Simple T Wave Metrics May Better Predict Early Ischemia as Compared to ST Segment
There is pressing clinical need to identify developing heart attack in patients as early as possible. State-of-the-art tools do not identify all patients with cardiac ischemia, worsening risk for adverse events and outcomes. We aimed to explore the portions of ECG cardiac repolarization that best captured electrophysiological changes associated with ischemia.
A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness
Accurate decomposition torque into its components has important clinical implications for the diagnosis, assessment, and monitoring of neuromuscular diseases that change the muscle tone, such as in spinal cord injury, cerebral palsy, multiple sclerosis, stroke and Parkinson’s disease. MATLAB code for the SDSS algorithm is available from our Github repository.
In-Vivo Electrophysiological Study of Induced Ventricular Tachycardia in Intact Rat Model of Chronic Ischemic Heart Failure
Ventricular tachycardia and ventricular fibrillation are the most common causes of sudden cardiac death in patients with chronic heart failure (CHF). We investigated the electrophysiologic consequences of ischemia in our in vivo rat model of chronic heart failure.
Orientation Independent Catheter-Based Characterization of Myocardial Activation
High density, multielectrode catheters are enabling new and more effective methods to map cardiac arrhythmias. We describe Omnipolar mapping Technology (OT) which relies on a traveling wave approximation to derive bipolar electrophysiologic signals along anatomic and physiologically meaningful directions.
Spatially Coherent Activation Maps for Electrocardiographic Imaging
ECGi is an emerging non-invasive technique that computes unipolar electrograms (EGMs) at the epicardial surface from ECG recordings and torso anatomy. We propose a new method that uses estimates of delays between neighboring points on top of local estimates. It improves the activation maps, yielding a 19% reduction in relative error compared to our reference clinical data.
SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal
Our work emphasizes the understanding of clinical considerations and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during preprocessing and postprocessing are investigated for their effect on seizure prediction accuracy.