Bayesian denoising framework of phonocardiogram based on a new dynamical model

In this paper, we introduce a model-based Bayesian denoising framework for phonocardiogram (PCG) signals. The denoising framework is founded on a new dynamical model for PCG, which is capable of generating realistic synthetic PCG signals. The introduced dynamical model is based on PCG morphology and is inspired by electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can represent various morphologies of normal PCG signals. The extended Kalman smoother (EKS) is the Bayesian filter that is used in this study. In order to facilitate the adaptation of the denoising framework to each input PCG signal, the parameters are selected automatically from the input signal itself. This approach is evaluated on several PCGs recorded on healthy subjects, while artificial white Gaussian noise is added to each signal, and the SNR and morphology of the outputs of the proposed denoising approach are compared with the outputs of the wavelet denoising (WD) method. The results of the EKS demonstrate better performance than WD over a wide range of PCG SNRs. The new PCG dynamical model can also be employed to develop other model-based processing frameworks such as heart sound segmentation and compression.     

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

A new model which is capable of generating realistic synthetic phonocardiogram (PCG) signals is introduced based on three coupled ordinary differential equations. The new PCG model takes into account the respiratory frequency, the heart rate variability and the time splitting of first and second heart sounds. This time splitting occurs with each cardiac cycle and varies with inhalation and exhalation. Clinical PCG statistics and the close temporal relationship between events in ECG and PCG are used to deduce values of PCG model parameters.In comparison with published PCG models, the proposed model allows a larger number of known PCG features to be taken into consideration. Moreover it is able to generate both normal and abnormal realistic synthetic heart sounds. Results show that these synthetic PCG signals have the closest features to those of a conventional heart sound in both time and frequency domains. Additionally, a sound quality test carried out by eight cardiologists demonstrates that the proposed model outperforms the existing models.This new PCG model is promising and useful in assessing signal processing techniques which are developed to help clinical diagnosis based on PCG.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.     

A robust heart sounds segmentation module based on S-transform

This paper presents a new module for heart sounds segmentation based on S-transform. The heart sounds segmentation process segments the PhonoCardioGram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2. An original localization method of heart sounds are proposed in this study. The method named SSE calculates the Shannon energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2. The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. To distinguish between S1 and S2, a feature extraction method based on the singular value decomposition (SVD) of the S-matrix is applied in this study. The proposed segmentation module is evaluated at each block according to a database of 80 sounds, including 40 sounds with cardiac pathologies.     

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.     

Novel PCG Analysis Method for Discriminating Between Abnormal and Normal Heart Sounds

A novel approach for separation among normal and heart murmurs sounds based on Phonocardiogram (PCG) analysis is introduced in this paper. The purpose of this work is to find the appropriate algorithm able to detect heart failures. Different features have been extracted from time and frequency domains. After the normalization step, the Principal Component Analysis algorithm is used for data reduction and compression. Support Vectors Machine (SVM), and k-Nearest Neighbors (kNN) classifiers were used with different kernels and number of neighbors in the classification step. Simulation results obtained from different databases are compared. The developed system gave good results when applied to different datasets. The accuracy of 96%, and 100% for the first, and the second dataset respectively were obtained. The algorithm shows its effectiveness in separation between normal and pathological cases.     

Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

The automatic segmentation of cardiac sound signals into heart beat cycles is generally required for the diagnosis of heart valve disorders. In this paper, a new method for segmentation of the cardiac sound signals using tunable-Q wavelet transform (TQWT) has been presented. The murmurs from cardiac sound signals are removed by suitably constraining TQWT based decomposition and reconstruction. The Q-factor, redundancy parameter and number of stages of decomposition of the TQWT are adapted to the desired statistical properties of the murmur-free reconstructed cardiac sound signals. The envelope based on cardiac sound characteristic waveform (CSCW) is extracted after the removal of low energy components from the reconstructed cardiac sound signals. Then the heart beat cycles are derived from the original cardiac sound signals by mapping the required timing information of CSCW which is obtained using established methods. The experimental results are included in order to show the effectiveness of the proposed method for segmentation of cardiac sound signals in comparison with other existing methods for various clinical cases.     

Paroxysmal atrial fibrillation recognition based on multi-scale wavelet <Emphasis Type="Italic">α</Emphasis>-entropy

Background

This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction.

Results

The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively.

Conclusions

Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.

     

Transponder-Aided Joint Calibration and Synchronization Compensation for Distributed Radar Systems

High-precision radiometric calibration and synchronization compensation must be provided for distributed radar system due to separate transmitters and receivers. This paper proposes a transponder-aided joint radiometric calibration, motion compensation and synchronization for distributed radar remote sensing. As the transponder signal can be separated from the normal radar returns, it is used to calibrate the distributed radar for radiometry. Meanwhile, the distributed radar motion compensation and synchronization compensation algorithms are presented by utilizing the transponder signals. This method requires no hardware modifications to both the normal radar transmitter and receiver and no change to the operating pulse repetition frequency (PRF). The distributed radar radiometric calibration and synchronization compensation require only one transponder, but the motion compensation requires six transponders because there are six independent variables in the distributed radar geometry. Furthermore, a maximum likelihood method is used to estimate the transponder signal parameters. The proposed methods are verified by simulation results.     

