Dynadx Corporation

Yanhui Liu

Men-Tzung Lo

Dan Guo

A method for quantitative sleep characterization includes acquiring a surface electroencephologram (surf-EEG) signal from an individual by a probe, decomposing the surf-EEG signal into a plurality of intrinsic mode functions (IMFs) by a computer processor in communication with the probe, wherein the plurality of IMFs comprises a first group of IMFs and a second group of IMFs, calculating a ratio comprising an amplitude or power of at least one of the IMFs in the first group in the numerator and a corresponding amplitude or power of at least one of the IMFs in the second group in the denominator; and identifying a period in the surf-EEG waveform as slow wave sleep (SWS) if the ratio associated with the period is above a predetermined threshold.

Systems and methods for quantitatively characterizing slow wave activities and states in sleep

A method for dynamic cerebral autoregulation (CA) assessment includes acquiring a blood pressure (BP) signal having a first oscillatory pattern from a first individual, acquiring a blood flow velocity (BFV) signal having a second oscillatory pattern from the first individual, decomposing the BP signal into a first group of intrinsic mode functions (IMFs), decomposing the BFV signal into a second group of IMFs, determining dominant oscillatory frequencies in the first group of IMFs, automatically selecting a first characteristic IMF from the first group of IMFs that has its associated dominant oscillatory frequency in a predetermined frequency range, automatically selecting a second characteristic IMF from the second group of IMFs, calculating a time sequence of instantaneous phase difference between the first characteristic IMF and the second characteristic IMF, computing an average of the instantaneous phase difference in the time sequence, and identifying a pathological condition in the first individual.

Systems and methods for assessing dynamic cerebral autoregulation

A method for detecting sleep-disordered breathing (SDB) includes acquiring a time sequence of a physiological signal from an individual, wherein the time sequence of the physiological signal includes a oscillatory pattern, computing an oscillatory interval signal using the time sequence of the physiological signal, decomposing the oscillatory interval signal into a plurality of ensemble empirical modes, selecting one of the plurality of ensemble empirical modes, calculating at least one of average amplitude or standard deviation of the instantaneous frequency in the selected ensemble empirical mode; and identifying SDB using at least one of the average amplitude or the standard deviation of the instantaneous frequency.

Growing community of inventors

Mountain View, CA, United States of America

Yanhui Liu