Cardiac Sounds Research Articles

Enhancement of cardiac and respiratory sounds for cellphone reproduction by means of digital sound processing methods

Enhancement of cardiac and respiratory sounds for cellphone reproduction by means of digital sound processing methods

Abstract 4118342: Acoustic Analysis For The Detection Of Leaflet Thrombosis After Transcatheter Aortic Valve Replacement

Introduction: Following trans-catheter aortic valve replacement (TAVR), patients may develop aortic valve leaflet thrombosis, a condition associated with an increased risk of embolic events. Currently, the diagnosis of post-TAVR leaflet thrombosis is based on echocardiography surveillance or cardiac tomography (CCT) scan, both require specialized team and equipment, and may also expose the patients to radiation and contrast. Acoustic analysis is a non-invasive reproducible method, which incorporates the recording of cardiac sounds and analyzing them via machine learning algorithms. Here, we investigated the diagnostic abilities of acoustic analysis in the detection of post-TAVR leaflet thrombosis. Methods: Using an electronic stethoscope, heart sounds were prospectively recorded at 2 time points: 24-hours and 6-months post-TAVR. CCT scan was performed 6-months following TAVR to detect leaflet thrombosis. Acoustic sounds were analyzed, using a Gaussian support vector machine (SVM) algorithm, to identify features associated with leaflet thrombosis. Results: Of 19 consecutive patients, 3 were found to have high-grade leaflet thrombosis based on CCT results. The SVM algorithm effectively classified heart sounds into "normal" and "thrombotic", with a sensitivity of 100% and specificity of 97.2%. Following anticoagulation therapy, all 3 patients showed no signs of leaflet thrombosis on follow-up CCT scans and were also classified as "normal" by the algorithm. Notably, at the time of leaflet thrombosis, Doppler echocardiographies showed normal pressure gradients across the affected valves. Conclusions: These initial results suggest that acoustic analysis, combined with machine learning algorithms, may identify post-TAVR leaflet thrombosis, providing a non-invasive, cost-effective, and radiation-free alternative to traditional diagnostic methods.

Advances in cardiac sounds monitoring enabled by triboelectric sensors

Cardiac sounds sensor as a noninvasive instrument plays a pivotal role in the early diagnosis of various heart disease. In recent years, triboelectric nanogenerators (TENGs) have been employed widely for sensing different physiological information benefiting from abundant materials choices, simplicity of construction and low cost. More importantly, the fast saturated constitutive characteristic of the contact-separation mode (C-S) TENG demonstrates ultrahigh sensitivity in the detection of micromechanical quantities than traditional piezoelectric sensing mechanism. This article presents an overview of the C-S TENG enabled self-powered cardiac sounds monitoring, focusing on the working principle, performance comparison between C-S TENG and piezoelectric generator (PEG), and related applications. The challenges and future research directions of cardiac sounds sensor and C-S TENG toward micro-displacement region have also been proposed.

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.

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.

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

SummaryBackgroundCardiac auscultation is an efficient and effective diagnostic tool, especially in low-income countries where access to modern diagnostic methods remains difficult. This study aimed to evaluate the effect of a digitally enhanced cardiac auscultation learning method on medical students’ performance and satisfaction.MethodsWe conducted a double-arm parallel controlled trial, including newly admitted 4th -year medical students enrolled in two medical schools in Yaoundé, Cameroon and allocated into two groups: the intervention group (benefiting from theoretical lessons, clinical internship and the listening sessions of audio recordings of heart sounds) and the control group (benefiting from theoretical lessons and clinical internship). All the participants were subjected to a pretest before the beginning of the training, evaluating theoretical knowledge and recognition of cardiac sounds, and a post-test at the eighth week of clinical training associated with the evaluation of satisfaction. The endpoints were the progression of knowledge score, skills score, total (knowledge and skills) score and participant satisfaction.ResultsForty-nine participants (27 in the intervention group and 22 in the control group) completed the study. The knowledge progression (+ 26.7 versus + 7.5; p ˂0.01) and the total progression (+ 22.5 versus + 14.6; p ˂ 0.01) were higher in the intervention group with a statistically significant difference compared to the control group. There was no significant difference between the two groups regarding skills progression (+ 25 versus + 17.5; p = 0.27). Satisfaction was higher in general in the intervention group (p ˂ 0.01), which recommended this method compared to the control group.ConclusionThe learning method of cardiac auscultation reinforced by the listening sessions of audio recordings of heart sounds improves medical students’ performances (knowledge and global – knowledge and skills) who find it satisfactory and recommendable.Trial RegistrationThis trial has been registered the 29/11/2019 in the Pan African Clinical Trials Registry (http://www.pactr.org) under unique identification number PACTR202001504666847 and the protocol has been published in BMC Medical Education.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis.

