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Unilateral Vocal Fold Paralysis (UVFP) is a type of neurogenic laryngeal disorder in which vocal folds of patients do not have their normal behaviors, leading to abnormal talking voices. In this paper, a new noninvasive method for processing telephony speech signals is proposed to remotely diagnose the voice of the patients with UVFP disease. The proposed feature extraction method benefits from an adaptive decomposition method, the Matching Pursuit (MP) algorithm, to decompose the involved signals to some predefined atoms. Then, the attributes of the obtained atoms assigned to the speech signal converts to a final feature vector, so called MSDMP. Simulation results indicate the usefulness of the proposed feature vector with respect to a commonly used wavelet based features (EWPD). The MSDMP feature vector has improved the classification rate by 4.98%, as compared to the EWPD feature vector. [ABSTRACT FROM AUTHOR]

Ankle sprains are one of the most common injuries during sport activities. Gait impairment is a significant problem in ankle sprained cases, leading to decreased activity and limitations in function. The early assessment of sprained ankle is concomitant with early treatment that can limit the necessary time of rest for the patient. Limiting the rest time is very important specially, for an injured athlete who wants to decrease the time lost in practice. In this study the design and construction of a simple and low cost embedded pressure sensor system with application in signal processing works is presented. This paper is focused on the ability of the constructed system in addressing the hypothesis that gait characteristics differ in normal people and in patients with sprained ankle. [ABSTRACT FROM AUTHOR]

Background and Objective: Background subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to structures with different intensities and independent motion patterns, making XCA background subtraction challenging., Methods: The current work proposes an online tree-structure-constrained robust PCA (OTS-RPCA) method to subtract the XCA background. A morphological closing operation is used as a pre-processing step to remove large-scale structures like the spine, chest and diaphragm. In the following, the XCA sequence is decomposed into three different subspaces: low-rank background, residual dynamic background and vascular foreground. A tree-structured norm is introduced and applied to the vascular submatrix to guarantee the vessel spatial coherency. Moreover, the residual dynamic background is separately extracted to remove noise and motion artifacts from the vascular foreground. The proposed algorithm also employs an adaptive regularization coefficient that tracks the vessel area changes in the XCA frames., Results: The proposed method is evaluated on two datasets of real clinical and synthetic low-contrast XCA sequences of 38 patients using the global and local contrast-to-noise ratio (CNR) and structural similarity index (SSIM) criteria. For the real XCA dataset, the average values of global CNR, local CNR and SSIM are 6.27, 3.07 and 0.97, while these values over the synthetic low-contrast dataset are obtained as 5.15, 2.69 and 0.94, respectively. The implemented quantitative and qualitative experiments verify the superiority of the proposed method over seven selected state-of-the-art methods in increasing the coronary vessel contrast and preserving the vessel structure., Conclusions: The proposed OTS-RPCA background subtraction method accurately subtracts backgrounds from XCA images. Our method might provide the basis for reducing the contrast agent dose and the number of needed injections in coronary interventions., Competing Interests: Declaration of competing interest I am submitting the paper titled Online Tree-Structure-Constrained RPCA for Background Subtraction of X-ray Coronary Angiography Images to Computer Methods and Programs in Biomedicine. I and another author (Mr. Saeid Shakeri) declare that we have no financial or personal relationships that could be perceived as a conflict of interest with respect to the content of this paper. Furthermore, we declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective. X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections. Approach. The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images. Main results. To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively. Significance. The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Maryam Faal and Farshad Almasganj.)

