A novel feature-level fusion scheme with multimodal attention CNN for heart sound classification.

BACKGROUND AND OBJECTIVE: Most of the existing machine learning-based heart sound classification methods achieve limited accuracy. Since they primarily depend on single domain feature information and tend to focus equally on each part of the signal rather than employing a selective attention mechanism. In addition, they fail to exploit convolutional neural network (CNN) - based features with an effective fusion strategy. METHODS: In order to overcome these limitations, a novel multimodal attention convolutional neural network (MACNN) with a feature-level fusion strategy, in which Mel-cepstral domain as well as general frequency domain features are incorporated to increase the diversity of the features, is proposed in this paper. In the proposed method, DilationAttenNet is first utilized to construct attention-based CNN feature extractors and then these feature extractors are jointly optimized in MACNN at the feature-level. The attention mechanism aims to suppress irrelevant information and focus on crucial diverse features extracted from the CNN. RESULTS: Extensive experiments are carried out to study the efficacy of the feature level fusion in comparison to that with early fusion. The results show that the proposed MACNN method significantly outperforms the state-of-the-art approaches in terms of accuracy and score for the two publicly available Github and Physionet datasets. CONCLUSION: The findings of our experiments demonstrated the high performance for heart sound classification based on the proposed MACNN, and hence have potential clinical usefulness in the identification of heart diseases. This technique can assist cardiologists and researchers in the design and development of heart sound classification methods.

Epilepsy-Related Injuries in Children: An Institution-Based Study.

Background: Epilepsy carries a lifetime risk of seizure-related accidents. The risk varies according to the socioeconomic profile of a place. Sufficient data is lacking for epilepsy-related accidents in the pediatric population. Objective: We aimed to identify the proportion of pediatric epileptic patients who met with accidents and their associated factors. Methods: A prospective study was done. Patients of less than 18 years with epilepsy of more than 1-year duration were included and were given a questionnaire modified for the pediatric population. The demography of accidents during seizures and drugs taken were recorded. Results: 135 epileptic children were included. 70.4% of patients suffered seizure-related accidents ranging from 1-10 accidents in their epilepsy duration. Falls (83.15%) were the most common, 25.26% of them required hospitalization. Accidents due to missing of drug dosage were observed in 23% patients. Conclusion: Seizure-related accidents are common in the pediatric population, and may lead to major accidents. Better epilepsy management with extra care for high-risk epilepsy patients may decrease their incidents.

Image Denoising Based on Fractional Gradient Vector Flow and Overlapping Group Sparsity as Priors.

In this paper, a new regularization term in the form of L1-norm based fractional gradient vector flow (LF-GGVF) is presented for the task of image denoising. A fractional order variational method is formulated, which is then utilized for estimating the proposed LF-GGVF. Overlapping group sparsity along with LF-GGVF is used as priors in image denoising optimization framework. The Riemann-Liouville derivative is used for approximating the fractional order derivatives present in the optimization framework. Its role in the framework helps in boosting the denoising performance. The numerical optimization is performed in an alternating manner using the well-known alternating direction method of multipliers (ADMM) and split Bregman techniques. The resulting system of linear equations is then solved using an efficient numerical scheme. A variety of simulated data that includes test images contaminated by additive white Gaussian noise are used for experimental validation. The results of numerical solutions obtained from experimental work demonstrate that the performance of the proposed approach in terms of noise suppression and edge preservation is better when compared with that of several other methods.

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge.

To better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice.

Endoscopic Anterior Cervical Discectomy (Disc Preserving).

BACKGROUND AND INTRODUCTION: Endoscopic anterior cervical approach has several advantages compared to conventional anterior cervical discectomy and fusion (ACDF). OBJECTIVE: This video demonstrates a step-by-step procedure for endoscopic anterior cervical discectomy. PROCEDURE: The patient is placed supine with the neck extended. A standard anterior cervical approach using about 3 cm skin incision is made and under "Easy Go" (Karl Storz, Tuttlingen, Germany) endoscopic vision, the uncinate process and uncus are drilled. Only a small portion of the normal disc, posterior longitudinal ligament (PLL), and compressing disc is removed. The closure is done in a standard manner. RESULTS: In 240 patients, the average postoperative reduction in disc height, operating time, and blood loss were 1.1 ± 0.2 mm, 110 ± 17 min, and 30 ± 11 mL, respectively. The average postoperative VAS score and Nurick grading improved significantly. There were no permanent complications or any mortality. CONCLUSION: Endoscopic technique is an effective and safe alternative to ACDF after attaining the learning curve.

