Modeling in electroencephalography (EEG) and transcranial electrical stimulation (TES) requires the precise geometry and conductivity specifications of the head. Bounded Electrical Impedance Tomography (EIT) offers a portable and affordable method for non-invasive determination of tissue conductivities, because it can be implemented within the same EEG system and electrodes. We demonstrate this method with a high density EEG system and show how the variability of previously reported estimates could be due to the different sophistication of head models employed.
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.
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.
We describe a QRS detection algorithm that is suitable for clean clinical ECGs as well as poorer quality telehealth ECG. A data repository of annotated telehealth ECGs has been made available on-line for future algorithmic development and testing.
We explore a modeling approach that automatically learns the reoccurring waveforms within EEG traces. To summarize waveforms learned across electrodes and subjects we propose a cluster analysis protocol using shift-invariant k-means. The spatial amplitude patterns associated with a subset of the learned waveforms are shown to discriminate between motor imagery modalities.