Voice reduction in cardiac auscultation sounds with reference signals measured from vocal resonators.

This study proposes the use of vocal resonators to enhance cardiac auscultation signals and evaluates their performance for voice-noise suppression. Data were collected using two electronic stethoscopes while each study subject was talking. One collected auscultation signal from the chest while the other collected voice signals from one of the three voice resonators (cheek, back of the neck, and shoulder). The spectral subtraction method was applied to the signals. Both objective and subjective metrics were used to evaluate the quality of enhanced signals and to investigate the most effective vocal resonator for noise suppression. Our preliminary findings showed a significant improvement after enhancement and demonstrated the efficacy of vocal resonators. A listening survey was conducted with thirteen physicians to evaluate the quality of enhanced signals, and they have received significantly better scores regarding the sound quality than their original signals. The shoulder resonator group demonstrated significantly better sound quality than the cheek group when reducing voice sound in cardiac auscultation signals. The suggested method has the potential to be used for the development of an electronic stethoscope with a robust noise removal function. Significant clinical benefits are expected from the expedited preliminary diagnostic procedure.

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals.

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject's thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1-S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland-Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

Hemodynamics-driven mathematical model of first and second heart sound generation.

NEW & NOTEWORTHY: To the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.

Novel Design of a Multimodal Technology-Based Smart Stethoscope for Personal Cardiovascular Health Monitoring.

Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user's chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users' muscle fatigue variation.

Simple Phonocardiography with the Ankle-Brachial Index Measurement System as a Tool for Detecting Fourth Heart Sounds of Hypertrophic Cardiomyopathy.

Conventional phonocardiography is useful for objective assessment of cardiac auscultation, but its availability is limited. More recently, an ankle-brachial index (ABI) measurement system equipped with simple phonocardiography has become widely used for diagnosing peripheral artery disease, however, whether this simple phonocardiography can be an alternative to conventional phonocardiography remains unclear.This retrospective study consisted of 48 patients with hypertrophic cardiomyopathy (HCM) and 107 controls. The presence of the fourth sound (S4) was assessed by conventional phonocardiography, in addition to apexcardiography and auscultation, in all patients with HCM. S4 was also estimated by the ABI measurement system with the phonocardiographic microphone on the sternum (the standard method) or at the apex (the apex method) in HCM patients and controls.S4 on conventional phonocardiography was detected in 42 of 48 patients (88%) with HCM. Auscultation for the detection of S4 had a sensitivity of 0.78, specificity of 0.57, and accuracy of 0.75. These diagnostic values were generally superior to those of the standard method using the ABI measurement system, whereas the apex method using the ABI measurement system had better diagnostic values, with an excellent specificity of 1.0, sensitivity of 0.77, and accuracy of 0.80. No significant differences were observed in low ABI defined as < 0.9.Simple phonocardiography equipped with the ABI measurement system may be an alternative to conventional phonocardiography for the detection of S4 in patients with HCM when the phonocardiographic microphone is moved from the sternum to the apex.

Ambulatory Monitoring of Heart Sounds via an Implanted Device Is Superior to Auscultation for Prediction of Heart Failure Events.

BACKGROUND: We compared the relationship between the third heart sound (S3) measured by an implantable cardiac device (devS3) and auscultation (ausS3) and evaluated their prognostic powers for predicting heart failure events (HFEs). METHODS AND RESULTS: In the MultiSENSE study, devS3 was measured daily with continuous values, whereas ausS3 was assessed at study visits with discrete grades. They were compared among patients with and without HFEs at baseline and against each other directly. Cox proportional hazard models were developed between follow-up visits and over the whole study. Simulations were performed on devS3 to match the limitations of auscultation. We studied 900 patients, of whom 106 patients experienced 192 HFEs. Two S3 sensing modalities correlated with each other, but at baseline, only devS3 differentiated patients with or without HFEs (P < 0.0001). The prognostic power of devS3 was superior to that of ausS3 both between follow-up visits (HR = 5.7, P < 0.0001, and 1.7, P = 0.047, respectively) and over the whole study (HR = 2.9, P < 0.0001, and 1.4, P = 0.216, respectively). Simulation results suggested this superiority may be attributed to continuous monitoring and to subaudible measuring capability. CONCLUSIONS: S3 measured by implantable cardiac devices has stronger prognostic power to predict episodes of future HFEs than that of auscultation.

