Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

A technical solution is described for implementing a computer-executed signal processing algorithm to search for time domain segments of a recorded electroencephalogram (EEG) that are highly correlated, either positively or negatively, to one or more, individual, synthetically generated, single-period single-frequency (SPSF) sinusoids. The SPSFs are motivated by the combined concepts of individual Striatal Beat Frequencies (SBF) used to model cortical neuron activity, Frequency Domain Reflectometry used to study Voltage Standing Wave Ratios (VSWR), Geophysics Seismograms, and ghosting effects of multipath passing through periodic sinusoids. This computationally intense approach is only recently realizable through the advent of high performance computing. The SPSF approach, since it is not constrained to the error-laden one-window-fits-all approach of the Time-Frequency Spectrogram, offer's a more detailed basis to assess, and truer visualization of, the health of brain's electrical activities. This approach is a push-back against the Uncertainty Principal.

A method for detecting an epileptic spike includes: obtaining, by a first module of a network model, a local feature of data to be detected, and obtaining, by a second module of the network model, a global feature of the data to be detected; and determining, by a third module of the network model, a detection result of whether there is the epileptic spike in the data to be detected according to the local feature and the global feature. The data to be detected contains a temporal domain and a spatial domain represented by multiple channels, the local feature is a single channel feature, and the global feature is a multichannel feature.

A computer-implemented method for operating an autonomous or semi-autonomous vehicle may include identifying a vehicle operator and retrieving an associated vehicle operator profile. Operating data regarding operation of the autonomous or semi-autonomous vehicle may be received that includes data from sensors disposed within the vehicle. When a request to enable an autonomous operation feature is received, (i) autonomous operation risk levels associated with vehicle operation by the autonomous operation feature based upon the received operating data, and (ii) operator risk levels associated with vehicle operation by the vehicle operator based upon the vehicle operator profile are determined. Autonomous operation feature enablement may be allowed based upon a comparison of (i) autonomous operation risk levels with (ii) operator risk levels. As a result, only safe autonomous feature engagement may be facilitated, and risk averse vehicle owners may receive insurance discounts based upon this safe autonomous feature engagement functionality.

Electrodes for use in electroencephalographic recording, including consciousness and seizure monitoring applications, have novel features that speed, facilitate or enforce proper placement of the electrodes, including any of alignment indicators, tabs and juts, color coding, and an insulating bridge between reference and ground electrodes which ensures a safe application distance between the conductive regions of the two electrodes in the event of cardiac defibrillation. A method of using a set of at least four such electrodes is also disclosed.

A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

A method for graphically displaying evoked potentials is disclosed herein. The method transforms each of an averaged evoked potentials into a single vertical line, wherein a first amplitude range is represented by a first color, a second amplitude range is represented by a second color, a third amplitude range is represented by a third color and a fourth amplitude range is represented by a fourth color.

One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams may be from multiple people and applied to an individual person.

A set of one or more processing circuits obtains eye movement-related eardrum oscillation (EMREO)-related measurements from one or more EMREO sensors of a hearing instrument. The EMREO sensors are located in an ear canal of a user of the hearing instrument and are configured to detect environmental signals of EMREOs of an eardrum of the user of the hearing instrument. The one or more processing circuits may perform an action based on the EMREO-related measurements.

Systems and methods for managing sleep quality of a patient, comprising: collecting physiological signal data of the patient using a data acquisition unit electrically coupled to at least one sensor affixed to the patient that generates the physiologic signal data; using one or more hardware processors executing instructions stored in a storage device: filtering the physiological signal data into a plurality of frequency bands corresponding to a plurality of power spectra waveforms; and characterizing an etiology of sleep quality of the patient based on a comparison of at least a first power spectra waveform of the plurality of power spectra waveforms against at least a second power spectra waveform of the plurality of power spectra waveforms, wherein the sleep quality of the patient is managed based on the characterized etiology of sleep.