Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma-delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal-oxide-semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.

Ultimate Third Heart Sound

A 79-year-old man with dilated cardiomyopathy and severe functional mitral regurgitation presented with general fatigue and dyspnea. Auscultation revealed a systolic regurgitant murmur with a minimized second heart sound due to a low output. On the other hand, the third heart sound was ultimately enhanced, being visible and palpable as a pulsatile knock of the precordium. Phonocardiography and echocardiography successfully confirmed early-diastolic rapid distension of the left ventricle along with rapid ventricular filling and abrupt deceleration of the atrioventricular blood flow to be the precise etiology of the ultimate third heart sound, indicating critically deteriorated hemodynamics due to massive mitral regurgitation combined with a low output.

Comparison of multiple cardiac signal acquisition technologies for heart rate variability analysis

Heart rate variability analysis is a recognized non-invasive tool that is used to assess autonomic nervous system regulation in various clinical settings and medical conditions. A wide variety of HRV analysis methods have been proposed, but they all require a certain number of cardiac beats intervals. There are many ways to record cardiac activity: electrocardiography, phonocardiography, plethysmocardiography, seismocardiography. However, the feasibility of performing HRV analysis with these technologies and particularly their ability to detect autonomic nervous system changes still has to be studied. In this study, we developed a technology allowing the simultaneous monitoring of electrocardiography, phonocardiography, seismocardiography, photoplethysmocardiography and piezoplethysmocardiography and investigated whether these sensors could be used for HRV analysis. We therefore tested the evolution of several HRV parameters computed from several sensors before, during and after a postural change. The main findings of our study is that even if most sensors were suitable for mean HR computation, some of them demonstrated limited agreement for several HRV analyses methods. We also demonstrated that piezoplethysmocardiography showed better agreement with ECG than other sensors for most HRV indexes.

AcCorps: A low-cost 3D printed stethoscope for fetal phonocardiography

The analysis of fetal heart rate provides valuable information regarding the fetus wellbeing. Fetal phonocardiography is a low-cost and passive method allowing the acquisition of fetal heart rate by recording acoustic vibrations on the mother's abdomen. However, most of available stethoscopes are not optimized for a robust acquisition of fetal heart sound. In this publication, we investigated a new design of low-cost and 3D printed stethoscope. This device was optimized to provide an acoustic amplification especially in the low-frequency band which corresponds to the fetal heart sounds. This device was tested i) in silico, ii) on a test bench and iii) on 5 pregnant volunteers.

Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography

This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

Cardiac cycle-synchronized electrical muscle stimulator for lower limb training with the potential to reduce the heart's pumping workload

Background

The lower limb muscle may play an important role in decreasing the heart’s pumping workload. Aging and inactivity cause atrophy and weakness of the muscle, leading to a loss of the heart-assisting role. An electrical lower limb muscle stimulator can prevent atrophy and weakness more effectively than conventional resistance training; however, it has been reported to increase the heart’s pumping workload in some situations. Therefore, more effective tools should be developed.

Methods

We newly developed a cardiac cycle-synchronized electrical lower limb muscle stimulator by combining a commercially available electrocardiogram monitor and belt electrode skeletal muscle electrical stimulator, making it possible to achieve strong and wide but not painful muscle contractions. Then, we tested the stimulator in 11 healthy volunteers to determine whether the special equipment enabled lower limb muscle training without harming the hemodynamics using plethysmography and a percutaneous cardiac output analyzer.

Results

In 9 of 11 subjects, the stimulator generated diastolic augmentation waves on the dicrotic notches and end-diastolic pressure reduction waves on the plethysmogram waveforms of the brachial artery, showing analogous waveforms in the intra-aortic balloon pumping heart-assisting therapy. The heart rate, stroke volume, and cardiac output significantly increased during the stimulation. There was no change in the systolic or diastolic blood pressure during the stimulation.

Conclusion

Cardiac cycle-synchronized electrical muscle stimulation for the lower limbs may enable muscle training without harmfully influencing the hemodynamics and with a potential to reduce the heart’s pumping workload, suggesting a promising tool for effectively treating both locomotor and cardiovascular disorders.

Novel features from autocorrelation and spectrum to classify Phonocardiogram quality

Phonocardiogram (PCG) or auscultation via a stethoscope forms the basis of preliminary medical screening. But PCG recorded in an uncontrolled environment is inherently noisy. In this paper we have derived novel features from the spectral domain and autocorrelation waveforms. These are used to identify the quality of a PCG recording and accepting only diagnosable quality recordings for further analysis. These features proved to be robust irrespective of variations in devices and in data collection protocols employed to ensure consistent data quality. A freely available, large, diverse, medical-grade PCG dataset was used for creating the training models. Results show that the proposed methodology yields an accuracy score of ~75% on our in-house PCG dataset, collected using a low-cost smartphone-based digital stethoscope.

