Cardiac Sounds Research Articles

Design and Fabrication of an Integrated Hollow Concave Cilium MEMS Cardiac Sound Sensor.

In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies. By rationally designing the acoustic encapsulation, the loss of heart acoustic distortion and weak cardiac murmurs is reduced. As demonstrated by experimentation, the proposed hollow MEMS structure cardiac sound sensor has a sensitivity of up to -206.9 dB at 200 Hz, showing 6.5 dB and 170 Hz increases in the sensitivity and operating bandwidth, respectively, in contrast to the planar ciliated MEMS sensor. The SNR of the sensor is 26.471 dB, showing good detectability for cardiac sounds.

Abdominal cardiovascular sound recording and analysis using cardio-microphones.

In view of using abdominal microphones for fetal heart rate (FHR) monitoring, the analysis of the obtained abdominal phonocardiogram (PCG) signals is complex due to many interferential noises including blood flow sounds. In order to improve the understanding of abdominal phonocardiography, a preliminary study was conducted in one healthy volunteer and designed to characterize the PCG signals all over the abdomen. Acquisitions of PCG signals in different abdominal areas were realized, synchronously with one thoracic PCG signal and one electrocardiogram signal. The analysis was carried out based on the temporal behavior, amplitude and mean pattern of each signal. The synchronized rhythmic signature of each signal confirms that the PCG signals obtained on the abdominal area are resulting from heart function. However, the abdominal PCG patterns are totally different from the thoracic PCG one, suggesting the recording of vascular blood flow sounds on the abdomen instead of cardiac valve sounds. Moreover, the abdominal signal magnitude depends on the sensor position and therefore to the size of the underlying vessel. The sounds characterization of abdominal PCG signals could help improving the processing of such signals in the purpose of FHR monitoring.

A critical review of heart sound signal segmentation algorithms

This paper discusses four heart sound segmentation (HSS) methods: Wavelet transform, Fractal decomposition, Hilbert Transform, and Shannon Energy Envelogram, in order to identify the different cardiac sounds. Many research studies related to heart signal analysis, have adopted these methods to give high heart sound segmentation results, especially, for the identification of first and second heart sounds and murmurs. Performance of the heart sound segmentation results have also been compared with one other to identify the most efficient method(s), and it has been found that Shannon energy envelogram provides the best accuracies among the segmentation methods. Understandings of the segmentation methods for heart sound may pave the way for more advanced studies in other heart-related researches, including heart sound classifications.

Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals

Background and objective: Auscultation is the first technique applied to the early diagnose of any cardiovascular disease (CVD) in rural areas and poor-resources countries because of its low cost and non-invasiveness. However, it highly depends on the physician’s expertise to recognize specific heart sounds heard through the stethoscope. The analysis of phonocardiogram (PCG) signals attempts to segment each cardiac cycle into the four cardiac states (S1, systole, S2 and diastole) in order to develop automatic systems applied to an efficient and reliable detection and classification of heartbeats. In this work, we propose an unsupervised approach, based on time-frequency characteristics shown by cardiac sounds, to detect and classify heartbeats S1 and S2.Methods: The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correction-classification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database.Results: The proposed method outperforms the detection and classification performance of other recent state-of-the-art methods. Although our proposal achieves the best average accuracy for PCG signals without cardiac abnormalities, 99.4% in heartbeat detection and 97.2% in heartbeat classification, its worst average accuracy is always above 92% for PCG signals with cardiac abnormalities, signifying an improvement in heartbeat detection/classification above 10% compared to the other state-of-the-art methods evaluated.Conclusions: The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.

Variational mode decomposition based automated fetal PCG denoising and extraction

Congenital cardiac anomalies of fetus are often characterized by the unprecedented changes in the auditory properties of cardiac sounds occurs during the gestation period of pregnant women. These abnormalities are often seen in inconsistent patterns of heart sounds that are driven by asynchronous variations in heart rates of mother and fetus. This hostile situation becomes severe if it is untreated and might threaten to life risk in pregnant women. In this work, we proposed a novel and automated signal processing paradigm using Variational Mode Decomposition (VMD) to detect and extract the mother and fetus heart sounds from the raw PCG signals recorded from outer surface of the maternal abdomen. The proposed framework constitutes a couple of cascaded VMD blocks: The first VMD block alleviates the raspatory noises and other artifacts from the raw PCG signal; while the Next one, decomposes the mother and fetus heart sounds obtained from its preceding block. A publicly available Shiraz University Fetal Heart Sounds Database is used to test the efficacy of the proposed model. In addition, noisy PCG corpus characterized by the additive white gaussian noise is used to test the efficacy of the proposed network. F

