In this paper, we have proposed a new technique for voice activity detection (VAD) using lacunarity index combined with empirical mode decomposition (EMD) technique. In the preprocessing stage of the proposed framework, the noisy speech... more
In this paper, we have proposed a new technique for voice activity detection (VAD) using lacunarity index combined with empirical mode decomposition (EMD) technique. In the preprocessing stage of the proposed framework, the noisy speech signal is decomposed into several intrinsic mode functions (IMFs) based on EMD technique. After that more informative IMFs are selected using spectral flatness measurement (SFM) approach. In the decision stage, the speech signal frames are identified by a threshold limit. The threshold value is calculated using statistical parameter of the lacunarity index of the reconstructed speech signal. The proposed technique gives superior results over existing techniques and is more effective at moderate noise levels.
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The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the... more
The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the signal. To overcome this problem we need an automated computer-aided diagnostic system that will be competent in noisy environment. In this paper, a novel feature extraction technique is introduced for discriminating various pulmonary dysfunctions in an automated way based on pattern recognition algorithms. In this work, the disease correlated relevant characteristics of lung sounds signals are identified in terms of statistical distribution parameters: mean, variance, skewness, and kurtosis. These features are extracted from selective morphological components of the mapped signal in the empirical mode decomposition domain. The feature set is fed to the classifier model to differentiate their corresponding classes. The significance of features developed ...
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Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac... more
Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individu...
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The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information which are useful for recognition of heart diseases. The unwanted signals are produced... more
The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound de-noising technique has been introduced based on a combined framework of wavelet packet transform (WPT) and singular value decomposition (SVD). The most informative node of wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biologica...
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An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound... more
An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (−5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classif...
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A computerized cardiac disorders classification system needs a proper boundary estimated cardiac cycle of recorded heart sound signals. In the proposed algorithm the boundaries of two primary heart sounds, S1 and S2 events are estimated... more
A computerized cardiac disorders classification system needs a proper boundary estimated cardiac cycle of recorded heart sound signals. In the proposed algorithm the boundaries of two primary heart sounds, S1 and S2 events are estimated using Hilbert transform method and a statistical approach. This method uses an adaptive threshold value which is calculated from the first- and second-order moments of the heart sound or Phonocardiogram signal (PCG) envelope. Here, Hilbert transform is used for extracting the envelope of the PCG signal and a threshold is applied to detect the boundary regions of the primary events, S1 and S2, of the same signal. The performance of the algorithm is evaluated for normal and five commonly occurring pathological cases. The proposed method is computationally fast and obtained an accuracy of 97.24 percent.
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After the invention of stethoscope by Lannec [1] in 1816; it is employed to detect lung diseases, but traditional auscultation with a stethoscope does not meet the requirements for a diagnostic test due, primarily, to limitation of the... more
After the invention of stethoscope by Lannec [1] in 1816; it is employed to detect lung diseases, but traditional auscultation with a stethoscope does not meet the requirements for a diagnostic test due, primarily, to limitation of the human auditory system [2]. The human ears are sensitive to a certain sound frequency range only (e.g. 15 to 20,000 Hz) and some significant lung sounds are not in this range and therefore will not be heard, even by the most highly trained physician. Another reason for human deficiency in the auscultatory analysis of lung sounds is the interference of heart sound signal with lung sound signals and other noises. The heart sound signals interfere and may even obscure the correct interpretation of lung sounds and can lead to an inaccurate diagnosis of the lung diseases. It is highly desirable, for accurate assessment of the lung status based on lung sound information, to remove the heart sound noise from the desired lung sound signals. The claimed inventi...
Heart disease is the second leading cause of human beings death, and most of these diseases occur due to the improper function of the heart valves [1]. Almost all diagnosis techniques of cardiac disorder require relevant features from the... more
Heart disease is the second leading cause of human beings death, and most of these diseases occur due to the improper function of the heart valves [1]. Almost all diagnosis techniques of cardiac disorder require relevant features from the heart sound characteristic pattern [2], [3]. The mechanical activity of heart produces sound that provides significance audio clue about the cardiac status [4]. In the ancient time, physicians were examined the heart status or diseased conditions by listening to the acoustical characteristics of sound signal directly from the chest wall of the patients through the listening organs (auricles). This old-unreliable procedure of chest sounds analysis is replaced by an electronics device is called the stethoscope tool that was invented by French physician Lannec in 1816 [5]. The stethoscope device is a very easy handling tool for auscultation of cardiac disorders and in addition it is a cost effective and non-invasive approach [6], [7]. It has also some...
Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. We... more
Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. The proposed method gives a classification ...
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ABSTRACT Lung sound (LS) contains information regarding the lungs status. Medical practitioners listen to these sounds using stethoscope and make interpretation. This procedure is known as auscultation which totally depends on the... more
ABSTRACT Lung sound (LS) contains information regarding the lungs status. Medical practitioners listen to these sounds using stethoscope and make interpretation. This procedure is known as auscultation which totally depends on the physicians experience and knowledge. There is a probability of misinterpretation due to human factor involved. In this paper, we propose a method based on complexity measuring theorem that can give reliable diagnosis of LS in an automated environment. The developed algorithm detects the lung conditions by calculating the sample entropy value of the frequency spectrum. The results are evaluated through statistical analysis and corroborated by a pulmonologist. The technique could be very useful in developing assisting device for medical professionals.