Tchebichef and Adaptive Steerable Based Total Variation Model for Image Denoising.

Structural information, in particular, the edges present in an image are the most important part that get noticed by human eyes. Therefore, it is important to denoise this information effectively for better visualization. Recently, research work has been carried out to characterize the structural information into plain and edge patches and denoise them separately. However, the information about the geometrical orientation of the edges are not considered leading to sub-optimal denoising results. This has motivated us to introduce in this paper an adaptive steerable total variation regularizer (ASTV) based on geometric moments. The proposed ASTV regularizer is capable of denoising the edges based on their geometrical orientation, thus boosting the denoising performance. Further, earlier works exploited the sparsity of the natural images in DCT and wavelet domains which help in improving the denoising performance. Based on this observation, we introduce the sparsity of an image in orthogonal moment domain, in particular, the Tchebichef moment. Then, we propose a new sparse regularizer, which is a combination of the Tchebichef moment and ASTVbased regularizers. The overall denoising framework is optimized using split Bregman-based multivariable minimization technique. Experimental results demonstrate the competitiveness of the proposed method with the existing ones in terms of both the objective and subjective image qualities.

Image denoising via overlapping group sparsity using orthogonal moments as similarity measure.

Recently, sparse representation has attracted a great deal of interest in many of the image processing applications. However, the idea of self-similarity, which is inherently present in an image, has not been considered in standard sparse representation. Moreover, if the dictionary atoms are not constrained to be correlated, the redundancy present in the dictionary may not improve the performance of sparse coding. This paper addresses these issues by using orthogonal moments to extract the correlations among the atoms and group them together by extracting the characteristics of the noisy image patches. Most of the existing sparsity-based image denoising methods utilize an over-complete dictionary, for example, the K-SVD method that requires solving a minimization problem which is computationally challenging. In order to improve the computational efficiency and the correlation between the sparse coefficients, this paper employs the concept of overlapping group sparsity formulated for both convex and non-convex denoising frameworks. The optimization method used for solving the denoising framework is the well known majorization-minimization method, which has been applied successfully in sparse approximation and statistical estimations. Experimental results demonstrate that the proposed method offers, in general, a performance that is better than that of the existing state-of-the-art methods irrespective of the noise level and the image type.

Compression of Steganographed PPG Signal with Guaranteed Reconstruction Quality Based on Optimum Truncation of Singular Values and ASCII Character Encoding.

OBJECTIVE: Extraction and analysis of various clinically significant features of photoplethysmogram (PPG) signals for monitoring several physiological parameters as well as for biometric authentication have become important areas of research in recent years. However, PPG signal compression; particularly quality-guaranteed compression, and steganography of patient's secret information is still lagging behind. METHOD: This paper presents a robust, reliable and highly-efficient singular value decomposition (SVD) and lossless ASCII character encoding (LL-ACE)-based quality-guaranteed PPG compression algorithm. This algorithm can not only be used to compress PPG signals but also do so for steganographed PPG signals that include the patient information. RESULT AND CONCLUSION: It is worth mentioning that such an algorithm is being proposed for the first time to compress steganographed PPG signals. The algorithm is tested on PPG signals collected from four different databases, and its performance is assessed using both quantitative and qualitative measures. The proposed steganographed PPG compression algorithm provides a compression ratio that is much higher than that provided by other algorithms that are designed to compress the PPG signals only. SIGNIFICANCE: (1) the clinical quality of the reconstructed PPG signal can be controlled precisely, (2) the patient's personal information is restored with no errors, (3) high compression ratio, and (4) the PPG signal reconstruction error is neither dependent on the steganographic operation nor on the size of the patient information data.

SVD and ASCII Character Encoding-Based Compression of Multiple Biosignals for Remote Healthcare Systems.

