SYSTEMS AND METHODS FOR THE DIAGNOSIS AND TREATMENT OF NEUROLOGICAL DISORDERS
Systems and methods for data compression which facilitate the diagnosis and treatment of neurodevelopmental and neurodegenerative disorders. The methods comprise performing the following operations by a computing device: generating Normalized Data (“ND”) from Original Data (“OD”) that defines a Normalized Waveform (“NW”) that is unitless and scaled from zero to one; processing ND to extract Micro-Movement Data (“MMD”) defining a Micro-Movement Waveform (“MMW”) comprising a plurality of MMD points; and generating compressed data comprising a stochastic signature of MMW. Each MMD point determined based on a value of a peak of NW and a value representing an average of all data point values between a first valley of NW immediately preceding the peak and a second valley of NW immediately following the peak. The stochastic signature is defined by empirically estimated values of at least one parameter representing a Probability Distribution Function (“PDF”) of a continuous family of PDFs.
1 . A method for data compression, comprising:
performing operations, by a computing device, to generate normalized data from original data defining neural or bodily rhythms of a subject, the normalized data defining a normalized waveform that is unitless and scaled from zero to one;
processing, by the computing device, the normalized data to extract micro-movement data defining a micro-movement waveform comprising a plurality of micro-movement data points, each said micro-movement data point determined based on a value of a peak of the normalized waveform and a value representing an average of all data point values between a first valley of the normalized waveform immediately preceding the peak and a second valley of the normalized waveform immediately following the peak; and
generating, by the computing device, compressed data comprising a stochastic signature of the micro-movement waveform, said stochastic signature defined by empirically estimated values of two parameters representing a probability distribution function of a continuous family of probability distribution functions.
2 . The method according to claim 1 , wherein the original data comprises sensor data specifying a raw neural or bodily rhythm created in part by a human subject's physiological system.
3 . The method according to claim 1 , wherein the normalized data defines a normalized waveform representing events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform defined by the original data.
4 . The method according to claim 1 , further comprising performing operations, by the computing device, to estimate moments of a continuous family of probability distribution functions best describing a continuous random process.
5 . The method according to claim 4 , wherein the moments include at least one of a first moment comprising a mean value, a second moment comprising a variance value, a third moment comprising skewness, and a fourth moment comprising kurtosis.
6 . The method according to claim 4 , wherein the probability distribution functions comprise a function from a continuous Gamma family of probability distribution functions.
7 . The method according to claim 1 , wherein the stochastic signature is obtained by:
performing statistical data binning using the micro-movement data;
processing the binned micro-movement data to generate a frequency histogram;
generating probability distribution function waveforms using different sets of variable values;
comparing the probability distribution function waveforms to the frequency histogram to identify a probability distribution function waveform from the probability distribution function waveforms that most closely matches a shape and a dispersion of the frequency histogram; and
considering the variable value used for generating the probability distribution function waveform as the stochastic signature.
8 . The method according to claim 7 , wherein vertical columns of the frequency histogram show how many micro-movement data points are contained in each of a plurality of statistical data bins.
9 . The method according to claim 1 , further comprising using the stochastic signature to obtain at least one of a Noise-to-Signal Ratio (“NSR”) for a signal defined by the original data and a level of randomness in the original data.
10 . The method according to claim 1 , further comprising mapping the stochastic signature on a parameter plane to determine noise and randomness classifications of a subject's neural or bodily rhythms defined by the original data.
11 . The method according to claim 1 , further comprising using the stochastic signature as a seed value to an encryption algorithm for encrypting sensitive information prior to being communicated over a network communications link.
12 . The method according to claim 1 , further comprising:
causing the computing device or a remote computing device to operate in a first session state in which first testing operations are performed to stimulate movement by a human subject in accordance with first testing parameters;
selecting or generating second testing parameters different from the first testing parameters based on the stochastic signature; and
transitioning the session state of the computing device or the remote computing device from the first session state to a second session state in which second testing operations are performed to stimulate movement by the human subject in accordance with the second testing parameters.
13 . The method according to claim 12 , wherein the transitioning is controlled by the human subject's nervous system evolving with treatment of a neurological disorder.
14 . The method according to claim 1 , further comprising receiving by the computing device the original data which was sent from a remote device over a network.
15 . A system, comprising:
a computing device configured to
generate normalized data from original data defining neural or bodily rhythms of a subject, the normalized data defining a normalized waveform that is unitless and scaled from zero to one,
process the normalized data to extract micro-movement data defining a micro-movement waveform comprising a plurality of micro-movement data points, each said micro-movement data point determined based on a value of a peak of the normalized waveform and a value representing an average of all data point values between a first valley of the normalized waveform immediately preceding the peak and a second valley of the normalized waveform immediately following the peak, and
generate compressed data comprising a stochastic signature of the micro-movement waveform, said stochastic signature defined by empirically estimated values of two parameters representing a probability distribution function of a continuous family of probability distribution functions.
16 . The system according to claim 15 , wherein the original data comprises sensor data specifying a raw neural or bodily rhythm created in part by a human subject's physiological system.
17 . The system according to claim 15 , wherein the normalized data defines a normalized waveform representing events of interest in a continuous random process capturing rates of changes in fluctuations in amplitude and timing of an original raw waveform defined by the original data.
18 . The system according to claim 15 , wherein the computing device is further configured to estimate moments of a continuous family of probability distribution functions best describing a continuous random process.
19 . The system according to claim 18 , wherein the moments include at least one of a first moment comprising a mean value, a second moment comprising a variance value, a third moment comprising skewness, and a fourth moment comprising kurtosis.
20 . The system according to claim 18 , wherein the probability distribution functions comprise a function from a continuous Gamma family of probability distribution functions.
21 - 32 . (canceled)