Analysis of phonocardiogram signals using wavelet transform

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds

Objective. Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification. Approach. A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model. Main results. The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or/and multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively. Significance. PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.

Identifying the precise Heart Sounds (HS) positions inside a Phonocardiogram (PCG); otherwise, Heart Sounds Segmentation (HSS) is a vital phase for the automatic examination of recordings of HS, permitting for the categorization of pathological proceedings. Analysis of HS signals (explicitly, PCG) in the last some decades, particularly for automated HSS and also classification, was largely learned and also stated to encompass the possible value for detecting pathology precisely in medical applications since the bad outcomes in these stages will ruin or shatter the HS detection system’s efficiency. Therefore, the PCG detection issues to implement a new efficient algorithm are required to be discussed. Here, the recently published pre-processing, segmentation, Feature Extractions (FE), and also classification techniques along with their top-notch of PCG signal examination were reviewed. Associated studies are contrasted with their datasets, FE, and the classifiers that they utilized. This effort aims to analyze all the research directions in PCG detection techniques. At the last of this appraisal, several directions for future researches toward PCG signal analysis are rendered.

Cardiovascular diseases (CVDs) are potentially detected using heart sound characteristics. The presence of pathological heart murmurs is associated with cardiac abnormalities. In this paper, a noble automatic detection framework that employs a cascaded variational mode decomposition (VMD) and acoustic-based features of the nonstationary phonocardiogram (PCG) signals is proposed. The main objective is to investigate whether a reliable detection of heart murmurs can still be achieved when the dataset is imbalanced. In light of this, the proposed work has been extended for the imbalanced dataset by incorporating Adaptive Synthetic (ADASYN) sampling approach that learns from the minority samples to detect heart murmurs as the performance of the conventional classifiers degrades when the dataset is imbalanced. The result analysis shows the promising outcome on both the balanced and imbalanced datasets in the correct recognition of heart murmurs thereby paving a way to assist cardiologist for the diagnosis of heart diseases.

Ubiquitous Perturbations in cardiac auscultation properties, cardiovascular diseases (CVDs) are widely recognized. In the auscultation procedure, the appearance of pathological cardiac murmurs is linked to heart disorders. A noble automated detection system using 1-D Convolutional Neural Network (CNN) for the detection of pathological heart murmurs is proposed in this study, which removes the difficult task of extracting and selecting features. It directly acts on the phonocardiogram (PCG) signals. The fundamental purpose of this research is to develop a classification model for consistent recognition of cardiac murmurs when the data-set is imbalanced. In view of this, the proposed study for the imbalanced data-set incorporates the Adaptive Synthetic (ADASYN) approach to generate synthetic data for the minority class. The outcome analysis illustrates the positive result in the identification of heart murmurs on both balanced and imbalanced data-sets. Therefore, the developed deep learning model will learn better from the minority class and classify heart murmurs accurately.

Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

A phonocardiogram (PCG) signal holds aural information generated by the heart during a cycle. A close examination of the PCG signal can reveal valuable cardiac information thereby allowing detection of abnormalities and diagnosis of heart diseases. An automation-aided analysis of PCG signals can play a vital role in the medical field, especially in remote patient monitoring, apart from being a very efficient approach. In this study, PCG signals are classified under 5 different classes based on the features extracted. The five classes are normal, mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic stenosis (N, MS, MR, MVP, AS). Mel-Frequency Cepstral Coefficients (MFCCs) are extracted from the PCG audio signals and fed into a deep learning based convolutional neural network (CNN). The proposed approach achieves a maximum accuracy of 99.64% which outperforms the existing state-of-the-art approaches.KeywordsMel-Frequency Cepstral Coefficients2-D Convolutional Neural NetworkCross Validation

Phonocardiogram signal analysis for classification of Coronary Artery Diseases using MFCC and 1D adaptive local ternary patterns

ABSTRACTCardiovascular disease is one of the leading causes of death worldwide. Early and accurate detection of abnormal cardiac activity can be an effective way to prevent serious cardiovascular events. Electrocardiogram (ECG) and phonocardiogram (PCG) signals provide an objective evaluation of the heart's electrical and acoustic functions, enabling medical professionals to make an accurate diagnosis. Therefore, the cardiologists often use them to make a preliminary diagnosis of abnormal cardiac activity in clinical practice. For this reason, many diagnostic models have been proposed. However, these models fail to utilize the interaction information within and between the signals to aid the diagnosis of disease. To address this issue, we designed a residual dual‐attention network (ResAN) for the detection of abnormal cardiac activity using synchronized ECG and PCG signals. First, ResAN uses a feature learning module with two parallel residual networks, for example, ECG‐ResNet and PCG‐ResNet to automatically learn the deep modal‐specific features from the ECG and PCG sequences, respectively. Second, to fully utilize the available information of different modal signals, ResAN uses a dual‐attention fusion module to capture the salient features of the integrated ECG and PCG features learned by the feature learning module, as well as the alternating features between them based on the attention mechanisms. Finally, these fused features are merged and fed to the classification module to detect abnormal cardiac activity. Our model achieves an accuracy of 96.1%, surpassing the performances of comparison models by 1.0% to 9.9% when using synchronized ECG and PCG signals. Furthermore, the ablation study confirmed the efficacy of the components in ResAN and also showed that ResAN performs better with synchronized ECG and PCG signals compared to using single‐modal signals. Overall, ResAN provides a valid solution for the early detection of abnormal cardiac activity using ECG and PCG signals.