Tamponamento cardíaco na adolescência: dois casos clínicos

Cardiac tamponade is a medical emergency and is the most serious complication of pericardial effusion. The authors describe two case reports with different etiologies, in adolescence. 17 years old female patient, goes to the emergency department due to polyarthralgia, chest pain, orthopnea and fatigue. At observation: fever, tachycardia, decreased cardiac sounds, decreased pulmonary sounds in bases and tibio-tarsal joint edema. Chest radiography: cardiomegaly and bilateral interstitial infiltrate; electrocardiogram: ST elevation; echocardiogram: massive pericardial effusion (“swinging heart”). Pericardiocentesis was performed, with no significant changes in the pericardial fluid. Therapy with non-steroidal anti-inflammatory drugs (NSAIDs), acetylsalicylic acid (ASA) and colchicine was started. Further investigation led to systemic lupus erythematosus (SLE) diagnosis and improvement happened after correct therapy. 16-year-old male patient with Crohns disease under azathioprine and adalimumab, goes to the emergency department due fever, cough and chest pain. Three days before he had started amoxicillin-clavulanic acid for pneumonia. At observation he was tachycardic, with decreased cardiac sounds, decreased right breath sounds and painful abdomen on palpation. Thoraco-abdominal ultrasound: pleural effusion, hepatomegaly, splenic nodules, adenopathies in the celiac trunk. 72 hours after admission, under triple antibiotical therapy, there was clinical worsening and a thoracoabdominal CT was performed: pericardial and pleural effusion, mediastinal adenomegalies, hepatosplenomegaly, splenic lesions. Echocardiogram: pericardial effusion with collapse of the right cavities. Pericardial fluid showed an adenosine deaminase (ADA) of 53U/L. Clinical improvement after therapy for tuberculosis was started. Clinical suspicion is essential for a rapid therapeutic intervention, with echocardiography being the gold standard.

Simulation Technology in Anesthesiology.

Simulation Technology in Anesthesiology.

A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds.

The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland–Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients.

Unsupervised Heart Sound Decomposition and State Estimation with Generative Oscillation Models.

This paper proposes a new generative probabilistic model for phonocardiograms (PCGs) that can simultaneously capture oscillatory factors and state transitions in cardiac cycles. Conventionally, PCGs have been modeled in two main aspects. One is a state space model that represents recurrent and frequently appearing state transitions. Another is a factor model that expresses the PCG as a non-stationary signal consisting of multiple oscillations. To model these perspectives in a unified framework, we combine an oscillation decomposition with a state space model. The proposed model can decompose the PCG into cardiac state dependent oscillations by reflecting the mechanism of cardiac sounds generation in an unsupervised manner. In the experiments, our model achieved better accuracy in the state estimation task compared to the empirical mode decomposition method. In addition, our model detected S2 onsets more accurately than the supervised segmentation method when distributions among PCG signals were different.

FPGA-Based High-Performance Phonocardiography System for Extraction of Cardiac Sound Components Using Inverse Delayed Neuron Model.

The study focuses on the extraction of cardiac sound components using a multi-channel micro-electromechanical system (MEMS) microphone-based phonocardiography system. The proposed multi-channel phonocardiography system classifies the cardiac sound components using artificial neural networks (ANNs) and synaptic weights that are calculated using the inverse delayed (ID) function model of the neuron. The proposed ANN model was simulated in MATLABR and implemented in a field-programmable gate array (FPGA). The proposed system examined both abnormal and normal samples collected from 30 patients. Experimental results revealed a good sensitivity of 99.1% and an accuracy of 0.9.

The Utility of Real-Time Remote Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial.

BackgroundThe urgent need for telemedicine has become clear in the COVID-19 pandemic. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown.ObjectiveThis study was conducted to assess the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using lung and cardiology patient simulators.MethodsThis was an open-label, randomized controlled trial including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. This study consisted of 2 parts: lung auscultation and cardiac auscultation. Each part contained a tutorial session and a test session. All participants attended a tutorial session, in which they listened to 15 sounds on the simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of 10 lung sounds and 10 cardiac sounds, depending on the study part. The intervention group listened to the sounds remotely using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each sound.ResultsIn total, 20 participants were included. There were no differences in age, sex, and years from graduation between the 2 groups in each part. The overall test score of lung auscultation in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%; P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%). The overall test score for cardiac auscultation in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%; P=.77). There was no cardiac sound for which the correct answer rate differed between groups.ConclusionsThe utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. This means that most of the real world’s essential cardiopulmonary sounds could be classified by a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope.Trial RegistrationUMIN-CTR UMIN000040828; https://tinyurl.com/r24j2p6s and UMIN-CTR UMIN000041601; https://tinyurl.com/bsax3j5f