Background and Objectives: Heart disease is one of the leading causes of death around the world. Phonocardiogram (PCG) is an important bio-signal which represents the acoustic activity of heart, typically without any spatiotemporal information of the involved acoustic sources. The aim of this study is to analyze the PCG by employing a microphone array by which the heart internal sound sources could be localized, too., Method: In this paper, it is intended to propose a modality by which the locations of the active sources in the heart could also be investigated, during a cardiac cycle. In this way, a microphone array with six microphones is employed as the recording set up to be put on the human chest. In the following, the Group Delay MUSIC algorithm which is a sub-space based localization method is used to estimate the location of the heart sources in different phases of the PCG., Results: We achieved to 0.14cm mean error for the sources of first heart sound (S 1 ) simulator and 0.21cm mean error for the sources of second heart sound (S 2 ) simulator with Group Delay MUSIC algorithm. The acoustical diagrams created for human subjects show distinct patterns in various phases of the cardiac cycles such as the first and second heart sounds. Moreover, the evaluated source locations for the heart valves are matched with the ones that are obtained via the 4-dimensional (4D) echocardiography applied, to a real human case., Conclusions: Imaging of heart acoustic map presents a new outlook to indicate the acoustic properties of cardiovascular system and disorders of valves and thereby, in the future, could be used as a new diagnostic tool., (Copyright © 2017. Published by Elsevier B.V.)

Objectives: Development of a noninvasive method for separating different vocal fold diseases is an important issue concerning vocal analysis. Due to the time variations along a pathologic vocal signal, application of dynamic pattern modeling tools is expected to help in the detection of defects that occur in the speech production mechanism., Materials and Methods: In the present study, the hidden Markov model, which is a state space model, is employed to sort some of the vocal diseases. Moreover, this research mainly investigates the effects of the processed vocal signal lengths on the mentioned sorting task. To this end, the signal lengths of 1, 3, and 5 seconds of different disorders are used., Results: The experimental results show that some pathologic conditions in vocal folds such as cyst, false vocal cord, and mass are more evident in continued voice production, and the recognition accuracies gained via dynamic modeling of pathologic voice signals with more lengths are considerably improved., (Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Early detection of squamous dysplasia and esophageal squamous cell carcinoma is of great importance. Adopting computer aided algorithms in predicting cancer risk using its risk factors can serve in limiting the clinical screenings to people with higher risks. In the present study, we show that the application of an advanced classification method, the Minimum Classification Error, could considerably enhance the classification performance in comparison to the logistic regression model and the variable structure fuzzy neural network, as the latest successful methods. The results yield the accuracy of 89.65% for esophageal squamous cell carcinoma, and 88.42% for squamous dysplasia risk prediction., (© 2013 Published by Elsevier Ltd.)

Early diagnosis of voice disorders and abnormalities by means of digital speech processing is a subject of interest for many researchers. Various methods are introduced in the literature, some of which are able to extensively discriminate pathological voices from normal ones. Voice disorders sorting, on the other hand, has received less attention due to the complexity of the problem. Although, previous publications show satisfactory results in classifying one type of disordered voice from normal cases, or two different types of abnormalities from each other, no comprehensive approach for automatic sorting of vocal abnormalities has been offered yet. In this paper, a solution for this problem is suggested. We create a powerful wavelet feature extraction approach, in which, instead of standard wavelets, adaptive wavelets are generated and applied to the voice signals. Orthogonal wavelets are parameterized via lattice structure and then, the optimal parameters are investigated through an iterative process, using the genetic algorithm (GA). GA is guided by the classifier results. Based on the generated wavelet, a wavelet-filterbank is constructed and the voice signals are decomposed to compute eight energy-based features. A support vector machine (SVM) then classifies the signals using the extracted features. Experimental results show that six various types of vocal disorders: paralysis, nodules, polyps, edema, spasmodic dysphonia and keratosis are fully sorted via the proposed method. This could be a successful step toward sorting a larger number of abnormalities associated with the vocal system., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The presence of abnormalities in the vocal system affects the quality of the voice and changes its characteristics. Digital analysis of pathological voices can be an effective and non-invasive tool for the detection of such alterations. This paper proposes a wavelet-based method to distinguish between normal and disordered voices. Wavelet filter banks are used in conjunction with support vector machines, as feature extractors and classifiers, respectively. Orthogonal filter banks are implemented using a highly efficient structure known as "lattice" that parameterizes filter banks and produces a few parameters. The overall problem is to find these parameters such that perfect classification is achieved. To search for such parameters, a genetic algorithm with a fitness function corresponding to the classification result is applied. Simulation is done on the KAY database (a comprehensive database including 710 normal and pathological voice signals, developed by the Massachusetts Eye and Ear Infirmary Voice and Speech Lab), and one additional test set. It is observed that a genetic algorithm is able to find the filter bank parameters such that a 100% correct classification rate is achieved in classifying normal and pathological voices when the test is performed on both databases., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

