Method and system for utilizing empirical null hypothesis for a biological time series
A method and system for utilizing empirical null hypothesis for a biological time series is disclosed herein. The method also includes calculating an amount that a second plurality of epochs is similar to a first plurality of epochs using large-scale testing to estimate an empirical null hypothesis for a subset of the epochs. The method also includes determining if the second plurality of epochs is from the same EEG recording.
1. A method for determining if a first section of a biological time series is comparable to a second section of a biological time series, the method comprising:
selecting, at a processor, a first section of a biological time series based on a plurality of digital signals generated from a source;
establishing, at the processor, the first section as a baseline section, wherein the processor comprises a computer readable medium and an empirical null hypothesis engine that outputs an empirical null hypothesis;
selecting, at the processor, a second section of the biological time series for measurement;
calibrating and validating a raw score processed through filters and neural network algorithms, wherein said calibrating and validating is based on the empirical null hypothesis, to provide a probability score of an event for the biological time series;
determining, at the processor, a similarity of the second section to the baseline section using the empirical null hypothesis in an empirical examination of data for a subset of the biological time series to validate the probability score of an event for the biological time series; and
generating, at a processor, a Z-score expressing a difference from a baseline that is displayed in a time series on a display device to show how a derived value is varying from the baseline over time, wherein the display device is at least one of a mobile phone, a PDA, a computer, or a TV screen.
2. An EEG system, the EEG system comprising: a processor comprising a computer readable medium and an empirical null hypothesis engine that outputs an empirical null hypothesis, wherein the computer readable medium includes instructions executable by the processor to:
calibrate and validate a raw score processed through artifact reduction filters and neural network algorithms, wherein said calibrating and validating is based on the empirical null hypothesis, to provide a probability score of a seizure event;
select a first epoch of an EEG recording;
establish the first epoch as a baseline epoch;
select a second epoch of the EEG recording for measurement;
determine a similarity of the second epoch to the baseline epoch using the empirical null hypothesis in an empirical examination of data for a subset of the epochs to validate the probability score of a seizure event; and
generate a Z-score expressing a difference from a baseline that is displayed in a time series on a display device to show how a derived value is varying from the baseline over time, wherein the display device is at least one of a mobile phone, a PDA, a computer, or a TV screen.