Detecting fetal heart sounds by means of Fractal Dimension analysis in the Wavelet domain

Phonocardiography is a low-cost technique for the detection of fetal heart sounds (FHS) that can extend clinical auscultation in mobile and home care setups. The work presented here examines the transferability of a Wavelet Transform (WT)-based method that combines also Fractal Dimension (FD) analysis, previously proposed as WT-FD for the cases of lung and bowel sound analysis [4], to the extraction of FHSs. The WT-FD method has been evaluated with 12 simulated FHS signals and has shown promising results in terms of accuracy and performance (89%) in identifying the location of heartbeat, even in cases of signals with additive noise up to (6dB). This robustness paves the way for WT-FD testing in real FHSs, recorded under clinical setting, clearly contributing to better evaluation of the fetal heart functionality.

Pattern discovery and similarity assessment for robust heart sound segmentation

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

Pattern discovery and similarity assessment for robust Heart Sound Segmentation

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

Design and evaluation of a parametric model for cardiac sounds

Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the Linear Predictive Coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal.

A robust dataset-agnostic heart disease classifier from Phonocardiogram

Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.

Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner

Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection. Thus, noise detection and elimination are crucial to ensure accurate clinical analysis. In this paper, we present a robust denoising technique, Proclean that precisely detects the noisy PCG signal through pattern recognition, and statistical learning. We propose a novel self-discriminant learner that ensures to obtain distinct feature set to distinguish clean and noisy PCG signals without human-in-loop. We demonstrate that our proposed denoising leads to higher accuracy in subsequent clinical analytics for medical investigation. Our extensive experimentations with publicly available MIT-Physionet datasets show that we achieve more than 85% accuracy for noisy PCG signal detection. Further, we establish that physiological abnormality detection improves by more than 20%, when our proposed denoising mechanism is applied.

Intelligent Phonocardiography for Screening Ventricular Septal Defect Using Time Growing Neural Network

This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system.

An open access database for the evaluation of heart sound algorithms

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Usefulness of the second heart sound for predicting pulmonary hypertension in patients with interstitial lung disease

CONTEXT AND OBJECTIVE

P2 hyperphonesis is considered to be a valuable finding in semiological diagnoses of pulmonary hypertension (PH). The aim here was to evaluate the accuracy of the pulmonary component of second heart sounds for predicting PH in patients with interstitial lung disease.

DESIGN AND SETTING

Cross-sectional study at the University of Brasilia and Hospital de Base do Distrito Federal.

METHODS

Heart sounds were acquired using an electronic stethoscope and were analyzed using phonocardiography. Clinical signs suggestive of PH, such as second heart sound (S2) in pulmonary area louder than in aortic area; P2 > A2 in pulmonary area and P2 present in mitral area, were compared with Doppler echocardiographic parameters suggestive of PH. Sensitivity (S), specificity (Sp) and positive (LR+) and negative (LR-) likelihood ratios were evaluated.

RESULTS

There was no significant correlation between S2 or P2 amplitude and PASP (pulmonary artery systolic pressure) (P = 0.185 and 0.115; P= 0.13 and 0.34, respectively). Higher S2 in pulmonary area than in aortic area, compared with all the criteria suggestive of PH, showed S = 60%, Sp= 22%; LR+ = 0.7; LR- = 1.7; while P2> A2 showed S= 57%, Sp = 39%; LR+ = 0.9; LR- = 1.1; and P2 in mitral area showed: S= 68%, Sp = 41%; LR+ = 1.1; LR- = 0.7. All these signals together showed: S= 50%, Sp = 56%.

CONCLUSIONS

The semiological signs indicative of PH presented low sensitivity and specificity levels for clinically diagnosing this comorbidity.

Exploring embedding matrices and the entropy gradient for the segmentation of heart sounds in real noisy environments

In this paper we explore a novel feature for the segmentation of heart sounds: the entropy gradient. We are motivated by the fact that auscultations in real environments are highly contaminated by noise and results reinforce our suspicions that the entropy gradient is not only robust to such noise but maintains a high sensitivity to the S1 and S2 components of the signal. Our whole approach consists of three stages, out of which the last two are novel contributions to this field. The first stage consists of typical pre-processing and wavelet reconstruction to obtain the Shannon energy envelogram. On the second stage we use an embedding matrix to track the dynamics of the system, which is formed by delay vectors with higher dimension than the corresponding attractor. On the third stage, we use the eigenvalues and eigenvectors of the embedding matrix to estimate the entropy of the envelogram. Finite differences are used to estimate entropy gradients, in which standard peak picking approaches are used for heart sound segmentation. Experiments are performed on a public dataset of pediatric auscultations obtained in real environments and results show the promising potential of this novel feature for such noisy scenarios.

[An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction]

In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.

