Scholars@Duke
Pratik Yashvant Chhatbar

Pratik Yashvant Chhatbar

Adjunct Assistant Professor in the Department of Neurology
Neurology, Stroke and Vascular Neurology
40 Duke Medicine Circle, Duke South, Suite 3052, DUMC Box 3824, Durham, NC 27710
40 Duke Medicine Circle, Duke Clinic Suite 3052, Durham, NC 27710

Scholarly Works - Conferences

Abstract P592: Admission Hyperglycemia is Associated With Subsequent Ischemic Stroke After Transient Ischemic Attack or Minor Stroke: A Secondary Analysis of the POINT Trial

Conference Stroke · March 2021 Introduction: Hyperglycemia is associated with increased lesion volume and worse functional outcome after acute ischemic stroke, however, it is not known whether it is associated with further cerebrov ... Full text Cite

Abstract MP46: Internal Carotid Artery Web and Acute Ischemic Stroke - A Systematic Review and Meta-Analysis

Conference Stroke · March 2021 Introduction: The carotid web is a compelling potential mechanism of embolic ischemic stroke. In this study, we perform a systematic review and meta-analysis to determine the prevalence of ipsilesiona ... Full text Cite

Biosafety of low-intensity pulsed transcranial focused ultrasound brain stimulation - A human skull study

Conference Proceedings of SPIE the International Society for Optical Engineering · January 1, 2021 Among a variety of existing modalities for noninvasive brain stimulation (NIBS), low-intensity pulsed transcranial focused ultrasound (tFUS) is a promising technique to precisely stimulate deep brain structures due to its high spatial specificity and super ... Full text Cite

Abstract TP152: Correlation of NIH Stroke Scale and Fugl-Meyer Motor Scales in a Longitudinal Stroke Recovery Study: Implication for Feasibility Survey for Stroke Rehabilitation Trial

Conference Stroke · February 2017 Introduction: Recruitments of stroke recovery trials have been challenging. NIH stroke scale (NIHSS) has been universally collected in the acute stroke phase, but stroke recovery trials generally use ... Full text Cite

Processing of a Directionally Dependent Reward Signal in Motor and Somatosensory Units

Conference 2014 IEEE SIGNAL PROCESSING IN MEDICINE AND BIOLOGY SYMPOSIUM (SPMB) · 2014 Link to item Cite

Stochastic kernel temporal difference for reinforcement learning

Conference IEEE International Workshop on Machine Learning for Signal Processing · December 5, 2011 This paper introduces a kernel adaptive filter using the stochastic gradient on temporal differences, kernel TD(λ), to estimate the state-action value function Q in reinforcement learning. Kernel methods are powerful for solving nonlinear problems, but the ... Full text Cite

Sparse coding of movement-related neural activity

Conference 2011 IEEE Signal Processing in Medicine and Biology Symposium Spmb 2011 · December 1, 2011 Modern systems neuroscience benefits from the ability to record from and digitize a large amount of functional data from hundreds or even thousands of neurons. Understanding, transmitting, storing, and parsing information of such volume and complexity call ... Full text Cite

Control of a center-out reaching task using a reinforcement learning Brain-Machine Interface

Conference 2011 5th International IEEE EMBS Conference on Neural Engineering Ner 2011 · July 20, 2011 In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to ad ... Full text Cite

Reinforcement learning via kernel temporal difference.

Conference Annu Int Conf IEEE Eng Med Biol Soc · 2011 This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this pap ... Full text Link to item Cite

Comparison of force and power generation patterns and their predictions under different external dynamic environments.

Conference Annu Int Conf IEEE Eng Med Biol Soc · 2010 Use of neural activity to predict kinematic variables such as position, velocity and direction etc of movements has been implemented in real-time control of robotic systems and computer cursors. In everyday life, however, we generate variable amounts of fo ... Full text Link to item Cite