors concluded that simulated obstructive apnea elicited large rotational iK swings, which were related to the intensity of the inspiratory effort and, as such, to the intensity of the left ventricular after-load.  These researchers stated that computation of cardiac kinetic energy through BCG and SCG may shed further light on the impact of obstructive respiratory events on the cardiovascular system.

Zaid et al (2021) noted that the time interval between the peaks in the ECG and BCG waveforms, thoracic electrical bioimpedance (TEB), has been associated with the pre-ejection period (PEP), which is an important marker of ventricular contractility.  However, the use of BCG-related markers in clinical practice is limited by the difficulty to obtain a replicable and consistent signal on patients.  These researchers examined the feasibility of BCG measurements within a complex clinical setting, by means of an accelerometer under the head pillow of patients admitted to the Surgical Intensive Care Unit (SICU).  The proposed technique proved capable of capturing TEB based on the R peaks in the ECG and the BCG in its head-to-toe and dorso- ventral directions.  TEB detection was found to be consistent and repeatable both in healthy individuals and SICU patients over multiple data acquisition sessions.  The authors concluded that this work provided a promising starting point to examine how TEB changes may relate to the patients' complex health conditions and give additional clinical insight into their care needs.

Sumali et al (20220 stated that cardiovascular disease (CVD) is the number one cause of death globally.  In the case of cardiopulmonary arrest due to myocardial infarction (MI), the survival rate is as low as 13.3 % 1 month after resuscitation, which necessitated the need for continuous heart monitoring.  These researchers develop a BCG measurement system using a load cell installed on a chair and a HR estimation algorithm that is robust to waveform changes, with the objective of constructing a non-contact HR acquisition system.  The proposed system was examined by employing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG.  The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases.  As a result of comparing the HR intervals estimated from BCG and those obtained from ECG, it was confirmed that the same HR variability (HRV) features could be obtained even for abnormal ECG subject. 

These researchers stated that there are several suggestions for future investigation.  First, larger sample size with varying age distribution, to validate the effectiveness of the results of this study for more generalized sample size.  Second, larger sample size of patients with various CVDs is needed to confirm the effectiveness of the proposed system for non-healthy subjects.  Third, the use of signal fusion technologies instead of signal selection might improve the overall performance of the system.  Fourth, the use of machine-learning (ML) models specialized for peak detection might also improve the performance; as in this study, convolutional neural network (CNN) improves the system’s effectiveness compared to the conventional studies using conventional peak detection algorithm.

Rajput et al (2022) noted that managing hypertension (HPT) remains a significant challenge.  Despite advancements in BP-measuring systems and the accessibility of safe and effective anti-hypertensive medicines, HPT is a major public health concern.  Headaches, dizziness and fainting are common symptoms of HPT . In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24-hour ambulatory BP monitoring.  This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently.  More importantly, uncontrolled HPT can result in severe complications (stroke, heart attack, kidney disease, and heart failure [HF]), mainly ignoring the signs in nascent stages.  HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals.  Hence, BCG signal was used in this study to detect HPT automatically.  The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D 2D-CNN model, which was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes.  The authors concluded that the proposed model obtained a high classification accuracy of 86.14 % with a 10-fold cross-validation (CV) strategy.  Hence, this was the 1st use of a 2D-CNN model (deep-learning algorithm) to detect HPT using BCG signals.  Moreover, these researchers stated that in the future, they will validate the developed model with more data belonging to different stages (mild, moderate, and severe) of HPT from different races.

The authors stated that this study had 2 main drawbacks.  First, the BCG signal collection using a smart.  To collect BCG signals, the subject must be continually lying on the mattress.  This method could not examine a subject’s physiological state while performing routine activities; and obtaining high quality signals proves a problem.  To overcome this problem, the inclusion of other smart monitoring devices is suggested, some of which may include smart watches or smart chairs to obtain signals during daytime activities.  Second, CNN takes a long duration to train a large dataset and generally requires a graphics processing unit (GPU) to accelerate the training process.  As a result, this increased the cost and complexity of the model.

Steffensen et al (2023) stated that BCG features are of interest in wearable cardiovascular monitoring of cardiac performance.  These investigators examined feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for CVD.  In addition, they investigated the relationship between PTT, BP, and stroke volume (SV) during exercise and posture interventions.  A total of 25 subjects underwent a bike exercise protocol with 4 incremental workloads (0 W, 50 W, 100 W, and 150 W) in supine and semi-recumbent postures.  BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and ECG were recorded.  Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT.  These researchers tested for significance using Wilcoxon signed-rank test.  BCG was successfully recorded at the wrist during exercise.  PTT exhibited a moderate negative correlation with systolic BP (ρSup = -0.65, ρSR = -0.57, ρAll = -0.54).  PTT differences between supine and semi-recumbent conditions were significant at 0 W and 50 W (p < 0.001), less at 100 W (p = 0.0135) and 150 W (p = 0.031).  Systolic BP and diastolic BP were lower in semi-recumbent posture (p < 0.01), while HR was slightly higher.  ECG confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74).  The authors concluded that wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

