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This research pertains to classification of the heart sound using digital Phonocardiogram (PCG) signals targeted to screen for heart ailments. In this study, an existing variant of the decision tree, i.e. XgBoost has been used with unsegmented heart sound signal. The dataset provided by PhysioNet Computing in Cardiology (CinC) Challenge 2016 has been used to validate the technique proposed in this research work. The said dataset comprises six databases (A–F) having 3240 heart sound recordings in all with the duration lasting from 5–120 s. The approach proposed in this paper has been compared with 18 existing methodologies. The proposed method is accurate with the mean score of 92.9, while sensitivity and specificity scores are 94.5 and 91.3, respectively. The timely prediction of heart health will support specialists to attain useful risk stratification of patients and also assist clinicians in effective decision-making. These predictive facts may serve as a guide to provide improved quality of care to the patients by way of effective treatment planning and monitoring.

The current standard technique for blood pressure determination is by using cuff/stethoscope, which is not suited for infants or children. Even for adults such an approach yields 60% accuracy with respect to intra-arterial blood pressure measurements. Moreover, it does not allow for continuous monitoring of blood pressure over 24 h and days. In this paper, a new methodology is developed that enables one to calculate the systolic and diastolic blood pressures continuously in a non-invasive manner based on the heart beats measured from the chest of a human being. To this end, we must separate the first and second heart sounds, known as S₁ and S₂, from the directly measured heart sound signals. Next, the individual characteristics of S₁ and S₂ must be identified and correlated to the systolic and diastolic blood pressures. It is emphasized that the material properties of a human being are highly inhomogeneous, changing from one organ to another, and the speed at which the heart sound signals propagate inside a human body cannot be determined precisely. Moreover, the exact locations from which the heart sounds are originated are unknown a priori, and must be estimated. As such, the computer model developed here is semi-empirical. Yet, validation results have demonstrated that this semi-empirical computer model can produce relatively robust and accurate calculations of the systolic and diastolic blood pressures with high statistical merits.

Fetal phonocardiography is a simple and noninvasive diagnostic technique for surveillance of fetal well-being. The fetal phonocardiographic (fPCG) signals carry valuable information about the anatomical and physiological states of the fetal heart. This article is concerned with a study of continuous wavelet transform (CWT)-based scalogram in analyzing the fPCG signals. The scalogram has both spatial and frequency resolution powers, whereas traditional spectral estimation methods only have the frequency resolution ability. The fPCG signals are acquired by a specially developed data recording system. Segmentation of these signals into fundamental components of fetal heart sound (S1 & S2) is carried out through envelope detection and thresholding techniques. CWT-based scalogram is used for time-frequency characterization of the segmented fPCG signals. It has been shown that the wavelet scalogram provides enough features of the fPCG signals that will help to obtain qualitative and quantitative measurements of the time-frequency characteristics of the fPCG signals and consequently, assist in diagnosis. The proposed method for time-frequency analysis (TFA) and the associated pre-processing have been shown to be suitable for the characterization of fPCG signals, yielding relatively good and robust results in the experimental evaluation.

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

A soft-computing method attenuating noise from heart sound (HS) signal for wearable e-healthcare device is proposed. The HS signal is decomposed by third-level wavelet packet transform (WPT). An automatic HS cycle detection algorithm is applied to find HS cycles in the (3, 0) leaf signal of WPT tree. Based on the quasi-cyclic feature of HS, short-time Fourier transform is implemented for cycles of each WPT tree leaf signal to decompose each cycle into time-frequency fragments which are called particles. Furthermore, the novel cuboid method is proposed to identify constituents of HS and noise from such generated particles. The particles representing HS are then retained and merged into noise-quasi-free WPT tree leaf signals. Eventually the inverse WPT (IWPT) is employed to build the noise-quasi-free HS signal. The method is assessed using mean square error (MSE) and compared with wavelet multi-threshold method (WMTM) and Tang's method. The experimental results show that the proposed method not only filters HS signal effectively but also well retains its pathological information.

The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than $96\%$ on both datasets show the effectiveness of the proposed model.

Heart sound signal processing is a low-cost, and noninvasive method for the early diagnosis of various types of cardiovascular diseases. In this study, a parallel diagnosing method was proposed to detect various types of heart diseases and healthy heart samples. The proposed system can detect a person who might be simultaneously suffering from two or more heart diseases. Contributing to this line of investigation, effective features were obtained from the morphological and statistical features extracted from five frequency ranges of heart sounds. Applying such features in diagnosing any heart disease acts as a fingerprint specific to that disease. Therefore, the investigation of selected features, especially in each of the frequency ranges of heart sounds and murmurs, provided us with valuable information about the behavior of the diagnostic system in the detection of heart diseases. In addition to using features related to the nature of heart sounds, the proposed method of this study got rid of both the need to apply different filters needed to remove noise and dependence on a specific dataset. With the aid of the effective features in the parallel diagnosis of 15 different types of important and common heart diseases and a healthy class from each other, the diagnostic system of the present study was able to achieve the average accuracy of 97.06%, the average sensitivity of 97.99%, and the average specificity of 96.18% in the shortest possible time. The proposed approach is an important step in the screening and remote monitoring and tracking of disease progression.