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

BackgroundCardiac auscultation remains an efficient and accessible diagnostic tool, especially in resource-limited countries where modern diagnostic devices like cardiac ultrasound are expensive and difficult to access. However, cardiac auscultation skills of medical students and physicians are declining, mainly because of an ineffective teaching method for this technique. The objective of this study is to evaluate the effect of a digitally enhanced cardiac auscultation learning method on participants’ theoretical knowledge and auscultation skills.MethodsThis will be a controlled study with two parallel arms (1:1). Participants (fourth-year medical students) will be divided into two groups: an intervention group (receiving additional lectures, clinical internship and audio listening sessions) and a control group (receiving additional lectures and clinical internship). At the beginning of the study, all participants will undergo a pre-test that consist of two parts: a knowledge assessment based on multiple-choice questions and a skills assessment based on recognition of cardiac sounds from audio files. Thereafter, three specific additional lectures on cardiac auscultation will be delivered and all participants will take part in their official clinical internship. During these clinical internships (eight weeks), participants of the intervention group will be invited to two listening sessions based on five digital recordings of heart sounds. At the end of the clinical internship, all participants will be invited to a post-test to evaluate their knowledge, skills and satisfaction according to their learning method. The main outcome will be the participants’ knowledge progression. The other outcomes will be the participants’ skills progression, participants’ total progression and satisfaction. Data will be collected and analyzed in per protocol.DiscussionThis study could contribute to the development of a learning method that takes into account the advantages of the conventional method and the contribution of digital technology. Positive results could lead to improved cardiac auscultation skills among health professionals, especially in developing countries.Trial registrationThe trial is registered on the Pan-African Clinical Trials Registry (http://www.pactr.org) under unique identification number: PACTR202001504666847, registered the 29 November 2019.

Acoustic-pressure sensor array system for cardiac-sound acquisition

This paper presents a cardiac-sound-detection system based on an acoustic-pressure sensor array. The traditional cardiac-sound probe has the disadvantages of high cost and thick structure, which makes embedding difficult. Therefore, a methodology for cardiac-sound detection, comprising a sound-pressure sensor array, is proposed to achieve a low cost, very thin, and wearable cardiac-sound probe, which can be embedded in furniture or clothes for long-term heart sound monitoring. By introducing several miniature electret-condenser-microphone-based acoustic-pressure sensors, a sensor array for picking up cardiac sound was designed. The data-processing platform comprised a field-programmable gate array and microcontroller unit. Furthermore, a series of de-noising methods including oversampling, finite impulse response digital filtering, and digital extraction were used, thereby facilitating accurate cardiac-sound signal identification. Test results showed that the average Root Mean Square amplitude of the noise after processing was more than 3 dB lower than that of the signal obtained from a single sensor in the array. The signal-to-noise ratio improved more than 7 dB after processing. Furthermore, actual cardiac sounds were acquired and compared using the proposed sensor array and the dedicated cardiac-sound sensor. The proposed 4 mm thick sensor array could effectively acquire S1- and S2-segments of cardiac sound. The proposed system demonstrates the potential for use in the development of wearable cardiac-sound monitoring systems.

Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis

Abstract This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds

Diseño, elaboración y validación de un simulador realista y de bajo costo para exploración cardiaca

Introduction: 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. Objective: To develop and validate a low-cost, high-fidelity simulator for heart exploration. Methods: 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. Results: 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. Conclusions: 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.

Estimation of Heart Rate From Tracheal Sounds Recorded for the Sleep Apnea Syndrome Diagnosis.

Obstructive sleep apnea is a common sleep disorder with a high prevalence and often accompanied by significant snoring activity. To diagnose this condition, polysomnography is the standard method, where a neck microphone could be added to record tracheal sounds. These can then be used to study the characteristics of breathing, snoring or apnea. In addition cardiac sounds, also present in the acquired data, could be exploited to extract heart rate. The paper presents new algorithms for estimating heart rate from tracheal sounds, especially in very loud snoring environment. The advantage is that it is possible to reduce the number of diagnostic devices, especially for compact home applications. Three algorithms are proposed, based on optimal filtering and cross-correlation. They are tested firstly on one patient presenting significant pathology of apnea syndrome, with a recording of 509 min. Secondly, an extension to a database of 16 patients is proposed (16 hours of recording). When compared to a reference ECG signal, the final results obtained from tracheal sounds reach an accuracy of 81% to 98% and an RMS error from 1.3 to 4.2 bpm, according to the level of snoring and to the considered algorithm.

