Author
M. Ramasubba Reddy
Other affiliations: Indian Institutes of Technology
Bio: M. Ramasubba Reddy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Imaging phantom & Speckle pattern. The author has an hindex of 11, co-authored 71 publications receiving 636 citations. Previous affiliations of M. Ramasubba Reddy include Indian Institutes of Technology.
Papers published on a yearly basis
Papers
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TL;DR: It is concluded that the regional and global left ventricle systolic dysfunction can be assessed by the ER measured at end‐diastole and end‐systole from 2D echocardiogram and may contribute to the high rate of cardiovascular disorders.
Abstract: Objectives We sought to quantify the left ventricle systolic dysfunction by a geometric index from two-dimensional (2D) echocardiography by implementing an automated fuzzy logic edge detection algorithm for the segmentation. Background The coronary injuries have repercussions on the left ventricle producing changes on wall contractility, the shape of the cavity, and as a whole changes on the ventricular function. Methods 2D echocardiogram and M-mode recordings were performed over the control group and those with the dysfunctions. From 2D recordings, individual frames were extracted for at least five cardiac cycles and then segmentation of left ventricle was done by automated fuzzy systems. In each frame, the volumes are measured and a geometric index, eccentricity ratio (ER), was derived. The endocardial fractional shortening (FS), midwall fractional shortening (mFS), and the relative wall thickness (RWT) were also measured in each case. Results Depressed value of endocardial FS (20.39 +/- 5.43 vs 34.28 +/- 9.36, P = 0.0046), mFS (33 +/- 8.3 vs 52.5 +/- 11.7, P = 0.0047), and the RWT (0.337 +/- 0.096 vs 0.525 +/- 0.119, P = 0.0002) was observed with dysfunction. ER measured at end-diastole (2.86 +/- 0.703 vs 4.14 +/- 0.38) and end-systole (3.14 +/- 0.79 vs 5.48 +/- 0.74) was found to be decreased in the dysfunction group and more significant at the end-systole (P = 0.00017 vs 6.6E-06). Conclusion This work concludes that the regional and global left ventricle systolic dysfunction can be assessed by the ER measured at end-diastole and end-systole from 2D echocardiogram and may contribute to the high rate of cardiovascular disorders.
7 citations
01 Jan 2005
TL;DR: An effective and clinically relevant parameter for differentiating ischemic optic neuropathy and optic neuritis patients using the visual evoked potentials (VEPs) has been identified and will help the neurologist to clearly classify the diseased patients and help them in early therapy planning.
Abstract: An effective and clinically relevant parameter for differentiating ischemic optic neuropathy (ION) and optic neuritis (ON) patients using the visual evoked potentials (VEPs) has been identified. In this study, statistical analysis has been carried out on time domain parameters of the VEP waveforms recorded for ION (17 patients) and ON (35 patients) groups. The ratio of the amplitude of P100 component of affected eye to fellow eye showed no significant difference between the two groups. However, the mean P100 absolute latency component of ION patients was found to be significantly (p<0.001) shorter than that of ON patients. This parameter will help the neurologist to clearly classify the diseased patients and help them in early therapy planning
7 citations
01 Dec 2012
TL;DR: In this article, a method for optimizing k-space sampling trajectory in compressive sampling MRI (CS-MRI) was presented, where a probability density function (PDF) was proposed to generate sampling trajectories.
Abstract: A method for optimizing k-space sampling trajectory in compressive sampling MRI (CS-MRI) is presented. In k-space, most of the energies are concentrated around the center. When k-space is undersampled, it is required to take most of its higher energy samples for proper CS reconstruction. Therefore more samples are required around the center than the periphery. Using this prior knowledge on k-space energy distribution, a probability density function (PDF) was proposed to generate sampling trajectories. Sampling trajectories were generated for various PDF parameters. These sampling trajectories were applied on the spatial frequency data of fully acquired brain MR images. The optimum sampling trajectory was chosen based on the reconstruction performance. With this optimum trajectory, only 38% of k-space data were required for proper image reconstruction. It was also found that at least 20% of the higher energy samples around the center of k-space were fully required and the rest of the higher energy samples were to be acquired as closely as possible. The optimized sampling trajectory was applied on the simulated k-space data of virtual brain phantom and k-space data of quality assurance phantom. It was verified that the quality of CS reconstructed image matches with the fully reconstructed image.
7 citations
TL;DR: periodic component analysis ( π CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise and provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost.
6 citations
TL;DR: It is shown that the discrete cosine transform of a given set of electrocardiographic cycles can be approximated to a set of narrow band signals by means of an optimally established orthogonality relationship between two complementary subspaces derived from the signal space of DCT coefficients.
6 citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
TL;DR: The steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation are investigated and future research directions related to basic and applied aspects of SSVEPs are outlined.
898 citations
TL;DR: This paper reviews the literature on SSVEP-based BCIs and comprehensively reports on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.
Abstract: Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.
563 citations
TL;DR: Novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented, tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates.
Abstract: In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed
511 citations
421 citations