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions.

This paper proposes a robust and real-time capable algorithm for classification of the firstand second heart sounds. The classification algorithm is based on the evaluation of the envelope curveof the phonocardiogram. For the evaluation, in contrast to other studies, measurements on twelveprobands were conducted in different physiological conditions. Moreover, for each measurement theauscultation point, posture and physical stress were varied. The proposed envelope-based algorithmis tested with two different methods for envelope curve extraction: the Hilbert transform andthe short-time Fourier transform. The performance of the classification of the first heart soundsis evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform,the algorithm has a better performance regarding the F1-score and computational effort. Theproposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5 %.The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (exceptmeasurements on the back) of the subjects. The ECG and PCG records are available from the authors.

Noninvasive Determination of Blood Pressure by Heart Sound Analysis Compared With Intra-Arterial Monitoring in Critically Ill Children-A Pilot Study of a Novel Approach.

OBJECTIVES: To develop a novel device to predict systolic and diastolic blood pressure based on measured heart sound signals and evaluate its accuracy in comparison to intra-arterial blood pressure readings. STUDY DESIGN: Prospective, observational pilot study. SETTING: PICU. PATIENTS: Critically ill children (0-18 yr) undergoing continuous blood pressure monitoring via radial artery intra-arterial catheters were enrolled in the study after informed consent. The study included medical, cardiac, and surgical PICU patients. INTERVENTIONS: Along with intra-arterial blood pressure, patient's heart sounds were recorded simultaneously by a highly sensitive sensor taped to the chest. Additional hardware included a data acquisition unit and laptop computer. Subsequently, advanced signal processing technologies were used to minimize random interfering signals and extract and separate S1 and S2 signals. A computerized model was then developed using artificial neural network systems to estimate blood pressure from the extracted heart sound analysis. MEASUREMENTS AND MAIN OUTCOMES: We found a statistically significant correlation for systolic (r = 0.964; R = 0.928) and diastolic (r = 0.935; R = 0.868) blood pressure readings (n = 491) estimated by the novel heart-sound signal-based method and those recorded by intra-arterial catheters. The mean difference of the individually paired determinations of the blood pressure between the heart-sound-based method and intra-arterial catheters was 0.6 ± 7 mm Hg for systolic blood pressure and -0.06 ± 5 mm Hg for diastolic blood pressure, which was within the recommended range of 5 ± 8 mm Hg for any new blood pressure devices. CONCLUSIONS: Our findings provide proof of concept that the heart-sound signal-based method can provide accurate, noninvasive blood pressure monitoring.

Real-World Evaluation of the Eko Electronic Teleauscultation System.

Heart murmur evaluation is the most common cause of referral to cardiology, and auscultation of heart sounds with a stethoscope remains a key component of the initial cardiovascular exam. Adoption of telecardiology has been limited by challenges in teleauscultation. We set out to compare in-person auscultatory findings with heart sounds recorded by the Core stethoscope (Eko, Berkeley, CA) in patients with normal heart sounds, innocent heart murmurs, and a variety of pathologic findings. Our study demonstrates that Eko recordings had a high percent of agreement with in-person auscultation findings and echocardiogram findings, with moderate inter-rater reliability. It was useful in identifying patients with pathologic murmurs who would benefit from further assessment. It was able to discern major types of pathological murmurs. Certain qualitative differences in the recorded sounds as compared to in-person auscultation were identified by the reading cardiologists. They were able to acclimate to these subtle differences. The system was felt to be easy to use, and most cardiologists in the study would consider using it in clinical settings. The Eko Core system may be a useful screening tool for murmur evaluation.