Tele-Clinic Visits in Pediatric Patients with Marfan Syndrome Using Parentally Acquired Echocardiography

To test feasibility of tele-clinic visits using parentally acquired vital signs and focused echocardiographic images in patients with Marfan syndrome. We included patients with Marfan syndrome aged 5-19years followed in our clinic. We excluded patients with Marfan syndrome and history of previous aortic root (AoR) surgery, cardiomyopathy, arrhythmia, or AoR ≥4.5cm. We trained parents in-person to acquire focused echocardiographic images on their children using a hand-held device as well as how to use a stadiometer, scale, blood pressure (BP) machine, and a digital stethoscope. Before tele-clinic visits, parents obtained the echocardiographic images and vital signs. We compared tele-clinic and on-site clinic visit data. Parental and clinic echocardiograms were independently analyzed. Fifteen patient/parent pairs completed tele-clinic visits, conducted at a median of 7.0 (IQR 3.0-9.9) months from the in-person training session. Parents took a median of 70 (IQR 60-150) minutes to obtain the height, weight, heart rate, BP, cardiac sounds, and echocardiographic images before tele-clinic visits. Systolic BP was greater on-site than at home (median +13mm Hg, P=.014). Height, weight, diastolic BP, heart rate, and AoR measurements were similar. This study provides information for implementing tele-clinic visits using parentally acquired vital signs and echocardiographic images in patients with Marfan syndrome. The results show that tele-clinic visits are feasible and that parents were able to obtain focused echocardiographic images on their children. ClinicalTrials.gov: NCT03581682.

Somatosensory Pulsatile Tinnitus Syndrome (SSPT) Revisited.

Our initial study reported consecutive patients with constant cardiac synchronous subjective tinnitus (pulsatile tinnitus without an identifiable acoustic source (P)) all of whom could suppress their pulsations with head and neck intense muscle contractions ("somatic testing" (ST)). The term somatosensory pulsatile tinnitus syndrome (SSPT) was coined to refer to this type of P. With now more than a decade of clinical experience with P, herein are reported (a) other ways P can present, beside SSPT, (b) how P is related to the somatosensory system and recumbency, and (c) what treatments have been effective. Retrospective case series of 58 adults with P encountered in an outpatient clinic or through telemedicine. P could be constant or intermittent, with or without non-pulsatile tinnitus (nP). 90% of cases could suppress their pulsations with ST; 9% could not. In 7 of 11 cases that had no P at time of testing, ST elicited P. The most common type of P was SSPT (constant pulsatile tinnitus suppressible by ST) (60%). Treatment of head and neck muscle dysfunction (muscle dry needling and Botulinum toxin injection) has abolished P; auricular electrical stimulation was effective in 2 cases. Suppression of pulsations by ST, eliciting P by ST, and abolishment of P by head and neck muscle treatments all support a major role of the craniocervical somatosensory system in the etiology of most, if not all, cases of P. Three mechanisms are proposed: (A) somatosensory afferents causing dysfunction of the CNS mechanisms that normally suppress self-generated cardiac and vascular sounds, (B) cardiac synchronous disinhibition of the auditory CNS by somatosensory afferents and (C) some combination of A and B.

Статистичний алгоритм для швидкої оцінки частоти серцебиття й локалізації серцевих тонів у фонокардіограмах, записаних за допомогою електронних стетоскопів

A draft version of the algorithm for fast determination of duration of the systolic and diastolic phases of the cardiac cycle based on a statistical analysis of a digital phonocardiogram is presented. The developed technique includes localization of the cardiac tones in the record by a quantile dichotomy and modal analysis of the calculated time intervals between the adjacent detected peaks. Next, the scatter plot is constructed in terms of time intervals before and after each peak. The stability of the cardiac rhythm is determined by the density of the cluster occurring near the cross-section of two modal values plotted along the axes. At dominance of a cardiac component in the signal, the discussed approach allows quite reliable determination of the moments of the emergence of the first and second cardiac sounds tones practically on each of the cardiac cycles. This opens the possibility to analyze the variation in the duration of separate phases of heart contractions over time. Due to the work directly in the time domain and low computational costs, the algorithms of this class are suitable for application in portable diagnostic systems with limited processor power.

Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual Geometry Group (VGG16), ResNet, DenseNet, and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and abnormal. For PhysioNet 2016, DenseNet has outperformed its peer models with an accuracy of 89.04 percent, whereas for PASCAL 2011, VGG has outperformed its peer approaches with an accuracy of 92.96 percent.