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Clinical fetal monitoring devices can only be operated by medical professionals and are overly costly, prone to detrimental false positives, and emit radiation. Thus, highly accurate, easily accessible, simplified, and cost-effective fetal monitoring devices have gained an enormous interest in obstetrics. In this study, a cost-effective and user-friendly wearable home fetal movement and distress detection device is developed and assessed for early-stage design progression by facilitating continuous, comfortable, and non-invasive monitoring of the fetus during the final trimester. The functionality of the developed prototype is mainly based on a microcontroller, a single accelerometer, and a specialized fetal phonocardiography (fPCG) acquisition board with a low-cost microphone. The developed system is capable of identifying fetal movement and monitors fetal heart rhythm owing to its considerable sensitivity. Further, the device includes a Global System for Mobile Communication (GSM)-based alert system for instant distress notifications to the mother, proxy, and emergency services. By incorporating digital signal processing, the system achieves zero false negatives in detecting fetal movements, which was validated against an open-source database. The acquired results clearly substantiated the efficacy of the fPCG acquisition board and alarm system, ensuring the prompt identification of fetal distress.

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AIMS: A fourth heart sound (S4) was reported to be almost never present in patients with amyloid light-chain cardiomyopathy. There have been no reports on S4 in patients with wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM). This study aimed to clarify the clinical implications of S4 in patients with ATTRwt-CM. METHODS AND RESULTS: Seventy-six patients with ATTRwt-CM (mean age: 80.4 ± 5.4 years, 68 males) who had undergone phonocardiography (PCG) were retrospectively assessed. We measured S4 amplitude on digitally recorded PCG. S4 was considered to be present when its amplitude was 1.0 mm or greater on the PCG. Distinct S4 was defined as S4 with an amplitude of 2.0 mm or greater, which is usually recognizable by auscultation. According to the rhythm and presence or absence of S4, the patients were divided into three groups, namely, sinus rhythm (SR) with S4, SR without S4, and non-SR. Non-SR consisted of atrial fibrillation, atrial flutter, and atrial tachycardia. Thirty-six patients were in SR and the remaining 40 patients were in non-SR. In the 36 patients in SR, S4 was shown by PCG to be present in 17 patients (47%), and distinct S4 was recognized in 7 patients (19%) by auscultation. In patients who were in SR, those with S4 had higher systolic blood pressure (124 ± 15 vs. 99 ± 8 mmHg, P < 0.001), lower level of plasma B-type natriuretic peptide (308 [interquartile range (IQR): 165, 354] vs. 508 [389, 765] pg/mL, P = 0.034) and lower level of high-sensitivity cardiac troponin T (0.068 [0.046, 0.089] vs. 0.109 [0.063, 0.148] ng/mL, P = 0.042) than those without S4. There was no significant difference in left atrium (LA) volume index or LA reservoir strain between patients with S4 and without S4. Patients with S4 had more preserved LA systolic function than those without S4 (peak atrial filling velocity: 53 ± 25 vs. 34 ± 9 cm/s, P = 0.033; LA contractile strain: 4.1 ± 2.1 vs. 1.6 ± 2.0%, P = 0.012). Patients in SR without S4 had worse short-term prognosis compared with the other two groups (generalized Wilcoxon test, P = 0.033). CONCLUSIONS: S4 was present in 47% of the patients in SR with ATTRwt-CM. Patients in SR without S4 had more impaired LA systolic function than those in SR with S4. The absence of S4 portends a poor short-term prognosis in patients with ATTRwt-CM.

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Phonocardiography (PCG) is used as an adjunct to teach cardiac auscultation and is now a function of PCG-capable stethoscopes (PCS). To evaluate the efficacy of PCG and PCS, the authors investigated the impact of providing PCG data and PCSs on how frequently murmurs, rubs, and gallops (MRGs) were correctly identified by third-year medical students. Following their internal medicine rotation, third-year medical students from the Georgetown University School of Medicine completed a standardized auscultation assessment. Sound files of 10 different MRGs with a corresponding clinical vignette and physical exam location were provided with and without PCG (with interchangeable question stems) as 10 paired questions (20 total questions). Some (32) students also received a PCS to use during their rotation. Discrimination/difficulty indexes, comparative chi-squared, and McNemar test p-values were calculated. The addition of phonocardiograms to audio data was associated with more frequent identification of mitral stenosis, S4, and cardiac friction rub, but less frequent identification of ventricular septal defect, S3, and tricuspid regurgitation. Students with a PCS had a higher frequency of identifying a cardiac friction rub. PCG may improve the identification of low-frequency, usually diastolic, heart sounds but appears to worsen or have little effect on the identification of higher-frequency, often systolic, heart sounds. As digital and phonocardiography-capable stethoscopes become more prevalent, insights regarding their strengths and weaknesses may be incorporated into medical school curricula, bedside rounds (to enhance teaching and diagnosis), and telemedicine/tele-auscultation efforts.