Chaotic based reconstructed phase space features for detecting ventricular fibrillation

Among the variety of cardiac arrhythmias, ventricular fibrillation (VF) and ventricular tachycardia (VT) are life-threatening; thus, accurate classification of these arrhythmias is a crucial task for cardiologists. Nevertheless, VT and VF signals are very similar in the time domain and accurate distinguishing these signals with naked eyes in some cases is impossible. In this paper, a novel self-similarity image-based scheme is introduced to classify the underlying information of VT, VF and normal electrocardiogram (ECG) signals. In this study, VT, VF and normal ECG signals are selected from CCU of the Royal Infirmary of Edinburgh and MIT-BIH datasets. According to the time delay method, signal samples can be assigned to state variables and a trajectory can be achieved. To extract the proposed self-similarity feature, first, two different trajectories from each signal trial are drawn according to two different delay time values. The two-dimensional state space of each trial trajectory is considered as an image. Therefore, two trajectory images are produced for each signal. Number of visited pixels in the first image is determined and is subtracted from that of the second image as the self-similarity feature of that signal. Moreover, another scheme is proposed to have a better estimation of self-similarity in which the logical AND operator is applied to both images (matrices) of each ECG trial. The third proposed criterion is similar to box counting method by this difference that each pixel is assigned a weight according to the trajectory density at that point and finally visited weighted pixels are counted. To classify VF from VT and normal ECG, a threshold is determined through the cross validation phase under the Receiver Operating Characteristic (ROC) criterion. To assess the proposed methods, the mentioned signals are classified using the-state-of-art chaotic features such as correlation dimension, the largest Lyapunov exponent and Approximate Entropy (ApEn). Experimental results indicate superiority of the proposed method in classifying the VT, VF and normal ECG signals compared to present traditional schemes. In addition, computational complexity of the introduced methods is very low and can be implemented in real-time applications.     

Automatic analysis of composite physical signals using non-negative factorization and information criterion

In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negative signal components, which are expressed by model functions. For analyzing the physical characteristics of a measured signal sequence, the parameters of the model functions are estimated. Furthermore, in order to quantitatively analyze real measurement data and to reduce the risk of improper decisions, it is necessary to obtain the statistical characteristics from several sequences rather than just a single sequence. In the present paper, we propose an automatic method by which to analyze composite signals using non-negative factorization and an information criterion. The proposed method decomposes the composite signal sequences using non-negative factorization subjected to parametric base functions. The number of components (i.e., rank) is also estimated using Akaike's information criterion. Experiments using simulated and real data reveal that the proposed method automatically estimates the acceptable ranks and parameters.     

On the assessment and evaluation of voice hoarseness

This article presents a non-invasive speech processing method for the assessment and evaluation of voice hoarseness. A technique based on time-scale analysis of the voice signal is used to decompose the signal into a suitable number of high-frequency details and extract the high-frequency bands of the signal. A discriminating measure, which measures the roll-off in power in the high-frequency bands of the signal, with respect to the decomposition index, is developed. The measure reflects the presence and degree of severity of hoarseness in the analyzed voice signals. The discriminating measure is supported by frequency-domain and time-series analyses of the high-frequency bands of normal and hoarse voice signals to provide a visual aid to the clinician or therapist. A database of sustained long vowels of normal and hoarse voices is created and used to assess the presence and degree of severity of hoarseness. The results obtained by the proposed method are compared to results obtained by perturbation analysis.     

Multiplicative multi-fractal modeling of electromyography signals for discerning neuropathic conditions

In this paper, we present a new method for multi-scale analysis of electromyography signals based on an interesting fractal process known as multiplicative cascade multi-fractal. Using simulated needle electromyography signals, we show this method provides a means for discrimination of normal and neuropathic electromyography signals. We also present experimental results that show the new parameters, computed using multiplicative cascade multi-fractal modeling, are more robust than the conventional signal parameter, number of turns, in the presence of additive noise. Results of multiplicative cascade multi-fractal modeling are consistent with other multi-scale approaches; advantages and differences are high lighted.     