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

There has been a steady rise in the number of deaths throughout the world due to heart diseases. This can be mitigated, to a large extent, if cardiovascular disorders can be detected timely and efficiently. Electrocardiograms (ECGs) and phonocardiograms (PCGs) are the two most popular diagnostic tools used for detecting cardiac problems. Another simple and efficient method for quickly identifying cardiovascular illness is Auscultation. In this work, the cardiac sound signal has been transformed into its equivalent spectrogram representation for detecting cardiac problems. The novelty of the proposed approach is the deployment of customized transfer learning (TL) models on sub-component of a spectrogram called Harmonic Spectrogram, instead of taking full spectrogram. Experiments have been conducted using PhysioNet 2016, which is considered a benchmark dataset. TL models, viz. MobileNet, DenseNet121, InceptionResnetV2, VGG16, and InceptionV3 have been put to use for categorizing cardiac sound waves as normal or pathological. The results exhibit that the MobileNet has achieved greater accuracy (93.45%), recall (92.46%), Precision (97.82%), F1 Score (95.06%) than many of the peers.

The soft sensors for monitoring respiratory and heart sound were composed of polyurethane and microphones. In this study, silica was blended with polyurethane to change the hardness of the chambers. The hardness would influence the frequency response of the sensors. The material composed of 60 phr silica was chosen to make the chamber of the sensor. It had higher hardness and resulted in the flatten frequency response across the range of 100–1200 Hz. By the filter band designed for heart sound and respiratory sound signal, the heart sound and respiratory sound can be collected. The measured sound was verified by the physician and showed no distortion.

Acoustic stethoscopes are used to monitor signals from patients. Incidentally, the connecting tube between the chest-piece and the ear-piece of common stethoscopes is known to serve as a medium for transmitting pathogens from patients to physicians or from one patient to another patient. This work presents a wireless stethoscope design with mobile integration that transmits heart sounds to mobile devices for evaluation and analysis, thus, eliminates the connecting tube. This is an extension of the previous work that presented the proof of concept of a wireless stethoscope with Bluetooth transmission. In this work, however, the chest-piece of the traditional stethoscope is integrated with microcontroller unit and Bluetooth communication device. Captured signals are processed and transmitted wirelessly to a mobile device with interface application software for recording, listening and visual display of waveforms. Following numerical simulation, a prototype was developed. Testing conducted on the prototype device using a class 2 Bluetooth device with 4dBm transmission power showed good quality received signals when the mobile device was placed 20m within indoor environments and 42m in open-space outdoor environment, beyond which degradation in quality occurs. It is worth pointing out that a smart Bluetooth device with high transmission range and data speed may produce much longer operating distance. Hence, for applications that require operating ranges beyond 50m, smart Bluetooth devices may be well suited for such systems. This system may serve as the means for monitoring patients from remote locations particularly quarantine units and can also be useful for training health personnel through broadcasting of recorded signals for analysis and evaluation by members of a medical team.

Cardiac auscultation is a basic means of initial diagnosis of congenital heart disease (CHD). It is significant to analyze Phonocardiogram (PCG) by using signal processing and machine learning techniques for the purpose of machine-assisted diagnosis of CHD. A novel method of machine-assisted diagnosis of CHD was proposed in this paper. First, the duration of the hidden Markov model (DHMM) was applied to locate and segment PCG into each cardiac cycle. Then, the envelope of the PCG was extracted by using Viola integral. After that, the variant logic theory was applied to extract the features and convert the envelope data of the heart sound signal into visual analysis measurement data. Finally, the classifier of the support vector machine (SVM) was used to classify the normal and abnormal heart sounds. There were 1000 cases used in this study. It was divided into a training set of 600 cases, a test set of 200 cases, and validation set of 200 cases. An accuracy of 0.965, a specificity of 0.898, and a sensitivity of 0.937 were achieved using the novel signal processing techniques. The results showed that DHMM and variant logic theory models were suitable for heart sound classification.

Diseases of the heart have become the Number One cause of death in the industrialized nations of the world. Every heart disease affects the biomechanics of the heart in a direct or indirect way. These effects can primarily be analyzed by the signals of the heart sounds and cardiac murmurs using techniques such as auscultation and phonocardiography. But these methods are very sophisticated and require a high degree of specialization. During the last years electronic stethoscopes and commercial PC techniques have improved essentially so an automatic analysis of heart sound has become a potential supporting tool for physicians in particular as a screening method for heart diseases by the general practitioner. This paper introduces a new automatic system to diagnose heart valve diseases based on time and frequency analyzing methods and feature extraction. It also describes an multivariate approach for an enhanced risk stratification in patients with heart failure considering the time interval between the ECG signal, especially the R-wave, and the first heart sound.

In conclusion we could demonstrate, that analysis of the heart sound is a suitable method to evaluate the state of the heart and to detect changes on the biomechanics at an early stage.