We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.

No abstract

Blind source separation assumes that the acquired signal is composed of a weighted sum of a number of basic components corresponding to a number of limited sources. This work poses the problem of ECG signal diagnosis in the form of a blind source separation problem. In particular, a large number of ECG signals undergo two of the most commonly used blind source separation techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), so that the basic components underlying this complex signal can be identified. Given that such techniques are sensitive to signal shift, a simple transformation is used that computes the magnitude of the Fourier transformation of ECG signals. This allows the phase components corresponding to such shifts to be removed. Using the magnitude of the projection of a given ECG signal onto these basic components as features, it was shown that accurate arrhythmia detection and classification were possible. The proposed strategies were applied to a large number of independent 3 s intervals of ECG signals consisting of 320 training samples and 160 test samples from the MIT-BIH database. The samples equally represent five different ECG signal types, including normal, ventricular couplet, ventricular tachycardia, ventricular bigeminy and ventricular fibrillation. The intervals analysed were windowed using either a rectangular or a Hamming window. The methods demonstrated a detection rate of sensitivity 98% at specificity of 100% using nearest neighbour classification of features from ICA and a rectangular window. Lower classification rates were obtained using the same classifier with features from either PCA or ICA and a rectangular window. The results demonstrate the potential of the new method for clinical use.

HuT-102 cells are considered one of the most representable human T-lymphotropic virus 1 (HTLV-1)-infected cell lines for studying adult T-cell lymphoma (ATL). In our previous studies, genome-wide screening was performed using the GeneChip system with Human Genome Array U133 Plus 2.0 for transforming growth factor-β-activated kinase 1 (TAK1)-, interferon regulatory factor 3 (IRF3)- and IRF4-regulated genes to demonstrate the effects of interferon-inducible genes in HuT-102 cells. Our previous findings demonstrated that TAK1 induced interferon inducible genes via an IRF3-dependent pathway and that IRF4 has a counteracting effect. As our previous data was performed by manual selection of common interferon-related genes mentioned in the literature, there has been some obscure genes that have not been considered. In an attempt to maximize the outcome of those microarrays, the present study reanalyzed the data collected in previous studies through a set of computational rules implemented using ‘R’ software, to identify important candidate genes that have been missed in the previous two studies. The final list obtained consisted of ten genes that are highly recommend as potential candidate for therapies targeting the HTLV-1 infected cancer cells. Those genes are ATM, CFTR, MUC4, PARP14, QK1, UBR2, CLEC7A (Dectin-1), L3MBTL, SEC24D and TMEM140. Notably, PARP14 has gained increased attention as a promising target in cancer cells.

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.