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring.

One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient's heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

The Conceptual Modeling of Interoperability Framework of Heart Sound Monitor in the Context of an Interoperable End-to-End Architecture.

Background: Heart sound monitor (HSM), a device suitable for home-use, can be used to acquire heart sounds. It enables the telemonitoring of cardiac function, which has been largely evolved and widely used in recent years. Nevertheless, the designers paid little attention to the consistency of information model and data interaction of HSM, thus the data could not be shared and aggregated among healthcare systems. Consequently, the device's development and its application in person-centered telehealth are hindered. Objective: To solve this problem and to build interoperability for HSM, this article proposes a HSM interoperability framework that is constructed by using standardized modeling methods. Methods: The authors collected the common device-output information of HSM involved in telemonitoring, leveraged the standardized interoperability framework defined in ISO/IEEE 11073 Personal Health Device (11073-PHD) standards to model the static data structure and dynamic interaction behaviors of HSM. Results: Via a meta-analysis, the HSM device-output information includes collected data (heart sound measurement), and derived data (e.g., device status). Based on such information, an 11073-PHD-compliant domain information model has been successfully created. This enables the interoperability between HSM and aggregation device, allowing inter-device plug-and-play using the service model and communication model. A prototype of this design has been implemented and validated via Continua Enabling Software Library. Conclusions: The ISO/IEEE 11073-PHD standard framework has the potential to accommodate the HSM, which implicate HSM can be integrated into the interoperable ecosystem to achieve holistic health solution. Findings in this article may be taken as a reference for standard developing organizations to establish a standardized interoperability framework for HSM.

An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provided in this paper. Firstly, the improved wavelet denoising method was used for signal preprocessing. Then, the logistic regression based hidden semi-Markov model algorithm was utilized to locate the boundary of the first HS and the second HS, therefore, the ratio of diastolic to systolic duration can be calculated. Eleven features were extracted based on multifractal detrended fluctuation analysis to analyze the differences of multifractal behavior of HS between healthy people and HFpEF patients. Afterwards, the statistical analysis was implemented on the extracted HS characteristics to generate the diagnostic feature set. Finally, the extreme learning machine was applied for HFpEF identification by the comparison of performances with support vector machine. The result shows an accuracy of 96.32%, a sensitivity of 95.48% and a specificity of 97.10%, which demonstrates the effectiveness of HS for HFpEF diagnosis.

Development of a Smartphone App for Visualizing Heart Sounds and Murmurs.

BACKGROUND: Auscultation is one of the basic techniques for the diagnosis of heart disease. However, the interpretation of heart sounds and murmurs is a highly subjective and difficult skill. OBJECTIVES: To assist the auscultation skill at the bedside, a handy phonocardiogram was developed using a smartphone (Samsung Galaxy J, Android OS 4.4.2) and an external microphone attached to a stethoscope. METHODS AND RESULTS: The Android app used Java classes, "AudioRecord," "AudioTrack," and "View," that recorded sounds, replayed sounds, and plotted sound waves, respectively. Sound waves were visualized in real-time, simultaneously replayed on the smartphone, and saved to WAV files. To confirm the availability of the app, 26 kinds of heart sounds and murmurs sounded on a human patient simulator were recorded using three different methods: a bell-type stethoscope, a diaphragm-type stethoscope, and a direct external microphone without a stethoscope. The recorded waveforms were subjectively confirmed and were found to be similar to the reference waveforms. CONCLUSIONS: The real-time visualization of the sound waves on the smartphone may help novices to readily recognize and learn to distinguish the various heart sounds and murmurs in real-time.

Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree.

In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.

Accidental Heart Murmurs.

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.

Evaluating the impact of high- and low-fidelity instruction in the development of auscultation skills.

