Classification of heart murmurs using cepstral features and support vector machines

In this paper we present the implementation of a diagnostic system based on artificial neural networks (ANN) that can be used in the detection and classification of heart murmurs. Segmentation and alignment algorithms serve as important pre-processing steps before heart sounds are applied to the ANN structure. The system enables users to create a classifier that can be trained to detect virtually any desired target set of heart sounds. The output of the system is the classification of the sound as either normal or a type of heart murmur. The ultimate goal of this research is to implement a heart sounds diagnostic system that can be used to help physicians in the auscultation of patients and to reduce the number of unnecessary echocardiograms - those that are ordered for healthy patients. Testing has been conducted using both simulated and recorded patient heart sounds as input. Three sets of results for the tested system are included herein, corresponding to three different target sets of simulated heart sounds. The system is able to classify with up to 85 plusmn 7.4% accuracy and 95 plusmn 6.8% sensitivity. For each target set, the accuracy rate of the ANN system is compared to the accuracy rate of a group of 2nd year medical students who were asked to classify heart sounds from the same group of heart sounds classified by the ANN system. System test results are also explored using recorded patient heart sounds

Power transmission lines are extremely important for the power system to deliver energy of electricity from the plant to the load. The short circuit of fault often occurs in the transmission line and may lead to disconnecting the power supply to the load. This study implements a hybrid technique that is Discrete Wavelet Transformation (DWT) and Support Vector Machine (SVM) for classification of fault in the transmission line. The DWT was created to extract the detailed signal of transient D8 and D9 (order of 4) at 50 kHz for sampling frequency. The value of Root Mean Square (RMS) will be determined by the coefficients D8 and D9 for training and test data using SVM technique. Furthermore, SVM is utilized to detect the fault for each phase and the ground is discovered in the type of fault. The SVM technique has been run using parameter C and kernel scale to achieve the great results of classification of the fault. Type of simulating fault has a variation of location of the fault, fault of resistance and initial angle. The training and test data run for the Test System of Riau, Indonesia. The test result for the classification of fault reaches the highest accuracy of 100%.

Heart sounds entail crucial heart function information. In conditions of heart abnormalities, such as valve dysfunctions and rapid blood flow, additional sounds are heard in regular heart sounds, which can be employed in pathology diagnosis. These additional sounds, or so-called murmurs, show different characteristics with respect to cardiovascular heart diseases, namely heart valve disorders. In this paper, we present a method of heart murmur classification composed by three basic steps: feature extraction, feature selection, and classification using a nonlinear classifier. A new set of 17 features extracted in the time, frequency and in the state space domain is suggested. The features applied for murmur classification are selected using the floating sequential forward method (SFFS). Using this approach, the original set of 17 features is reduced to 10 features. The classification results achieved using the proposed method are compared on a common database with the classification results obtained using the feature sets proposed in two well-known state of the art methods for murmur classification. The achieved results suggest that the proposed method achieves slightly better results using a smaller feature set.

This study compares single and hybrid soft computing models for estimating daily solar radiation flux for two scenarios. Scenario I developed single soft computing models, including multilayer perceptron (MLP), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multivariate adaptive regression splines (MARS), for estimating daily solar radiation flux at two stations from the USA and South Korea. The MLP model was used to evaluate the effect of factors controlling daily solar radiation flux. Using different combinations of controlling factors as input, the MLP and SVM models, based on evaluation measures, were found to be superior to the ANFIS and MARS models at Big Bend station, USA. In addition, the MLP, SVM, and MARS models performed better than did the ANFIS model at Incheon station, South Korea. Scenario II combined the discrete wavelet transform (DWT) and single soft computing models (e.g., MLP and SVM) for improved performance using 4-input combination. The wavelet-based MLP (WMLP) and SVM (WSVM) models were superior to other single soft computing models (MLP, SVM, ANFIS, and MARS) at two stations. Taylor diagrams, violin plots and point density plots were also utilized to examine the similarity between the observed and estimated solar radiation flux values. Results showed that scenarios I and II can be alternatives for estimating daily solar radiation flux based on different meteorological information, such as peninsular and continental conditions.

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.

