ABSTRACT
EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals. Besides, many studies have involved the time and frequency domain features to classify EEG signals. On the other hand, a very limited number of studies combine the spatial and temporal dimensions of the EEG signal. Brain dynamics are very complex across different mental tasks, thus it is difficult to design efficient algorithms with features based on prior knowledge. Therefore, in this study, we utilized the 2D AlexNet Convolutional Neural Network (CNN) to learn EEG features across different mental tasks without prior knowledge. First, this study adds spatial and temporal dimensions of EEG signals to a 2D EEG topographic map. Second, topographic maps at different time indices were cascaded to populate a 2D image for a given time window. Finally, the topographic maps enabled the AlexNet to learn features from the spatial and temporal dimensions of the brain signals. The classification performance was obtained by the proposed method on a multiclass dataset from BCI Competition IV dataset 2a. The proposed system obtained an average classification accuracy of 81.09%, outperforming the previous state-of-the-art methods by a margin of 4% for the same dataset. The results showed that converting the EEG classification problem from a (1D) time series to a (2D) image classification problem improves the classification accuracy for BCI systems. Also, our EEG topographic maps enabled CNN to learn subtle features from spatial and temporal dimensions, which better represent mental tasks than individual time or frequency domain features.
Subject(s)
Brain-Computer Interfaces , Algorithms , Electroencephalography , Machine Learning , Neural Networks, ComputerABSTRACT
Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.
Subject(s)
Alzheimer Disease/diagnosis , Cognitive Dysfunction/diagnosis , Diffusion Tensor Imaging/methods , Magnetic Resonance Imaging/methods , Aged , Algorithms , Alzheimer Disease/diagnostic imaging , Alzheimer Disease/pathology , Cognitive Dysfunction/diagnostic imaging , Cognitive Dysfunction/pathology , Deep Learning , Disease Progression , Female , Gray Matter/diagnostic imaging , Gray Matter/physiology , Hippocampus/diagnostic imaging , Hippocampus/pathology , Humans , Image Interpretation, Computer-Assisted/methods , Machine Learning , Male , Neural Networks, Computer , Neuroimaging/methods , Support Vector MachineABSTRACT
The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.
ABSTRACT
Elucidating protein subcellular localization is an essential topic in proteomics research due to its importance in the process of drug discovery. Unfortunately, experimentally uncovering protein subcellular targets is an arduous process that may not result in a successful localization. In contrast, computational methods can rapidly predict protein subcellular targets and are an efficient alternative to experimental methods for unannotated proteins. In this work, we introduce a new method to predict protein subcellular localization which increases the predictive power of generative probabilistic models while preserving their explanatory benefit. Our method exploits Markov models to produce a feature vector that records micro-similarities between the underlying probability distributions of a given sequence and their counterparts in reference models. Compared to ordinary Markov chain inference, we show that our method improves overall accuracy by 10% under 10-fold cross-validation on a dataset consisting of 10 subcellular locations. The source code is publicly available on https://github.com/aametwally/MC MicroSimilarities.
Subject(s)
Markov Chains , Computational Biology , Databases, Protein , Protein Transport , Proteins , Sequence Analysis, Protein , Software , Subcellular FractionsABSTRACT
Haplotype-based methods compete with "one-SNP-at-a-time" approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.
Subject(s)
Chromosomes, Human, Pair 6 , Haplotypes , Arthritis, Rheumatoid/genetics , Case-Control Studies , Female , Genetic Association Studies , Genetic Predisposition to Disease , Genotype , Humans , Linkage Disequilibrium , Male , Polymorphism, Single NucleotideABSTRACT
Genetics of autoimmune diseases represent a growing domain with surpassing biomarker results with rapid progress. The exact cause of Rheumatoid Arthritis (RA) is unknown, but it is thought to have both a genetic and an environmental bases. Genetic biomarkers are capable of changing the supervision of RA by allowing not only the detection of susceptible individuals, but also early diagnosis, evaluation of disease severity, selection of therapy, and monitoring of response to therapy. This review is concerned with not only the genetic biomarkers of RA but also the methods of identifying them. Many of the identified genetic biomarkers of RA were identified in populations of European and Asian ancestries. The study of additional human populations may yield novel results. Most of the researchers in the field of identifying RA biomarkers use single nucleotide polymorphism (SNP) approaches to express the significance of their results. Although, haplotype block methods are expected to play a complementary role in the future of that field.
ABSTRACT
The growing importance of three-dimensional radiotherapy treatment has been associated with the active presence of advanced computational workflows that can simulate conventional x-ray films from computed tomography (CT) volumetric data to create digitally reconstructed radiographs (DRR). These simulated x-ray images are used to continuously verify the patient alignment in image-guided therapies with 2D-3D image registration. The present DRR rendering pipelines are quite limited to handle huge imaging stacks generated by recent state-of-the-art CT imaging modalities. We present a high performance x-ray rendering pipeline that is capable of generating high quality DRRs from large scale CT volumes. The pipeline is designed to harness the immense computing power of all the heterogeneous computing platforms that are connected to the system relying on OpenCL. Load-balancing optimization is also addressed to equalize the rendering load across the entire system. The performance benchmarks demonstrate the capability of our pipeline to generate high quality DRRs from relatively large CT volumes at interactive frame rates using cost-effective multi-GPU workstations. A 5122 DRR frame can be rendered from 1024 × 2048 × 2048 CT volumes at 85 frames per second.