CONTEXT: A principal justification for the use of high-fidelity (HF) simulation is that, because it is closer to reality, students will be more motivated to learn and, consequently, will be better able to transfer their learning to real patients. However, the increased authenticity is accompanied by greater complexity, which may reduce learning, and variability in the presentation of a condition on an HF simulator is typically restricted. OBJECTIVES: This study was conducted to explore the effectiveness of HF and low-fidelity (LF) simulation for learning within the clinical education and practice domains of cardiac and respiratory auscultation and physical assessment skills. METHODS: Senior-level nursing students were randomised to HF and LF instruction groups or to a control group. Primary outcome measures included LF (digital sounds on a computer) and HF (human patient simulator) auscultation tests of cardiac and respiratory sounds, as well as observer-rated performances in simulated clinical scenarios. RESULTS: On the LF auscultation test, the LF group consistently demonstrated performance comparable or superior to that of the HF group, and both were superior to the performance of the control group. For both HF outcome measures, there was no significant difference in performance between the HF and LF instruction groups. CONCLUSIONS: The results from this study suggest that highly contextualised learning environments may not be uniformly advantageous for instruction and may lead to ineffective learning by increasing extraneous cognitive load in novice learners.

Third heart sound in hospitalised patients with acute heart failure: insights from the ATTEND study.

BACKGROUND: Several previous studies have suggested that detection of a third heart sound (S3) in patients with chronic congestive heart failure is associated with adverse long-term outcomes. However, the short-term prognostic value of identifying an S3 on admission in patients with acute heart failure (AHF) is not well established. We therefore analysed the in-hospital prognostic value of detecting an S3 on admission in hospitalised patients with AHF. METHODS: The Acute Decompensated Heart Failure Syndromes (ATTEND) study investigators enrolled 4107 patients hospitalised with AHF. Investigators evaluated the presence or absence of an S3 during routine physical examination. RESULTS: On admission to hospital, 1673 patients (41%) had an S3. Patients with an S3 had a higher heart rate, higher serum level of B-type natriuretic peptide and higher creatinine levels than patients without an S3. However, there were no significant differences of systolic blood pressure, serum sodium, haemoglobin, C-reactive protein and total bilirubin between the two groups. Multivariate analysis adjusted for various markers of disease severity revealed that only the presence of an S3 was independently associated with an increase of in-hospital all cause death [adjusted odds ratio (OR), 1.69; 95% confidence interval (CI), 1.19-2.41; p = 0.003] and cardiac death (adjusted OR, 1.66; 95% CI, 1.08-2.54; p = 0.020) among the congestive physical findings related to heart failure (S3, rales, jugular venous distension and peripheral oedema). CONCLUSIONS: Detecting an S3 on admission was independently associated with adverse in-hospital outcomes in patients with AHF. Our findings suggest that careful bedside assessment is clinically meaningful.

Auscultating heart and breath sounds through patients' gowns: who does this and does it matter?

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.

Cuffless and Continuous Blood Pressure Estimation from the Heart Sound Signals.

Cardiovascular disease, like hypertension, is one of the top killers of human life and early detection of cardiovascular disease is of great importance. However, traditional medical devices are often bulky and expensive, and unsuitable for home healthcare. In this paper, we proposed an easy and inexpensive technique to estimate continuous blood pressure from the heart sound signals acquired by the microphone of a smartphone. A cold-pressor experiment was performed in 32 healthy subjects, with a smartphone to acquire heart sound signals and with a commercial device to measure continuous blood pressure. The Fourier spectrum of the second heart sound and the blood pressure were regressed using a support vector machine, and the accuracy of the regression was evaluated using 10-fold cross-validation. Statistical analysis showed that the mean correlation coefficients between the predicted values from the regression model and the measured values from the commercial device were 0.707, 0.712, and 0.748 for systolic, diastolic, and mean blood pressure, respectively, and that the mean errors were less than 5 mmHg, with standard deviations less than 8 mmHg. These results suggest that this technique is of potential use for cuffless and continuous blood pressure monitoring and it has promising application in home healthcare services.

AI diagnosis of heart sounds differentiated with super StethoScope.

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.