Automated detection of sleep and waking states

Abstract

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.

1 . A method, comprising:
 obtaining data indicative of brainwave activity;   normalizing a raw spectrogram utilizing at least one frequency range of said data to change a power level of the data in said at least one frequency range relative to data in another frequency range for bin-to-bin comparison, to form normalized data indicative of brainwave activity; and   analyzing said normalized data indicative of brainwave activity utilizing a computing device to provide at least one parameter indicative of sleep state from said analyzing.   
     
     
         2 . A method as in  claim 1 , wherein said analyzing comprises automatically clustering said normalized data into clusters, and using said clusters in said analyzing, to determine said parameter. 
     
     
         3 . A method as in  claim 1 , wherein said normalizing comprises Z scoring the data. 
     
     
         4 . A method as in  claim 1 , further comprising a second normalizing the data, to form double normalized data, prior to said analyzing. 
     
     
         5 . A method as in  claim 1 , wherein said parameter indicative of sleep state comprises a probable sleep state corresponding to a current time period. 
     
     
         6 . A method as in  claim 1 , wherein said parameter indicative of sleep state comprises information indicative of likely drug consumption. 
     
     
         7 . A method as in  claim 1 , wherein said normalizing is carried out using a computer to change the data. 
     
     
         8 . The method of  claim 1  further comprising removing artifacts from the data indicative of brainwave activity. 
     
     
         9 . The method of  claim 1  further comprising, prior to said normalizing, segmenting the data indicative of brainwave activity in a plurality of time segments. 
     
     
         10 . The method of  claim 16  wherein the segmenting comprises using one of a scanning window or a sliding window. 
     
     
         11 . The method of  claim 17  wherein the segmenting comprises determining at least one time series increment selected from the group consisting of:
 whole time series; 
 overlapping time series; and 
 non-overlapping time series. 
 
     
     
         12 . An apparatus, comprising: a computing device, receiving at least one signal indicative of brainwave activity, and normalizing at least one frequency range of said signal to change a power level of data in said at least one frequency range relative to data in another frequency range, to form normalized data indicative of brainwave activity and using said normalized data indicative of brainwave activity to determine at least one parameter indicative of sleep state. 
     
     
         13 . An apparatus as in claim  41 , wherein said computing device carries out said normalizing by Z scoring the data. 
     
     
         14 . An apparatus as in claim  41 , wherein said computer operates to carry out a second normalizing of the data, to form double normalized data, prior to using said data. 
     
     
         15 . An apparatus as in claim  43 , wherein said second normalizing carried out by said computer comprises normalizing frequencies across time. 
     
     
         16 . An apparatus as in claim  42 , wherein said computer operates based on a discrimination function which represents characteristics of said double normalized data for plurality of different sleep states, and uses said discrimination function to determine a sleep state from said normalized data. 
     
     
         17 . An apparatus as in claim  46  wherein said discrimination function is a function that is in terms of frequencies which are present in specified ranges and not present in specified other ranges, to define a sleep state. 
     
     
         18 . An apparatus as in claim  43 , wherein said computer operates to determine a preferred frequency as a frequency which has a highest normalized value in any specified time, and analyzes the preferred frequency to determine said at least one parameter. 
     
     
         19 . An apparatus as in claim  43 , wherein said computer determines a fragmentation of the double normalized data as a part of said analyzing. 
     
     
         20 . An apparatus as in claim  41 , further comprising a brain wave electrode, connected to obtain said signal. 
     
     
         21 . An apparatus, comprising:
 a first receiving part, receiving information indicative of brainwave signals; and   a processing part, normalizing at least one frequency range of said brainwave signals, to form normalized data indicative of brainwave activity and using said normalized data indicative of brainwave activity to determine at least one parameter indicative of sleep state.   
     
     
         22 . An apparatus as in claim  50 , wherein said processing part carries out said normalizing by Z scoring the data. 
     
     
         23 . An apparatus as in claim  50 , wherein said processing part carries out two separate normalizing of the data, to form double normalized data, prior to using said data.