Subject(s)
Cone-Beam Computed Tomography , Imaging, Three-Dimensional , Radiographic Image Enhancement , Tomography, X-Ray Computed , Algorithms , Computer Graphics , Computers , Humans , Image Processing, Computer-Assisted , Programming Languages , Software , X-RaysABSTRACT
Digitally reconstructed radiographs (DRRs) play a significant role in modern clinical radiation therapy. They are used to verify patient alignments during image guided therapies with 2D-3D image registration. The generation of DRRs can be implemented intuitively in O(N3) relying on direct volume rendering (DVR) methods, such as ray marching. This complexity imposes certain limitations on the rendering performance if high quality DRR images are needed. Those DRRs can be alternatively generated in the k-space using the central slice theorem in O(N2logN). Several rendering pipelines have been designed to create the DRRs in the k-space, but they were either limited to specific vendor or entail particular software requirements. We present a high performance implementation of a k-space-based DRR generation pipeline that is executable on various heterogeneous computing architectures using OpenCL. Our implementation generates a DRR for a 5123 CT volume in 6, 2.7 and 0.68 milli-seconds on a commodity CPU, mid-range and high-end GPUs respectively.
Subject(s)
Cone-Beam Computed Tomography , Imaging, Three-Dimensional , Radiographic Image Enhancement , Algorithms , Computer Graphics , Computer Systems , Fourier Analysis , Humans , Programming Languages , SoftwareABSTRACT
Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.
Subject(s)
Brain-Computer Interfaces , Image Processing, Computer-Assisted , Motor Activity , Adult , Algorithms , Electroencephalography , Fourier Analysis , Humans , Male , Signal Processing, Computer-Assisted , Wavelet AnalysisABSTRACT
Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socio-economic impact. The aim of the current study was to investigate eight candidate RA susceptibility loci to identify the associated variants in Egyptian population. Eight single nucleotide polymorphisms (SNPs) (MTHFR-C677T and A1298C, TGFß1 T869C, TNFB A252G, and VDR-ApaI, BsmI, FokI, and TaqI) were tested by genotyping patients with RA (n = 105) and unrelated controls (n = 80). Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium (LD) between the VDR SNPs was measured to detect any indirect association. By comparing RA patients with controls (TNFB, BsmI, and TaqI), SNPs were associated with RA using all models. MTHFR C677T was associated with RA using all models except the recessive model. TGFß1 and MTHFR A1298C were associated with RA using the dominant and the co-dominant models. The recessive model represented the association for ApaI variant. There were no significant differences for FokI and the presence of RA disease by the used models examination. For LD results, There was a high D' value between BsmI and FokI (D' = 0.91), but the r(2) value between them was poor. All the studied SNPs may contribute to the susceptibility of RA disease in Egyptian population except for FokI SNP.
Subject(s)
Arthritis, Rheumatoid/genetics , Genetic Predisposition to Disease , Lymphotoxin-alpha/genetics , Methylenetetrahydrofolate Reductase (NADPH2)/genetics , Polymorphism, Single Nucleotide , Receptors, Calcitriol/genetics , Transforming Growth Factor beta1/genetics , Adult , Aged , Alleles , Case-Control Studies , Female , Gene Frequency , Genetic Association Studies , Genotype , Haplotypes , Humans , Linkage Disequilibrium , Male , Middle AgedABSTRACT
Diseases of the immune and the skeletal systems should be studied together for the deep interaction between them. Many studies consider osteoporosis (OP) as a risk factor for the prediction of disease progression in rheumatoid arthritis (RA). The aim of this research is to study the effect of four single nucleotide polymorphisms (SNPs) on RA patients with and without OP. The examined SNPs (MTHFR (C677T, and A1298C), TGFß1 (T869C), and TNFB (A252G)) were tested by genotyping 17 RA patients with OP and 72 RA patients without OP. Associations were tested using four models (multiplicative, dominant, recessive, and co-dominant). The studied SNPs were not significantly associated with the risk of OP in RA. MTHFR, TGFß1, and TNFB polymorphisms don't appear to be clinically useful genetic markers for predicting RA severity in Egyptian women population.
Subject(s)
Arthritis, Rheumatoid/genetics , Lymphotoxin-alpha/genetics , Methylenetetrahydrofolate Reductase (NADPH2)/genetics , Osteoporosis/genetics , Transforming Growth Factor beta1/genetics , Adult , Case-Control Studies , Female , Gene Frequency , Genetic Association Studies , Genetic Predisposition to Disease , Humans , Middle Aged , Polymorphism, Single Nucleotide , Sequence Analysis, DNAABSTRACT
Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result of its ðª(N (2)logâ¡N) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are ðª(N (3)) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.