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The timely psychological stress detection can improve the quality of human life by preventing stress-induced behavioral and pathological consequences. This paper presents a novel framework that eliminates the need of Electrocardiography (ECG) signals-based referencing of Phonocardiography (PCG) signals for psychological stress detection. This stand-alone PCG-based methodology uses wavelet scattering approach on the data acquired from twenty-eight healthy adult male and female subjects to detect psychological stress. The acquired PCG signals are asynchronously segmented for the analysis using wavelet scattering transform. After the noise bands removal, the optimized segmentation length (L), scattering network parameters namely-invariance scale (J) and quality factor (Q) are utilized for computation of scattering features. These scattering coefficients generated are fed to K-nearest neighbor (KNN) and Extreme Gradient Boosting (XGBoost) classifier and the ten-fold cross validation-based performance metrics obtained are-accuracy 94.30 %, sensitivity 97.96 %, specificity 88.01 % and area under the curve (AUC) 0.9298 using XGBoost classifier for detecting psychological stress. Most importantly, the framework also identified two frequency bands in PCG signals with high discriminatory power for psychological stress detection as 270-290 Hz and 380-390 Hz. The elimination of multi-modal data acquisition and analysis makes this approach cost-efficient and reduces computational complexity.

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Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

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Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

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In this study a frequency scaling law for 3D anatomically representative supravalvular aortic stenosis (SVAS) cases is proposed. The law is uncovered for stethoscopy's preferred auscultation range (70-120 Hz). LES simulations are performed on the CFD solver Fluent, leveraging Simulia's Living Heart Human Model (LHHM), modified to feature hourglass stenoses that range between 30 to 80 percent (mild to severe) in addition to the descending aorta. For physiological hemodynamic boundary conditions the Windkessel model is implemented via a UDF subroutine. The flow-generated acoustic signal is then extracted using the FW-H model and analyzed using FFT. A preferred receiver location that matches clinical practice is confirmed (right intercostal space) and a correlation between the degree of stenosis and a corresponding acoustic frequency is obtained. Five clinical auscultation signals are tested against the scaling law, with the findings interpreted in relation to the NHS classification of stenosis and to the assessments of experienced cardiologists. The scaling law is thus shown to succeed as a potential quantitative decision-support tool for clinicians, enabling them to reliably interpret stethoscopic auscultations for all degrees of stenosis, which is especially useful for moderate degrees of SVAS. Computational investigation of more complex stenotic cases would enhance the clinical relevance of this proposed scaling law, and will be explored in future research.

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The paper presents the design and high-fidelity prototype of the remote patient self-monitoring system using a combination of intelligent phonocardiography, mobile and web-based platforms. The advantage of self-monitoring is patient awareness about potential changes, the convenience of performing the measurement often, and the saving of the findings. A mobile platform enables a physician to see the data, get a summary of patient recordings, and as well as saving the data. We have designed two user profiles to enable such functionality and to enable consultations. During the three development iterations, two main prototypes were developed. In the patient prototype, the main functionality is measuring PCG signals, but with the possibility of reading more details about the results. In the physician's prototype, the main functionality is the patient overview, with the possibility of querying through old patient data to consult newer patients. For physicians to monitor patients monitoring themselves, the solution needs to be properly clinically validated and regulatory demands satisfy before it could be utilized in the Norwegian health domain.

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Optimal heart function depends on perfect synchronization between electrical and mechanical activity. In this pilot study, we aimed to investigate the electromechanical activity of the heart in healthy cats and cats with cardiomyopathy with phonocardiography (PCG) synchronized to an electrocardiography (ECG) pilot device. We included 29 cats (12 healthy cats and 17 cats diagnosed with cardiomyopathy) and performed a clinical examination, PCG synchronized with ECG and echocardiography. We measured the following durations with the pilot PCG device synchronized with ECG: QRS (ventricular depolarization), QT interval (electrical systole), QS1 interval (electromechanical activation time (EMAT)), S1S2 (mechanical systole), QS2 interval (electrical and mechanical systole) and electromechanical window (end of T wave to the beginning of S2). The measured parameters did not differ between healthy cats and cats with cardiomyopathy; however, in cats with cardiomyopathy, EMAT/RR, QS2/RR and S1S2/RR were significantly longer than in healthy cats. This suggests that the hypertrophied myocardium takes longer to generate sufficient pressure to close the mitral valve and that electrical systole, i.e., depolarization and repolarization, and mechanical systoles are longer in cats with cardiomyopathy. The PCG synchronized with the ECG pilot device proved to be a valuable tool for evaluating the electromechanical activity of the feline heart.

