Biomedical subjects

Narayanan Srinivasan

Publications and source records attributed to Narayanan Srinivasan.

3 recordsLinked to original sources

PubMed2006

Effects of endogenous spatial attention on the detection and discrimination of spatial frequencies.

Two experiments were designed to explore the relationship between visual attention and spatial-frequency processing using a cuing paradigm. In both experiments, the targets were a sharp-edged line segment with high spatial frequencies present and a blurred line segment with only low spatial frequencies present. In each trial an endogenous cue appeared at fixation indicating the probable location, left or right, in which a stimulus would appear. In experiment 1, a typical cuing effect was found with simple reaction times (RTs) for detecting the stimuli being faster when they appeared at a cued (ie attended) compared to an uncued (ie unattended) location. In experiment 2, choice RTs were measured, with participants indicating whether the sharp-edged line segment or the blurred line segment was presented in each trial. In this case, when it was necessary to process the spatial-frequency content of the stimuli, RTs were significantly faster at the attended location only for the sharp-edged line segment. For the blurred line segment without high spatial frequencies, RTs did not differ for attended and unattended locations. The results indicate that endogenous spatial attention interacts differently with high-spatial-frequency and low-spatial-frequency selective mechanisms depending on whether the task is to detect a stimulus or identify it on the basis of its spatial-frequency content.

Adult↗

PubMed2004

Human perceptual performance with nonliteral imagery: region recognition and texture-based segmentation.

In this study the authors address the issue of how the perceptual usefulness of nonliteral imagery should be evaluated. Perceptual performance with nonliteral imagery of natural scenes obtained at night from infrared and image-intensified sensors and from multisensor fusion methods was assessed to relate performance on 2 basic perceptual tasks to fundamental characteristics of the imagery. Specifically, single-sensor imagery and fused multisensor imagery (both achromatic and false color) were used to test performance on a region recognition task and a texture segmentation task. Results indicate that the use of color rendering and type of scene content play specific roles in determining perceptual performance allowed by nonliteral imagery. The authors argue that the usefulness of various image-rendering methods should be evaluated with respect to multiple perceptual tasks.

Adult↗

PubMed2002

Cardiac arrhythmia classification using autoregressive modeling.

BACKGROUND: Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac arrhythmias including atrial premature contraction (APC), premature ventricular contraction (PVC), superventricular tachycardia (SVT), ventricular tachycardia (VT) and ventricular fibrillation (VF). METHODS: AR Modeling was performed on ECG data from normal sinus rhythm as well as various arrhythmias. The AR coefficients were computed using Burg's algorithm. The AR coefficients were classified using a generalized linear model (GLM) based algorithm in various stages. RESULTS: AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classification algorithm. CONCLUSION: The results show that AR modeling is useful for the classification of cardiac arrhythmias, with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.

Arrhythmias, Cardiac↗