ABSTRACT
Several progresses have been introduced in the field of bone regenerative medicine. A new term tissue engineering (TE) was created. In TE, a highly porous artificial extracellular matrix or scaffold is required to accommodate cells and guide their growth in three dimensions. The design of scaffolds with desirable internal and external structure represents a challenge for TE. In this paper, we introduce a new method known as automated scaffold design (ASD) for designing a 3D scaffold with a minimum mismatches for its geometrical parameters. The method makes use of k-means clustering algorithm to separate the different tissues and hence decodes the defected bone portions. The segmented portions of different slices are registered to construct the 3D volume for the data. It also uses an isosurface rendering technique for 3D visualization of the scaffold and bones. It provides the ability to visualize the transplanted as well as the normal bone portions. The proposed system proves good performance in both the segmentation results and visualizations aspects.
Subject(s)
Algorithms , Imaging, Three-Dimensional , Tissue Engineering/methods , Tissue Scaffolds/chemistry , AutomationABSTRACT
Digitally Reconstructed Radiographs (DRRs) play a vital role in medical imaging procedures and radiotherapy applications. They allow the continuous monitoring of patient positioning during image guided therapies using multi-dimensional image registration. Conventional generation of DRRs using spatial domain algorithms such as ray casting is associated with computational complexity of O(N(3)). Fourier slice theorem is an alternative approach for generating the DRRs in the k-space with reduced time complexity. In this work, we present a high performance, scalable, and optimized DRR generation pipeline on the Graphics Processing Unit (GPU). The strong scaling performance of the presented pipeline is investigated and demonstrated using two contemporary GPUs. Our pipeline is capable of generating DRRs for 512(3) volumes in less than a milli-second.
Subject(s)
Computer Graphics , Image Processing, Computer-Assisted/methods , Radiographic Image Enhancement/methods , Algorithms , Fourier Analysis , Humans , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging , Patient Positioning , Software , Tomography, X-Ray Computed , X-RaysABSTRACT
This paper features an advanced implementation of the X-ray rendering algorithm that harnesses the giant computing power of the current commodity graphics processors to accelerate the generation of high resolution digitally reconstructed radiographs (DRRs). The presented pipeline exploits the latest features of NVIDIA Graphics Processing Unit (GPU) architectures, mainly bindless texture objects and dynamic parallelism. The rendering throughput is substantially improved by exploiting the interoperability mechanisms between CUDA and OpenGL. The benchmarks of our optimized rendering pipeline reflect its capability of generating DRRs with resolutions of 2048(2) and 4096(2) at interactive and semi interactive frame-rates using an NVIDIA GeForce 970 GTX device.
Subject(s)
Computer Graphics , Image Processing, Computer-Assisted/methods , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Computers , Head/diagnostic imaging , Humans , Models, Statistical , Programming Languages , Radiographic Image Enhancement/methods , Software , X-RaysABSTRACT
Performance benchmarking have become a very important component in all successful organizations nowadays that must be used by Clinical Engineering Department (CED) in hospitals. Many researchers identified essential mainstream performance indicators needed to improve the CED's performance. These studies revealed mainstream performance indicators that use the database of a CED to evaluate its performance. In this work, we believe that those indicators are insufficient for hospitals. Additional important indicators should be included to improve the evaluation accuracy. Therefore, we added new indicators: technical/maintenance indicators, economic indicators, intrinsic criticality indicators, basic hospital indicators, equipment acquisition, and safety indicators. Data is collected from 10 hospitals that cover different types of healthcare organizations. We developed a software tool that analyses collected data to provide a score for each CED under evaluation. Our results indicate that there is an average gap of 67% between the CEDs' performance and the ideal target. The reasons for the noncompliance are discussed in order to improve performance of CEDs under evaluation.
Subject(s)
Biomedical Engineering/standards , Task Performance and Analysis , Biomedical Engineering/economics , Documentation/standards , Hospitals , Maintenance/standardsABSTRACT
In dynamic healthcare environments, caregivers and patients are constantly moving. To increase the healthcare quality when it is necessary, caregivers need the ability to reach each other and securely access medical information and services from wherever they happened to be. This paper presents an Interactive Telemedicine Solution (ITS) to facilitate and automate the communication within a healthcare facility via Voice over Internet Protocol (VOIP), regular mobile phones, and Wi-Fi connectivity. Our system has the capability to exchange/provide securely healthcare information/services across geographic barriers through 3G/4G wireless communication network. Our system assumes the availability of an Electronic Health Record (EHR) system locally in the healthcare organization and/or on the cloud network such as a nation-wide EHR system. This paper demonstrate the potential of our system to provide effectively and securely remote healthcare solution.