This paper introduces a combinational feature extraction approach to improve speech recognition systems. The main idea is to simultaneously benefit from some features obtained from Poincaré section applied to speech reconstructed phase space (RPS) and typical Mel frequency cepstral coefficients (MFCCs) which have a proved role in speech recognition field. With an appropriate dimension, the reconstructed phase space of speech signal is assured to be topologically equivalent to the dynamics of the speech production system, and could therefore include information that may be absent in linear analysis approaches. Moreover, complicated systems such as speech production system can present cyclic and oscillatory patterns and Poincaré sections could be used as an effective tool in analysis of such trajectories. In this research, a statistical modeling approach based on Gaussian mixture models (GMMs) is applied to Poincaré sections of speech RPS. A final pruned feature set is obtained by applying an efficient feature selection approach to the combination of the parameters of the GMM model and MFCC-based features. A hidden Markov model-based speech recognition system and TIMIT speech database are used to evaluate the performance of the proposed feature set by conducting isolated and continuous speech recognition experiments. By the proposed feature set, 5.7% absolute isolated phoneme recognition improvement is obtained against only MFCC-based features.

Acoustic analysis of voice features can complete the invasive observation-based methods for the diagnosis of vocal fold pathologies. Selection of an appropriate feature extraction method from the voice can significantly improve the diagnostic results for patients with vocal disorders. In this paper, the performance of nonlinear dynamics and acoustical perturbation features is evaluated in order to distinguish patients with vocal fold disorder and other normal cases. As a matter of fact, vocal fold pathology is one of the major causes of voice quality reduction or feature variation in patients with dysphonic voices. Due to the devastating impact of vocal folds dysfunction on the complex dynamical structure of the speech signals, spectral analysis methods are not suitable for characterizing such changes in disordered voices. Therefore, the using measures that can reflect the nonlinear nature of such changes in the acoustical signals is an efficient alternative for the conventional methods. In order to compare and contrast the effectiveness of such approaches, we exploit features such as correlation dimension, the largest Lyapunov exponent, approximate entropy, fractal dimension and Ziv-Lempel complexity, and we also evaluate their performance with respect to some conventional features like jitter and shimmer, in the voice diagnosis task. Using the support vector machine classifier, our simulation results show that correlation dimension and the largest Lyapunov exponent features with the highest recognition rates of 94.44% and 88.89% can be used as a highly reliable method for the clinical diagnosis of vocal folds pathologies and other relevant applications., (2009 Elsevier Ltd. All rights reserved.)

Unilateral vocal fold paralysis (UVFP) is one of the most severe types of neurogenic laryngeal disorder in which the patients, due to their vocal cords malfunction, are confronted by some serious problems. As the effect of such pathologies would be significantly evident in the reduced quality and feature variation of dysphonic voices, this study is designed to scrutinize the piecewise variation of some specific types of these features, known as energy and entropy, all over the frequency range of pathological speech signals. In order to do so, the wavelet-packet coefficients, in five consecutive levels of decomposition, are used to extract the energy and entropy measures at different spectral sub-bands. As the decomposition procedure leads to a set of high-dimensional feature vectors, genetic algorithm is invoked to search for a group of optimal sub-band indexes for which the extracted features result in the highest recognition rate for pathological and normal subjects' classification. The results of our simulations, using support vector machine classifier, show that the highest recognition rate, for both optimized energy and entropy measures, is achieved at the fifth level of wavelet-packet decomposition. It is also found that entropy feature, with the highest recognition rate of 100% vs. 93.62% for energy, is more prominent in discriminating patients with UVFP from normal subjects. Therefore, entropy feature, in comparison with energy, demonstrates a more efficient description of such pathological voices and provides us a valuable tool for clinical diagnosis of unilateral laryngeal paralysis.