Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Abstract: A system of seizure detection including one or more processing circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine a plurality of metrics based on the EEG signal, the plurality of metrics indicating non-linear features of the EEG signal. The one or more processing circuit are configured to perform a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal indicates a candidate seizure or that the EEG signal is insignificant, perform a secondary analysis with one or more metrics of the plurality of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal is insignificant, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure.

Abstract: A system of seizure detection including one or more processing circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine a plurality of metrics based on the EEG signal, the plurality of metrics indicating non-linear features of the EEG signal. The one or more processing circuit are configured to perform a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal indicates a candidate seizure or that the EEG signal is insignificant, perform a secondary analysis with one or more metrics of the plurality of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal is insignificant, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A system of seizure detection including one or more processing circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine a plurality of metrics based on the EEG signal, the plurality of metrics indicating non-linear features of the EEG signal. The one or more processing circuit are configured to perform a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal indicates a candidate seizure or that the EEG signal is insignificant, perform a secondary analysis with one or more metrics of the plurality of metrics to determine whether the EEG signal indicates the candidate seizure or that the EEG signal is insignificant, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

Abstract: Free-field-to-eardrum transfer functions (FETF's) are developed by comparing auditory data for points in three-dimensional space for a model ear and auditory data collected for the same listening location with a microphone. Each FETF is represented as a weighted sum of frequency-dependent functions obtained from an expansion of the measured FETF's covariance matrix. Spatial transformation characteristic functions (STCF's) are applied to transform the weighted frequency-dependent factors to functions of spatial variables for azimuth and elevation. A generalized spline model is fit to each STCF to filter out noise and permit interpolation of the STCF between measured points. Sound is reproduced for a selected direction by synthesizing the weighted frequency-dependent factors with the smoothed and interpolated STCF's.

Abstract: Signals from plural electrodes placed at known positions on the head of an individual are digitized and processed in a computer processor which includes a bank of spatial filters. The filters are designed to meet a linearly constrained minimum variance criterion so that they each substantially pass signal energy from a location within the brain known with respect to the electrodes while rejecting signal energy from other locations within the brain. The outputs of the filters can be used to estimate the signal power at each of the locations in the brain, or the dipole moment at each location, and this information can be displayed on a display device to provide a map of source activity within the brain. The spatial filters do not require prior knowledge of the number of sources, and the number of discrete sources can be identified as well as the location, power, and dipole moment of the sources.

Abstract: A hearing aid system utilizes digital signal processing to correct for the hearing deficit of a particular user and to maximize the intelligibility of the desired audio signal relative to noise. An analog signal from a microphone is converted to digital data which is operated on by a digital signal processor, with the output of the digital signal processor being converted back to an analog signal which is amplified and provided to the user. The digital signal processor includes a time varying spectral filter having filter coefficients which can be varied on a quasi-real time basis to spectrally shape the signal to match the hearing deficit of the user and to accommodate ambient signal and noise levels. The coefficients of the spectral filter are determined by estimating the energy in several frequency bands within the frequency range of the input signal, and using those energy estimates to calculate desired gains for the frequency bands and corresponding spectral filter coefficients.

Abstract: A hearing aid system utilizing digital signal processing is programmable to fit the hearing deficit of a particular use and adaptive to the sound environment to maximize the intelligibility of the desired audio signal relative to noise. An analog signal picked from a microphone is amplified, filtered and converted to digital data. A digital signal processor preferably performs spectral shaping on the data to match the user's preference and performs a non-linear adaptive amplification function on the digital data. The amplification gain function may include several piecewise linear sections, including a first section providing expansion up to a first knee point, a second section providing linear amplification from the first knee point to a second knee point, and a third section providing compression for signals above the second knee to reduce the effort of over range signals and minimize loudness discomfort to the user.

Abstract: A mounting apparatus for mounting a microphonic probe tube on a patient includes a headband adapted to fit securely on the head of a patient and an adjusting arm which extends outwardly from the headband above each ear of the patient. Each adjusting arm is mounted to the headband in a manner which allows lateral adjustment of the position of the arm so that it can be located directly over the ear canal. The microphonic probe tube is mounted to a mounting rod extending downwardly from the adjusting arm and can be positioned at a point directly outwardly from the ear canal. When the probe tube is so positioned, a dial at the end of the adjusting arm can be turned by the operator to move the probe tube tip a selected distance into the ear canal of the patient to a position at which the real ear measurements are to be made.