Advancements in electronics and miniaturized device fabrication technologies have enabled simultaneous acquisition of multiple biosignals (MBioSigs), but the area of compression of MBioSigs remains unexplored to date. This paper presents a robust singular value decomposition (SVD) and American standard code for information interchange (ASCII) character encoding-based algorithm for compression of MBioSigs for the first time to the best of our knowledge. At the preprocessing stage, MBioSigs are denoised, down sampled and then transformed to a two-dimensional (2-D) data array. SVD of the 2-D array is carried out and the dimensionality of the singular values is reduced. The resulting matrix is then compressed by a lossless ASCII character encoding-based technique. The proposed compression algorithm can be used in a variety of modes such as lossless, with or without using the down sampling operation. The compressed file is then uploaded to a hypertext preprocessor (PHP)-based website for remote monitoring application. Evaluation results show that the proposed algorithm provides a good compression performance; in particular, the mean opinion score of the reconstructed signal falls under the category "very good" as per the gold standard subjective measure.

A new approach to optimal control of conductance-based spiking neurons.

This paper presents an algorithm for solving the minimum-energy optimal control problem of conductance-based spiking neurons. The basic procedure is (1) to construct a conductance-based spiking neuron oscillator as an affine nonlinear system, (2) to formulate the optimal control problem of the affine nonlinear system as a boundary value problem based on Pontryagin's maximum principle, and (3) to solve the boundary value problem using the homotopy perturbation method. The construction of the minimum-energy optimal control in the framework of the homotopy perturbation technique is novel and valid for a broad class of nonlinear conductance-based neuron models. The applicability of our method in the FitzHugh-Nagumo and Hindmarsh-Rose models is validated by simulations.

Spastic quadriparesis due to pathological fracture of odontoid secondary to carcinoma prostate: A rare presentation.

Prostate carcinoma presenting as symptomatic metastases to atlantoaxial spine is extremely rare. Spastic quadriparesis due to pathological fracture of odontoid as the only initial manifestation without symptoms of primary malignancy is rarer still. We report a 64-year-old male who presented with progressive spastic quadriparesis along with urinary retention of 3 weeks duration. Computed tomography and magnetic resonance imaging cervical spine and craniovertebral junction showed type III pathological fracture of odontoid with anterior translation of C1 with spinal cord compression. Biopsy from an enlarged prostate showed adenocarcinoma of prostate. The patient was managed conservatively from neurological aspect as he refused for any surgical intervention.

Learning the Conformal Transformation Kernel for Image Recognition.

In this paper, we present a multiclass data classifier, denoted by optimal conformal transformation kernel (OCTK), based on learning a specific kernel model, the CTK, and utilize it in two types of image recognition tasks, namely, face recognition and object categorization. We show that the learned CTK can lead to a desirable spatial geometry change in mapping data from the input space to the feature space, so that the local spatial geometry of the heterogeneous regions is magnified to favor a more refined distinguishing, while that of the homogeneous regions is compressed to neglect or suppress the intraclass variations. This nature of the learned CTK is of great benefit in image recognition, since in image recognition we always have to face a challenge that the images to be classified are with a large intraclass diversity and interclass similarity. Experiments on face recognition and object categorization show that the proposed OCTK classifier achieves the best or second best recognition result compared with that of the state-of-the-art classifiers, no matter what kind of feature or feature representation is used. In computational efficiency, the OCTK classifier can perform significantly faster than the linear support vector machine classifier (linear LIBSVM) can.

Prevalence of hypothalamo pituitary dysfunction in patients of traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is common in young soldiers of armed forces leading to significant morbidity and mortality. We studied the prevalence of hypopituitarism following TBI and its association with trauma severity. MATERIALS AND METHODS: We conducted a 12-month prospective study of 56 TBI patients for the presence of hormonal dysfunction. Hormonal parameters were estimated during the early phase (0-10 days posttraumatically) and after 6 and 12 months. Dynamic testing was done when required, and the results were analyzed by appropriate statistical methods. RESULTS: Hormonal dysfunction was seen in 39 of the 56 (70%) patients at initial assessment. Persisting pituitary deficiencies are seen in 7 and 8 patients at the end of 6 months and 12 months, respectively. Hypogonadotropic hypogonadism, hypothyroidism, and growth hormone deficiency are the most common diagnoses. Initial severe TBI and plurihormonal involvement predicted the long-term hypopituitarism. CONCLUSION: Early hypopituitarism was common in severe TBI, but recovers in majority. Evaluation for the occult pituitary dysfunction is required during the rehabilitation of TBI patients.