Electronic stethoscope used to detect cardiac sounds that contain essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electromechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, it is found that triboelectric sensor features superior advantages over piezoelectric one for microquantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215mV Pa-1) than the piezoelectric counterpart (21mV Pa-1) in the sound pressure range of 50-80dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis.

Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students.

Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann-Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students' familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation.

A Survey on Prediction of Heart Functionality Sound by using Deep Learning Algorithm

The primary objective of this study is to enhance the precision of predicting cardiovascular illnesses by introducing a sophisticated system that leverages deep learning techniques. Traditional diagnostic methods have relied on the analysis of cardiac sounds, achieving an accuracy of approximately 87.5% through the use of machine learning algorithms such as Random Forest and Decision Trees. However, these conventional approaches have their limitations. In contrast, the proposed hybrid approach aims to surpass these limitations by focusing on capturing intricate patterns and temporal relationships within heart sound data using deep learning. This innovative approach seeks to demonstrate a substantial improvement in prediction accuracy when compared to existing methods. If proven effective, this deep learning-based diagnostic tool has the potential to provide a more nuanced understanding of heart function, enabling early identification of anomalies and ultimately leading to improved patient outcomes in cardiovascular health.

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking.

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6dB at an input SNR of - 6dB and by 3.3dB at an input SNR of 10dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56dB.

Recent Advances in PCG Signal Analysis using AI: A Review

Abstract The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.

Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning.

Cardiovascular disease (CVDs) has become one of the leading causes of death, posing a significant threat to human life. The development of reliable Artificial Intelligence (AI) assisted diagnosis algorithms for cardiac sounds is of great significance for early detection and treatment of CVDs. However, there is scarce research in this field. Existing research mainly faces three major challenges: (1) They mainly limited to murmur classification and cannot achieve murmur grading, but attempting both classification and grading may lead to negative effects between different multi-tasks. (2) They mostly pay attention to unstructured cardiac sound modality and do not consider the structured demographic modality, as it is difficult to balance the influence of heterogeneous modalities. (3) Deep learning methods lack interpretability, which makes it challenging to apply them clinically. To tackle these challenges, we propose a method for cardiac murmur grading and cardiac risk analysis based on heterogeneous modality adaptive multi-task learning. Specifically, a Hierarchical Multi-Task learning-based cardiac murmur detection and grading method (HMT) is proposed to prevent negative interference between different tasks. In addition, a cardiac risk analysis method based on Heterogeneous Multi-modal feature impact Adaptation (HMA) is also proposed, which transforms unstructured modality into structured modality representation, and utilizes an adaptive mode weight learning mechanism to balance the impact between unstructured modality and structured modality, thus enhancing the performance of cardiac risk prediction. Finally, we propose a multi-task interpretability learning module that incorporates an important evaluation using random masks. This module utilizes SHAP graphs to visualize crucial murmur segments in cardiac sound and employs a multi-factor risk decoupling model based on nomograms. And then we gain insights into the cardiac disease risk in both pre-decoupled multi-modality and post-decoupled single-modality scenarios, thus providing a solid foundation for AI assisted cardiac murmur grading and risk analysis. Experimental results on a large real-world CirCor DigiScope PCG dataset demonstrate that the proposed method outperforms the state-of-the-art (SOTA) method in murmur detection, grading, and cardiac risk analysis, while also providing valuable diagnostic evidence.