The authors stated that a drawback of this study was the use of steady state exercise.  Steady exercise conditions allow multiple heart beats to be averaged, mitigating signal quality issues to a degree.  However, this approach removes some of the variance in the averaged data (e.g., breathing cycles) that may be of interest.  Furthermore, the steady states examined in this trial may not be directly comparable to many uncontrolled exercise conditions where exertion levels might vary more dynamically.  Application in more realistic exercise situations requires further investigation in instrumentation as well as in signal processing to handle motion artifacts and enable the use of shorter ensemble windows.  The sample size of this trial was small, and extrapolation to larger populations may also be complicated by the homogenous make-up of the participant group.  In addition, these investigators noted that the 4 exercise load levels, set to absolute levels of delivered work, allowed for intra-individual comparison across a linearly increasing load; however, individual response at each level will vary significantly.  In the absence of quantitative measures of perceived exertion, i.e., Borg scale ratings, inter-individual comparisons must be made with caution.

Feng et al (2023) noted that under the influence of COVID-19 and the in-hospital cost, the in-home detection of CVD with smart sensing devices is becoming more popular recently.  In the presence of the qualified signals, BCG cannot only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner.  However, for the potential HF patients, the additional quality ECG-aided assessment requires more procedures and brings the inconvenience to their in-home HF diagnosis.  To enable the detection of HF in many real applications, these researchers proposed a ML-aided scheme for HF detection, where the BCG signals recorded from the force sensor were used without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems.  Lastly, the effectiveness of the proposed HF detection scheme was verified in comparative experiments.  First, a piezoelectric sensor was employed to record a signal sequence of the two-dimensional (2D) vital sign, which included the BCG and the respiratory effort.  Then, the linear and the non-linear features with respect to BCG and respiratory effort signals were extracted to serve the HF detection.  Finally, the improved HF detection performance was verified via the leave-one-out (LOO) and the leave-one-subject-out (LOSO) cross-validation settings with different ML classifiers.  The proposed ML-aided scheme achieved the robust performance in HF detection by using 4 different classifiers; and yielded an accuracy of 94.97 % and 87.00 % in the LOO and the LOSO experiments, respectively.  Furthermore, experimental results showed that the designed respiratory and cardiopulmonary features were beneficial to HF detection (LVEF less than or equal to 49 %).  The authors concluded that comparing with existing studies focusing on the BCG signals, the proposed scheme fully exploited the relationship between the heart and the lung systems.  The experiment results verified that these features could significantly improve the accuracy performance and the robustness of HF detection.  These researchers stated that in the further step of this study, quantitative analysis for possible classification between HF patients with LVEF less than or equal to 40 % and LVEF greater than 40 % will be considered.  They noted that It is thus expected that the proposed scheme has the potential for patients with limited mobility performing in-home HF detection.

Optical Vibrocardiography

Optical vibrocardiography (VCG) is a novel, non-contact, tool for monitoring cardiac activity.  Morbiducci et al (2007) described the use of VCG for heart rate (HR) monitoring, based on the measurement of chest wall movements induced by the pumping action of the heart, which is eligible as a surrogate of electrocardiogram (ECG) in assessing both cardiac rate and HR variability (HRV).  The method is based on the optical recording of the movements of the chest wall by means of laser Doppler interferometry.  To this aim, the ECG signal and the velocity of vibration of the chest wall, named VCG, were simultaneously recorded on 10 subjects.  The time series built from the sequences of consecutive R waves (on ECG) and vibrocardiographic (VV) intervals were compared in terms of HR.  To evaluate the ability of VCG signals as quantitative marker of the autonomic activity, HRV descriptors were also calculated on both ECG and VCG time series.  Heart rate and HRV indices obtained from the proposed method agreed with the rate derived from ECG recordings (mean percent difference less than 3.1 %).  The authors concluded that optical VCG provides a reliable assessment of HR and HRV analysis, with no statistical differences in term of gender are present.  They noted that optical VCG appears promising as non-contact method to monitor the cardiac activity under specific conditions (e.g., in magnetic resonance environment, or to reduce exposure risks to workers subjected to hazardous conditions).  The technique may be used also to monitor subjects (e.g., severely burned, for which contact with the skin needs to be minimized).

Mahnke (2009) noted that heart disease is a major cause of worldwide morbidity and mortality.  Properly performed, the cardiac auscultatory examination is an inexpensive, widely available tool in the detection and management of heart disease.  Unfortunately, accurate interpretation of heart sounds by primary care providers is fraught with error, leading to missed diagnosis of disease and/or excessive costs associated with evaluation of normal variants.  Thus, automated heart sound analysis, also known as CAA, has the potential to become a cost-effective screening and diagnostic tool in the primary care setting.

Auscultation Jacket

Visagie and colleagues (2009) presented the findings of a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities.  The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for under-served locations, for instance in rural areas or in developing countries where patients far out-number the available medical personnel.  Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies.  Electrocardiograms were also recorded simultaneously with phonocardiographic data.  Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network.  The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84 % and a specificity of 86 %.  Although the number of training and testing samples presented were limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings.  The results of this study showed the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.


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