De-noising the abdominal phonogram for foetal heart rate extraction: blind source separation versus empirical filtering

This work explored the suitability of using the foetal phonocardiogram (FPCG) blindly separated from the abdominal phonogram as a source for foetal heart rate (FHR) measuring in antenatal surveillance. To this end, and working on a dataset of 15 abdominal phonograms, the FPCG was estimated by using two de-noising approaches (1) single-channel independent component analysis (SCICA) to produce FPCG(e) and (2) empirical filtering to produce FPCG(g). Next, the FPCGs were further processed to collect the beat-to-beat FHR and the resulting time-series (FCTG(e) and FCTG(g) were compared to the reference signal given by the abdominal ECG (FCTG(r)). Results are promising, the FPCG(e) gives rise to a FCTG(e) that resembles FCTG(r) and, most importantly, whose mean FHR value is statistically equivalent to that given by FCTG(r) (p > 0.05). Thus, the mean FHR value obtained from the FPCG(e), is likely to be equivalent to the value given by the abdominal ECG, which is especially significant since the FPCG(e) is retrieved from the noisy abdominal phonogram. Hence, as far as this study has gone, it can be said that, when using SCICA to de-noise the abdominal phonogram, the resulting FPCG is likely to become a useful source for FHR collection in antenatal surveillance.

No influence of lower leg heating on central arterial pulse pressure in young men

Central arterial pulse pressure (PP), a strong predictor of cardiovascular disease, mainly consists of an incident wave generated by left ventricular ejection and a late-arriving reflected wave emanating from the lower body. We have tested the hypothesis that a reduction in leg vascular tone by heat treatment of the lower leg attenuates the central arterial PP. Pressure and wave properties of the peripheral and central arteries were measured in eight young men before and after heat treatment of the lower leg (temperature approx. 43 °C) for 30 and 60 min, respectively. Following the lower leg heat trial, leg (femoral-ankle) pulse wave velocity (PWV) was significantly decreased, but aortic (carotid-femoral) PWV and parameters of wave reflection and carotid arterial PP did not change significantly. No significant changes were observed in these parameters in the control trial. These results suggest that the reduction in leg vascular tone induced by heat treatment of the lower leg may not affect wave reflection and central arterial PP in young men.

Assessment of left ventricular function by noninvasive methods

The possibility of evaluating left ventricular function by noninvasive methods is discussed in detail. The methods that are considered are electrocardiograph, phonocardiography, apex cardiography, sphygmography, impedance cardiography, electrokymography, and echocardiography. Following a brief section of 'definitions', each method is described in detail including technical problems, difficulties, and results. The systolic time intervals and the stress tests are briefly discussed. Based on modern experimental studies, the stress test should include both an electro- and a phonocardiogram. In the latter, one would measure the amplitude of the first heart sound as an index of contractility. The conclusion is that combined methods give the best results. They are electrocardiography, phonocardiography, impedance cardiography, and echocardiography. An alternative, dictated by technical problems, is to use at first phonocardiography and impedance plus electrocardiography; then echocardiography plus electrocardiography; and then, if indicated, a stress test might complete the study; the latter should include both an electrocardiogram and a phonocardiogram.

Nonalcoholic fatty liver disease as a risk factor of arterial stiffness measured by the cardioankle vascular index

Nonalcoholic fatty liver disease (NAFLD) is associated with risk factors for cardiovascular disease. The cardioankle vascular index (CAVI), a new measure of arterial stiffness, was recently developed and is independent of blood pressure. We investigated whether NAFLD is associated with arterial stiffness as measured using the CAVI in an apparently healthy population.A total of 2954 subjects without any known liver diseases were enrolled. NAFLD was diagnosed via typical ultrasonography. The clinical characteristics examined included age, sex, body mass index (BMI), waist circumference (WC), and the levels of aspartate aminotransferase, alanine aminotransferase, total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol triglycerides, and glucose. Arterial stiffness was defined using an age- and sex-specific threshold of the upper quartile of the CAVI.NAFLD was found in 1249 (42.3%) of the analyzed subjects. Using an age-, sex-, and BMI-adjusted model, NAFLD was associated with a 42% increase in the risk for arterial stiffness (highest quartile of the CAVI). The risk for arterial stiffness increased according to the severity of NAFLD (adjusted odds ratio [95% confidence interval], 1.27 [1.02 - 1.57] vs 1.78 [1.37 - 2.31], mild vs moderate-to-severe, respectively). When adjusted for other risk factors, including BMI, WC, smoking status, diabetes, and hypertension, these relationships remained statistically significant.Patients with NAFLD are at a high risk for arterial stiffness regardless of classical risk factors. The presence of cardiometabolic risk factors may attenuate the prediction of arterial stiffness by means of NAFLD presence. Thus, physicians should carefully assess subjects with NAFLD for atherosclerosis and associated comorbidities.