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning.

In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model.

Heartbeat Cardiac Sounds Analysis

Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. This paper presents an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. Secondly the heartbeat cardiac sounds represented by the phonocardiogram (PCG) signal once segmented is one of the useful approaches to explore cardiac activity and extract many features to help researchers develop techniques that can be used for the medical treatment of several heart diseases. For people affected by a heart activity problem, it is a serious health problem that requires special care. In this paper, importance is given to heart murmurs and their severity to show their impact on heart rate. Heart murmurs are very widespread pathologies in the world and depending on their severity they could constitute. This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models technology which used to extract a smooth envelogram which enable us to apply the tests necessary for temporal localization of heart sounds and heart murmurs.

Highly sensitive low-frequency-detectable acoustic sensor using a piezoresistive cantilever for health monitoring applications

This study investigates a cantilever-based pressure sensor that can achieve a resolution of approximately 0.2 mPa, over the frequency range of 0.1–250 Hz. A piezoresistive cantilever with ultra-high acoustic compliance is used as the sensing element in the proposed pressure sensor. We achieved a cantilever with a sensitivity of approximately 40 times higher than that of the previous cantilever device by realizing an ultrathin (340 nm thick) structure with large pads and narrow hinges. Based on the measurement results, the proposed pressure sensor can measure acoustic signals with frequencies as low as 0.1 Hz. The proposed pressure sensor can be used to measure low-frequency pressure and sound, which is crucial for various applications, including photoacoustic-based gas/chemical sensing and monitoring of physiological parameters and natural disasters. We demonstrate the measurement of heart sounds with a high SNR of 58 dB. We believe the proposed microphone will be used in various applications, such as wearable health monitoring, monitoring of natural disasters, and realization of high-resolution photoacoustic-based gas sensors. We successfully measured the first (S1) and second (S2) cardiac sounds with frequencies of 7–100 Hz and 20–45 Hz, respectively.

Cocaine user with chest pain.

A 52-year-old male presented to the emergency department with a past medical history of active cocaine use and a chief complaint of chest pain that radiated to the back. Physical exam revealed a well-appearing, comfortable male with normal cardiac sounds, regular rate and rhythm, bilateral equal pulses in all four extremities, and intact motor and sensation in the extremities. A bedside ultrasound was performed and revealed an ascending aorta dissection (Figure 1 and Video 1). This was confirmed on intraoperative transesophageal echocardiogram that demonstrated the proximity of the aortic dissection flap to the aortic valve (Figure 2). Computed tomography angiography (CTA) of the chest confirmed the diagnosis of Stanford A (type 1) aortic dissection extending from the supravalvular region to the level of the renal arteries. The patient was placed on nicardipine and esmolol drips and cardiothoracic surgery was emergently consulted. The patient underwent surgery where he had an intraoperative transesophageal echocardiogram, stenting of the aortic root, ascending aorta, and descending aorta. He recovered from his surgery and was ultimately discharged to home. Aortic dissection is a relatively rare disease with an estimated global death rate of 2.78 per 100,000.1 The diagnosis is typically made with CTA of the chest. Thoracic aortic dissection is classified into two categories: Stanford type A that involves the proximal ascending aorta before the left subclavian and Stanford type B that involves the aorta only distal to the left subclavian.2 The treatment of aortic dissection includes blood pressure and heart rate reduction. Early surgical consultation is important because type A aortic dissection is a true surgical emergency.

Design, assembly and validation of a low-cost, high-fidelity simulator for heart exploration.

Heart exploration is an essential clinical competence that requires continuous training and exposure. Low availability and accessibility to patients with heart disease constitutes a barrier to acquiring this competence. Inadequate cardiac auscultation skills in medical students, residents, and graduate physicians have been documented. To develop and validate a low-cost, high-fidelity simulator for heart exploration. A low-cost, high-fidelity heart examination simulator capable of reproducing normal cardiac sounds was designed and developed. Subsequently, the simulator was validated by a group of experts who gave their opinion according to a Likert scale. Ninety-four percent agreed that the simulator motivates the learning of heart exploration, and 92 % considered it to be a realistic model; 91 % considered that the simulator is an attractive tool to reinforce learning and 98 % recommended its further use. The use of the simulator facilitates the acquisition of skills and stimulates learning in the student, which can be attributed to repeated practice, longer exposure time and cognitive interaction.