Underdetermined DOA Estimation Using MVDR-Weighted LASSO.

The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least absolute shrinkage and selection operator (OLS A-LASSO) is applied for the first time for DOA estimation. Furthermore, a new LASSO algorithm, the minimum variance distortionless response (MVDR) A-LASSO, which solves the DOA problem in the CS framework, is presented. The proposed algorithm does not depend on the singular value decomposition nor on the orthogonality of the signal and the noise subspaces. Hence, the DOA estimation can be done without a priori knowledge of the number of sources. The proposed algorithm can estimate up to ( ( M 2 - 2 ) / 2 + M - 1 ) / 2 sources using M sensors without any constraints or assumptions about the nature of the signal sources. Furthermore, the proposed algorithm exhibits performance that is superior compared to that of the classical DOA estimation methods, especially for low signal to noise ratios (SNR), spatially-closed sources and coherent scenarios.

MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions.

BACKGROUND: The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem. RESULTS: We propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log-loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log-loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65). CONCLUSIONS: We have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most-widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum-of-pairs and total column metrics.

Prediction of Indel flanking regions in protein sequences using a variable-order Markov model.

MOTIVATION: Insertion/deletion (indel) and amino acid substitution are two common events that lead to the evolution of and variations in protein sequences. Further, many of the human diseases and functional divergence between homologous proteins are more related to indel mutations, even though they occur less often than the substitution mutations do. A reliable identification of indels and their flanking regions is a major challenge in research related to protein evolution, structures and functions. RESULTS: In this article, we propose a novel scheme to predict indel flanking regions in a protein sequence for a given protein fold, based on a variable-order Markov model. The proposed indel flanking region (IndelFR) predictors are designed based on prediction by partial match (PPM) and probabilistic suffix tree (PST), which are referred to as the PPM IndelFR and PST IndelFR predictors, respectively. The overall performance evaluation results show that the proposed predictors are able to predict IndelFRs in the protein sequences with a high accuracy and F1 measure. In addition, the results show that if one is interested only in predicting IndelFRs in protein sequences, it would be preferable to use the proposed predictors instead of HMMER 3.0 in view of the substantially superior performance of the former.

A method for automatic segmentation of nuclei in phase-contrast images based on intensity, convexity and texture.

This paper presents a method for automatic segmentation of nuclei in phase-contrast images using the intensity, convexity and texture of the nuclei. The proposed method consists of three main stages: preprocessing, h-maxima transformation-based marker controlled watershed segmentation ( h-TMC), and texture analysis. In the preprocessing stage, a top-hat filter is used to increase the contrast and suppress the non-uniform illumination, shading, and other imaging artifacts in the input image. The nuclei segmentation stage consists of a distance transformation, h-maxima transformation and watershed segmentation. These transformations utilize the intensity information and the convexity property of the nucleus for the purpose of detecting a single marker in every nucleus; these markers are then used in the h-TMC watershed algorithm to obtain segments of the nuclei. However, dust particles, imaging artifacts, or prolonged cell cytoplasm may falsely be segmented as nuclei at this stage, and thus may lead to an inaccurate analysis of the cell image. In order to identify and remove these non-nuclei segments, in the third stage a texture analysis is performed, that uses six of the Haralick measures along with the AdaBoost algorithm. The novelty of the proposed method is that it introduces a systematic framework that utilizes intensity, convexity, and texture information to achieve a high accuracy for automatic segmentation of nuclei in the phase-contrast images. Extensive experiments are performed demonstrating the superior performance ( precision = 0.948; recall = 0.924; F1-measure = 0.936; validation based on  âˆ¼ 4850 manually-labeled nuclei) of the proposed method.