A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository https://github.com/shamiktiwari/ PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05-0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20-70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.

Coronary Artery Disease (CAD) is a significant cause of increased mortality worldwide. Long-term monitoring and accurately assessing can effectively reduce the mortality rate associated with CAD. Electrocardiogram (ECG) and phonocardiogram (PCG) are simple, affordable, and non-invasive approaches that reflect the heart's physiological state, with ECG detecting electrical activity abnormalities caused by ischemia and PCG providing insights into mechanical function and contractility. This study simultaneously recorded ECG and PCG signals from 214 subjects, who were categorized into four groups based on varying degrees of coronary artery stenosis: severe CAD (sCAD, >70%), mild CAD (mCAD, 30%-70%), suspected CAD (susCAD, <30%), and healthy control (Health). To identify pathological changes in ECG and PCG signals, our approach locates key points in ECG (P, Q, R, S, T) and PCG (S1, S2, systole, diastole), and extracted various interval sequences (RR, QT, PR, S1, S2, systole, diastole) and amplitude sequences (P, Q, R, T, ST, S1, S2, systole, diastole). Subsequently, multi-domain features, including time-domain, frequency-domain, and nonlinear features, were computed, after which Recursive Feature Elimination (RFE) was employed to select the optimal feature subset, while Support Vector Machine (SVM) was utilized to identify CAD severity. Result showed that the joint analysis outperforms traditional single-signal methods in detecting CAD severity, and the addition of amplitude features can extract more pathological information and enhance classification performance, achieving accuracies of 93.82%, 97.69%, and 97.14% for the three degrees of coronary artery disease, respectively. Furthermore, this study confirms the feasibility of using ECG and PCG signals for the early screening of coronary artery disease.

Phonocardiography (PCG) is a noninvasive method for obtaining recordings of cardiovascular sound, that is, acoustic phenomena that are perceivable by auscultation at the cardiac region on the chest wall. Essentially, vibrations of the chest surface are observed that are generated in cardiac structures and transmitted to the chest wall. In physiological terms, heart sounds and murmurs are mentioned: Heart sounds have a transient, musical character; murmurs, except for the musical types, have a random, noisy character. Normal heart sounds are initiated by valve closing (the first and the second sound) or by filling phenomena (the third and the fourth sound); besides the normal ones pathological sounds exist, for example, generated at opening of degenerated valves. Heart murmurs of the random type generally result from turbulence in blood flow; the latter can be caused by flow through a narrowed valve opening or a leaking valve. Besides a small number of normal types, most heart murmurs are pathological and many of these can be related to malfunctioning of cardiac valves. Phonocardiography and auscultation thus aim at the recognition of pathological sounds and murmurs and their relation to malfunctioning heart structures. Recording of heart sounds at the chest surface, as for any vibration recording, requires a vibration transducer (also called a heart vibration pickup, a heart sound microphone), a suitable amplifier with specific frequency characteristics and a storage, visualization, and processing device. Contact transducers (accelerometers) and air‐coupled transducers are used. The first type can be indicated as absolute, the second as relative as it measures a weighted difference in vibration between the air chamber surface and the surface at the edge of the rigid housing of the transducer. The sensing chest piece of the (electronic) stethoscope accords with the second type. Generally, quantitative measurement requires a transducer that does not affect the vibration to be measured. This is hard to achieve in vibration measurement on soft tissue as is the case for the chest wall. Affecting the vibrating surface is, for example, a technique applied in heart auscultation: with the use of a stiff membrane and by applying firm pressure on the stethoscope chest piece higher frequencies are better observed. In order to obtain equivalent results as from auscultation (due to the qualities of the human hearing sense) high pass frequency filter sets have to be applied on recorded vibrations in phonocardiography. A single signal representation does not provide the same information as heard with the stethoscope. On the other hand, heart sound recording can exceed the hearing abilities and inaudible sounds can be recorded. In conventional phonocardiography, signals are stored on paper with a suitable graphic recorder; Evidently, modern systems make use of information technology for storage, processing, and visualization. As a result of different transducers, an important interference with the chest wall vibration and different signal conditioning, heart sound recording is not standardized, as compared with electrocardiography, which has a clear millivolt scale on its ordinate axis: The method can be indicated semiquantitative as results depend on the measuring apparatus used. Nevertheless, knowledge of heart sounds and murmurs has been greatly increased with this technique. Signal analysis, more specifically time–frequency analysis, has proven to be very useful in the identification and classification of heart sound components and murmurs, and their relation to cardiac structures and hemodynamic variables. Whereas auscultation still holds its position as a diagnostic tool for general physicians and cardiologists, phonocardiography in its classical form has lost interest mostly as a result of the availability of techniques as Doppler echocardiography and cardiac imaging methods, which give more direct information on cardiac structures and hemodynamic variables. The historical value of conventional phonocardiography has to be stressed. On the other hand, recording and processing of heart sounds continue to be subjects of scientific research and remain beneficial for training and for supporting of diagnosis of physicians. Electronic stethoscopes coupled to a laptop and connected to the internet for automated or for remote diagnosis by a specialist may grow in importance in the next few years.

Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals

The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.