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

Objective. Cardiopulmonary auscultation is promising to get smart due to the emerging of electronic stethoscopes. Cardiac and lung sounds often appear mixed at both time and frequency domain, hence deteriorating the auscultation quality and the further diagnosis performance. The conventional cardiopulmonary sound separation methods may be challenged by the diversity in cardiac/lung sounds. In this study, the data-driven feature learning advantage of deep autoencoder and the common quasi-cyclostationarity characteristic are exploited for monaural separation. Approach. Different from most of the existing separation methods that only handle the amplitude of short-time Fourier transform (STFT) spectrum, a complex-valued U-net (CUnet) with deep autoencoder structure, is built to fully exploit both the amplitude and phase information. As a common characteristic of cardiopulmonary sounds, quasi-cyclostationarity of cardiac sound is involved in the loss function for training. Main results. In experiments to separate cardiac/lung sounds for heart valve disorder auscultation, the averaged achieved signal distortion ratio (SDR), signal interference ratio (SIR), and signal artifact ratio (SAR) in cardiac sounds are 7.84 dB, 21.72 dB, and 8.06 dB, respectively. The detection accuracy of aortic stenosis can be raised from 92.21% to 97.90%. Significance. The proposed method can promote the cardiopulmonary sound separation performance, and may improve the detection accuracy for cardiopulmonary diseases.

MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM-BASED ABRUPT CHANGES DETECTION FOR HEART SOUNDS SEGMENTATION

The aim of this paper is cardiac sound segmentation in order to extract significant clinical parameters that can aid cardiologists in diagnosis, through maximal overlap discrete wavelet transform (MODWT) and abrupt changes detection. After reconstruction of the fifth to seventh level of decomposition of the pre-processed phonocardiogram (PCG), we can correctly measure the time duration of Fundamental heart sounds (S1, S2), while the third and fourth levels localize murmurs and clicks. From this scope, it is possible to establish the time interval between clicks and fundamental heart sounds or evaluating murmur severity through energetic ratio. We have tested this approach on several phonocardiography records. Results show that this method performs greatly on long and short PCG records and gives the precise duration of fundamental heart sounds; we have achieved an accuracy of 88.6% in cardiac sounds segmentation.

Robust Denoising of Phonocardiogram Signals using Time-Frequency Analysis and U-Nets

Cardiovascular diseases are the cause of many deaths worldwide every day. Automated cardiac auscultation is a promising diagnosis method; however, one of its main disadvantages is that it is prone to receiving too much noise during sound recording, which hinders diagnosis. In the available literature, most phonocardiogram (PCG) denoising methods have been evaluated using only synthetic sources such as white noise; this work proposes a more realistic approach. In this paper, the denoising process occurs in the time-frequency domain. More specifically, we compute the Short-Time Fourier Transform (STFT) of the contaminated PCG signal and train a U-Net to recognize normal and pathological cardiac sounds from noise. We are unaware of previous attempts to develop a robust PCG-denoising algorithm capable of simultaneously removing noise signals from four different sources: additive white Gaussian noise (AWGN), additive pink Gaussian noise (APGN), speech, and real PCG background noise. Since we are limited by the relatively small number of clean PCG signals available, we also propose a method for high-quality phonocardiogram data augmentation. In our tests, the proposed method exhibits high performance even in unfavorable scenarios since it can denoise a PCG signal contaminated with a signal-to-noise ratio (SNR) of −5 dB with average improvements ranging from 11.85 dB up to 17.60 dB, depending on the noise type used to degrade the cardiac signal. This method could significantly improve the performance of automatic cardiac sound classification algorithms in noisy environments but could also be used in electronic stethoscopes.