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The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon.

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BACKGROUND: Third heart sounds in cats frequently are associated with hypertrophic cardiomyopathy (HCM) but their exact characterization and timing within the cardiac cycle remains unknown. OBJECTIVES: Characterize third heart sounds in cats by phonocardiography and test the ability of 3 observers with different levels of experience and training to recognize third systolic heart sounds in cats. ANIMALS: Fifty client-owned cats of different breeds presented for heart screening. METHODS: Cats were prospectively assessed using an electronic stethoscope (with digital recording) and then underwent full conventional echocardiographic examination. Audio recordings were blindly assessed in a random order by 3 observers: the cardiologist who collected clinical data, as well as a trained and an untrained junior veterinarian. Cohen's kappa coefficients were calculated to quantify agreement between the opinion of each observer and the echocardiography results (considered the gold standard). RESULTS: Twenty cats had a third systolic sound on phonocardiography and an obstructive HCM phenotype with systolic anterior motion of the mitral valve (SAM) on echocardiography. Agreement with echocardiography was very good for the experienced cardiologist, substantial for the trained junior veterinarian, and poor for the untrained junior veterinarian (kappa of 0.92, 0,64, and 0.08, respectively). CONCLUSIONS AND CLINICAL IMPORTANCE: We describe here a new auscultatory abnormality in cats with obstructive HCM. It could help a trained non-cardiologist veterinarian in suspecting obstructive HCM in cats based on auscultation only.

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In this paper, we identify a new (acoustic) frequency-stenosis relation whose frequencies lie within the recommended auscultation threshold of stethoscopy (< 120 Hz). We show that this relation can be used to extend the application of phonoangiography (quantifying the degree of stenosis from bruits) to widely accessible stethoscopes. The relation is successfully identified from an analysis restricted to the acoustic signature of the von Karman vortex street, which we automatically single out by means of a metric we propose that is based on an area-weighted average of the Q-criterion for the post-stenotic region. Specifically, we perform CFD simulations on internal flow geometries that represent stenotic blood vessels of different severities. We then extract their emitted acoustic signals using the Ffowcs Williams-Hawkings equation, which we subtract from a clean signal (stenosis free) at the same heart rate. Next, we transform this differential signal to the frequency domain and carefully classify its acoustic signatures per six (stenosis-)invariant flow phases of a cardiac cycle that are newly identified in this paper. We then automatically restrict our acoustic analysis to the sounds emitted by the von Karman vortex street (phase 4) by means of our Q-criterion-based metric. Our analysis of its acoustic signature reveals a strong linear relationship between the degree of stenosis and its dominant frequency, which differs considerably from the break frequency and the heart rate (known dominant frequencies in the literature). Applying our new relation to available stethoscopic data, we find that its predictions are consistent with clinical assessment. Our finding of this linear correlation is also unlike prevalent scaling laws in the literature, which feature a small exponent (i.e., low stenosis percentage sensitivity over much of the clinical range). They hence can only distinguish mild, moderate, and severe cases. Conversely, our linear law can identify variations in the degree of stenosis sensitively and accurately for the full clinical range, thus significantly improving the utility of the relevant scaling laws... Future research will investigate incorporating the vibroacoustic role of adjacent organs to expand the clinical applicability of our findings. Extending our approach to more complex 3D stenotic morphologies and including the vibroacoustic role of surrounding organs will be explored in future research to advance the clinical reach of our findings.

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Background: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. Objective: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. Material and Methods: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. Results: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2. Conclusion: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases.

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Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Physionet dataset of heart sound recordings. The accuracy of the parallel architecture of LSTM-CNN reached 98.0% outperforming all the combined architectures, with a sensitivity of 87.2%. The conventional CNN offered sensitivity/accuracy of 95.9%/97.3% with far less complexity. Results show that a conventional CNN can appropriately perform and solely employed for the classification of heart sound signals.

