Abstract: Deep brain stimulation (DBS) can be used to treat many neurological conditions beyond traditional movement disorders. When patients do not suffer from traditional movement disorders, medical professionals cannot use traditional observation-based methods to configure the DBS system. A new method for selecting stimulation configurations can include recording internal data and external data as the patient performs (or attempts to perform) a motor task. The internal data is electrophysiology data recorded by a plurality of DBS electrodes, used to identify at least one of the plurality of electrodes closest to a neuronal population involved in control of the at least one motor task. The external data is electroencephalogram (EEG) data recorded by scalp electrodes, which is used to select at least one of the potential stimulation electrodes to deliver the DBS. When the electrode(s) delivering the DBS are selected, optimal parameters for the DBS are then chosen.

Abstract: General diagnostic and real-time application of digital Hermite functions allows features to be extracted from a measured signal through expansion of the measured signal. Specifically, the digital Hermite functions represent the shape of the measured signal in a set of vectors derived from a symmetrical tridiagonal matrix. This allows for efficient computation of the Hermite expansion coefficients, in real-time, to represent the expanded signal. The signal expansion also allows any artifacts, such as noise, to be isolated and removed, allowing the underlying signal of interest to be revealed.

Abstract: The present invention relates to general diagnostic and real-time applications of discrete Hermite functions to digital data. More specifically, the invention relates to methods and systems for the application of dilated discrete digital Hermite functions (DDHF) to biomedical data, for example, to extract features from digital signals, including but not limited to, ECGs, EMGs, EOGs, EEGs, and others, by expanding the measured signals using a computationally efficient technique. These digital Hermite functions form the basis for the new discrete Hermite transform, generated on a beat by beat basis, which provides information about the shape of the signals, such as that in the BCG artifact in an EEG or in an ECG interval, or any noise in any other electrical signal. An automated system and method for real-time interpretation of any abnormalities present in a digital biomedical signal is provided.

Abstract: The present invention relates to methods and systems for evaluating abnormalities in electrocardiograms (ECGs), including abnormalities associates with cardiac ischemia. More particularly, the present invention relates to an automated system and method for interpreting any abnormalities present in an electrocardiogram (ECG), including those abnormalities associated with cardiac ischemia. In one embodiment, the present invention relates to a method for monitoring abnormalities in an ECG, the method comprising the steps of: (a) gathering at least one ECG; (b) subjecting the at least one ECG to a QRS detection algorithm in order to scan for R-peak location; (c) calculating the Hermite coefficients corresponding to the individual ECG complexes from each individual ECG; and (d) subjecting the Hermite coefficients to a Neural Network in order to determine the present and/or absence of ECG abnormalities.