A Flattest Constrained Envelope Approach for Empirical Mode Decomposition

Empirical mode decomposition (EMD) is an adaptive method for nonlinear, non-stationary signal analysis. However, the upper and lower envelopes fitted by cubic spline interpolation (CSI) may often occur overshoots. In this paper, a new envelope fitting method based on the flattest constrained interpolation is proposed. The proposed method effectively integrates the difference between extremes into the cost function, and applies a chaos particle swarm optimization method to optimize the derivatives of the interpolation nodes. The proposed method was tested on three different types of data: ascertain signal, random signals and real electrocardiogram signals. The experimental results show that: (1) The proposed flattest envelope effectively solves the overshoots caused by CSI method and the artificial bends caused by piecewise parabola interpolation (PPI) method. (2) The index of orthogonality of the intrinsic mode functions (IMFs) based on the proposed method is 0.04054, 0.02222±0.01468 and 0.04013±0.03953 for the ascertain signal, random signals and electrocardiogram signals, respectively, which is lower than the CSI method and the PPI method, and means the IMFs are more orthogonal. (3) The index of energy conversation of the IMFs based on the proposed method is 0.96193, 0.93501±0.03290 and 0.93041±0.00429 for the ascertain signal, random signals and electrocardiogram signals, respectively, which is closer to 1 than the other two methods and indicates the total energy deviation amongst the components is smaller. (4) The comparisons of the Hilbert spectrums show that the proposed method overcomes the mode mixing problems very well, and make the instantaneous frequency more physically meaningful.     

心脏杂音的自适应时频谱分析

应用自适应时频分析方法对心脏杂音（收缩期杂音、舒张期杂音、连续性杂音）进行分类研究,旨在克服固定核时频分析分辨率较低的缺陷。对多例心脏病人不同类型的心脏杂音分析结果表明：基于自适应锥形核分布的时频谱反映了心脏杂音在时间－频率平面的能量分布及动态变化过程,并有较高的时频分辨率,不同类型心脏杂音的时频谱具有明显的时频特征。     

Activity pattern detection in electroneurographic and electromyogram signals through a heteroscedastic change-point method

In this work, we propose a heteroscedastic method in the detection of activity patterns of electroneurographic and electromyogram signals involved in rhythmic activities of nerves and muscles, respectively. The electric behavior observed in such signals is characterized by phases of activity and silence. The beginning and the length of electrically active and electrically silent phases in a signal allow us to quantitatively analyze the changes and the effects on a rhythmic activity produced by experimental changes. In order to distinguish between these two phases, signals are assumed to be a sample of a time-dependent, normally distributed random variable with non-constant variance, and that the determination of the variance at each point allows us to determine in which phase is the signal. The parameters of the model are determined by means of an iterative process which maximizes the log-likelihood under the proposed model. Moreover, we apply our method to the determination of the activity phases and silence phases in sequences of experimental and synthetic electroneurographic and electromyogram signals. The results obtained with synthetic data show that the method performs well in the determination of these activity patterns. Finally, the study of particular signals simulated under a generalized autoregressive conditional heteroscedasticity model suggests the robustness of the method with respect to the assumption of independence.     

Analysis of ECG signal denoising method based on S-transform

Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. However in real situation, noise is often embedded with ECG signal during acquisition. In this paper, a novel ECG signal denoising technique is proposed using Stockwell transform (S-transform). This method is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises to visually inspected clean ECG recordings. The experimental results demonstrate that the proposed method shows the better signal to noise ratio (SNR), lower root mean square error (RMSE) and percent root mean square difference (PRD) compared to generally used ECG denoising method like wavelet transform.     

Determination of human arterial wall parameters from clinical data

This study suggests a method to compute the material parameters for arteries in vivo from clinically registered pressure-radius signals. The artery is modelled as a hyperelastic, incompressible, thin-walled cylinder and the membrane stresses are computed using a strain energy. The material parameters are determined in a minimisation process by tuning the membrane stress to the stress obtained by enforcing global equilibrium. In addition to the mechanical model, the study also suggests a preconditioning of the pressure-radius signal. The preconditioning computes an average pressure-radius cycle from all consecutive cycles in the registration and removes, or reduces, undesirable disturbances. The effect is a robust parameter identification that gives a unique solution. The proposed method is tested on clinical data from three human abdominal aortas and the results show that the material parameters from the proposed method do not differ significantly (p < 0.01) from the corresponding parameters obtained by averaging the result from consecutive cycles.     

Optimized glucuronide hydrolysis for the detection of psilocin in human urine samples

In order to develop a sensitive and reliable analytical method for psilocin (PC) in urine samples, the hydrolysis conditions including the acid, alkaline and enzymatic hydrolyses have been investigated by monitoring not only PC but also psilocin glucuronide (PCG) by liquid chromatography tandem mass spectrometry (LC-MS-MS); PCG was initially identified in a "magic mushroom (MM)" user's urine by liquid chromatography mass spectrometry (LC-MS) and LC-MS-MS. The proposed conditions optimized for the hydrolysis are as follows: hydrolysis, enzymatic hydrolysis; enzyme, Escherichia coli beta-glucuronidase (5000 units/ml urine); incubation, pH 6 at 37 degrees C for 2h. The complete hydrolysis of PCG in urine was obtained under these conditions, while the enzymatic hydrolyses with three types of beta-glucuronidases originated from bovine liver (Type B-1), Helix pomatia (Type H-1) and Ampullaria provided uncompleted hydrolysis of PCG. Also, neither the acid nor alkaline hydrolysis was found to be applicable. According to the present method, 3.55 microg/ml of psilocin was detected in the "magic mushroom" user's urine after the enzymatic hydrolysis, though psilocin was not detected without hydrolysis.