Phonocardiogram in adult patients with tetralogy of Fallot

Diastolic murmur in mid-ventricular obstructive hypertrophic cardiomyopathy: A case report

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

Information and communication technology usage in the healthcare sector is not perceptible due to various challenges with increased healthcare needs. With the outburst of COVID-19, when the different countries announced lockdown and social distancing rules, it is crucial to predict a person's symptoms, which will help in the early diagnosis. In such situations, there is a tremendous growth seen in the usage of various technologies, such as remote health monitoring, Wireless Body Area Networks (WBANs), Machine Learning (ML), and Decision Support system (DSS). Hence, the chapter focuses on detecting diseases and associated symptoms using various ML algorithms. A total of 3073 patient data (heartbeat, snore, and body temperature) has been collected. The collected data were preprocessed to remove empty cells and zero values by replacing the mean of the cells. Later, the extracted features were used in Empirical Mode Decomposition (EWD) and Discrete Wavelet Transformation (DWT). Then, the optimized algorithms with the threshold values were identified by consulting doctors for accurate disease prediction. With the testing performance of various ML algorithms, such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), Gradient Descent (SGD), Naive Bayes (NB), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF), was compared. Performance evaluation parameters are accuracy, precision, F1 score, and recall. The results showed an average of 100% accuracy with SGD and SVM with DWT, whereas EMD, SVM, and MLP outperformed the state-of-the-art algorithms with 99.83% accuracy.

Phonocardiography of the human fetus

This study presents a new framework for human identity recognition using heart sound signals. The proposed framework is based on extracting cepstral features from heart sound signals, which are known as phono-cardio-gram (PCG). Two well-known cepstral features have been adopted in most of the previously implemented PCG biometric authentication systems; namely, mel-frequency and linear frequency cepstral features. In this study, two more cepstral features are proposed based on modifying the mel-frequency cepstral features. The first one is based on modifying the mel-frequency equation to increase the non-linearity of the triangular filters in the frequency range of the PCG signal. The other is based on replacing mel-scaled triangular filters with wavelet packet filters where a non-linear filter bank structure is designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. The proposed system uses wavelet de-noising for pre-processing and linear discriminant analysis for classification. The proposed system is evaluated on two databases; one consists of 21 users (BioSec. database) and the other consists of 206 users (HSCT-11 database). Moreover, the proposed system is compared with previous systems that used the same databases. On the basis of the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.

The incidence of patients with degenerative valvular but also of patients with congenital heart disease surviving until adulthood or even old age will increase in the next decades. Auscultation with the stethoscope remains an important diagnostic means in the detection and treatment of heart disease. Heart murmurs (especially systolic heart murmurs) are extremely common. There are helpful clues to differentiate heart murmurs. It can occasionally be relatively simple to differentiate a systolic murmur due to valvular heart disease from an innocent, ejection murmur; however, there are important limitations of auscultation. Overall, auscultation and clinical examination alone do not suffice to correctly diagnose and treat patients with heart failure or a murmur Clinically significant aortic stenosis, aortic regurgitation and mitral regurgitation as well as hypertrophic cardiomyopathy are not uncommonly missed or misinterpreted. An echocardiographic exam is mandatory in all patients with more than a soft systolic murmur, any diastolic murmur, cardiac symptoms and/or ECG changes.

Detection and classification of multiple power quality disturbances in Microgrid network using probabilistic based intelligent classifier

Recently, many studies were performed using several techniques to classify and diagnose lung sound, but as a drawback the age category was limited, almost adult only, as well as the insufficient number of samples and this unfortunately leads to an unfair classification of lung sound. While this study deals with different methods to analyze lung sounds and extract distinctive features then classify them to diagnose lung sounds in infant and children to one of the three categories: Normal, Wheeze, or Stridor. Features were extracted using three different techniques in separate ways to compare the effectiveness; these techniques are Discrete Wavelet transform (DWT), Short Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCCs). After that the sounds are categorized using four different classification techniques which include Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naive Bayes (NB). The main aim of this research is to choose the best signal processing technique with the most suitable classifier to diagnose lung sounds by categorizing 300 lung sounds especially in infants and children to Normal, Wheeze, or Stridor. These sounds are collected from Alexandria University Children Hospital (AUCH) – Egypt as a particular environment which is considered one of the main advantages of this research. Moreover, extra 146 wheezes were used to validate the usefulness of the classifiers. The results were very promising.

For an automated classification of heart murmurs there is a need of detailed analysis of heart sounds both in time and frequency domain. In this work the possibility of extracting the formant frequencies from the phonocardiogram was examined. The purpose of examination described here was to obtain a result which could facilitate the classification by offering additional information about the sound characteristics.