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This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

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This study assessed a new smartphone-based digital stethoscope (DS) featuring simultaneous phonocardiographic and one-lead electrocardiogram (ECG) recording in dogs and cats. The audio files and ECG traces obtained by the device were compared with conventional auscultation and standard ECG. A total of 99 dogs and nine cats were prospectively included. All cases underwent conventional auscultation using an acoustic stethoscope, standard six-lead ECG, standard echocardiography and recordings with the DS. All the audio recordings, phonocardiographic files and ECG traces were then blind reviewed by an expert operator. The agreement between methods was assessed using Cohen's kappa and the Bland-Altman test. Audio recordings were considered interpretable in 90% animals. Substantial agreement was found in the diagnosis of heart murmur (κ = 0.691) and gallop sound (k = 0.740). In nine animals with an echocardiographic diagnosis of heart disease, only the DS detected a heart murmur or gallop sound. ECG traces recorded with the new device were deemed interpretable in 88 % animals. Diagnosis of heart rhythm showed moderate agreement in the identification of atrial fibrillation (k = 0.596). The detection of ventricular premature complexes and bundle branch blocks revealed an almost perfect agreement (k = 1). Overall, the DS showed a good diagnostic accuracy in detecting heart murmurs, gallop sounds, ventricular premature complexes and bundle branch blocks. A clinically relevant overdiagnosis of atrial fibrillation was found but without evidence of false negatives. The DS could represent a useful screening tool for heart sound abnormalities and cardiac arrhythmias..

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Background: Screening for critical congenital heart defects should be performed as early as possible and is essential for saving the lives of children and reducing the incidence of undetected adult congenital heart diseases. Heart malformations remain unrecognized at birth in more than 50% of neonates at maternity hospitals. Accurate screening for congenital heart malformations is possible using a certified and internationally patented digital intelligent phonocardiography machine. This study aimed to assess the actual incidence of heart defects in neonates. A pre-evaluation of the incidence of unrecognized severe and critical congenital heart defects at birth in our well-baby nursery was also performed. Methods: We conducted the Neonates Cardiac Monitoring Research Project (ethics approval number: IR-IUMS-FMD. REC.1398.098) at the Shahid Akbarabadi Maternity Hospital. This study was a retrospective analysis of congenital heart malformations observed after screening 840 neonates. Using a double-blind format, 840 neonates from the well-baby nursery were randomly chosen to undergo routine clinical examinations at birth and digital intelligent phonocardiogram examinations. A pediatric cardiologist performed echocardiography for each neonate classified as having abnormal heart sounds using an intelligent machine or during routine medical examinations. If the pediatric cardiologist requested a follow-up examination, then the neonate was considered to have a congenital heart malformation, and the cumulative incidence was calculated accordingly. Results: The incidence of heart malformations in our well-baby nursery was 5%. Furthermore, 45% of heart malformations were unrecognized in neonates at birth, including one critical congenital heart defect. The intelligent machine interpreted innocent murmurs as healthy heart sound. Conclusion: We accurately and cost-effectively screened for congenital heart malformations in all neonates in our hospital using a digital intelligent phonocardiogram. Using an intelligent machine, we successfully identified neonates with CCHD and congenital heart defects that could not be detected using standard medical examinations. The Pouya Heart machine can record and analyze sounds with a spectral power level lower than the minimum level of the human hearing threshold. Furthermore, by redesigning the study, the identification of previously unrecognized heart malformations could increase to 58%.

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BACKGROUND AND OBJECTIVES: Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate. METHODS: To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms. MAIN RESULTS: Among all classifiers, ViT provides the best performance by achieving mean average accuracy Acc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models. CONCLUSIONS: The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.

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Aims: Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF. Methods and results: Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients' diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (E/e') were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different E/e'. Patients with a higher E/e' showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of E/e' > 9 [area under the curve (AUC): 0.72 (0.51-0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46-0.85), a value not better than any PCG feature (P > 0.05). Conclusion: Phonocardiography features stratify E/e' in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.

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This work studied, for the first time, the time-frequency characteristics of the vibrations underlying the first fetal heart sound (S1). To this end, the continuous wavelet transform was used to produce time-energy and time-frequency representations of S1 from where five vibrations were studied by their timing, energy, and frequency characteristics in three gestational age groups (early, G1, preterm, G2, and term, G3). Results on a dataset of 1111 S1s (9 phonocardiograms between 33 and 40 weeks) indicate that such representations uncovered a set of five well-defined, non-overlapped, and large-energy vibrations whose features presented interesting behaviors. Thus, for each group, while the timing characteristics of the five vibrations were likely to be statically different, their frequencies were similar. Also, the energies of the vibrations were likely to be different only in G2 and G3. Alternatively, while the frequencies and energies of each vibration were likely to statistically change among groups (excluding the energy of the third vibration), the timings were more likely to change only from G1 to G2 and from G2 to G3. Therefore, this methodology seems suitable to detect and study the generating vibrations of S1. Future work will test the correlation between these vibrations and the valvular events.

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