System and method for SPO2 instability detection and quantification

Abstract

The disclosed embodiments relate to a system and method for analyzing data. An exemplary method comprises the acts of receiving data corresponding to at least one time series, and computing a plurality of sequential instability index values of the data. An exemplary system comprises a source of data indicative of at least one time series of data, and a processor that is adapted to compute at least one of a plurality of sequential instability index values of the data.

1 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series; and    computing a plurality of sequential instability index values of the data.    
   
   
       2 . The method recited in  claim 1 , converting the plurality of sequential instability index values into an instability index time series.  
   
   
       3 . The method recited in  claim 2 , comprising analyzing the instability index time series to detect at least one of a pattern and a threshold.  
   
   
       4 . The method recited in  claim 1 , comprising producing an output if at least one of the plurality of sequential instability index values exceeds a threshold.  
   
   
       5 . The method recited in  claim 1 , comprising expressing at least one of the plurality of sequential instability index values according to a numerical scale.  
   
   
       6 . The method recited in  claim 5 , wherein the numerical scale comprises a finite range.  
   
   
       7 . The method recited in  claim 1 , comprising converting at least one of the plurality of sequential instability index values to correspond to a numerical scale.  
   
   
       8 . The method recited in  claim 7 , wherein the numerical scale comprises a finite range.  
   
   
       9 . The method recited in  claim 1 , wherein the at least one time series includes data indicative of an SPO2 level of a person.  
   
   
       10 . The method recited in  claim 1 , wherein the at least one time series includes data indicative of a CO2 level of a person.  
   
   
       11 . The method recited in  claim 1 , wherein the at least one time series includes data derived from a plethesmographic pulse.  
   
   
       12 . The method recited in  claim 1 , wherein the at least one time series includes data indicative of a respiration level of a person.  
   
   
       13 . The method recited in  claim 1 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a peak measure of the at least one time series.  
   
   
       14 . The method recited in  claim 1 , wherein the peak measure comprises at least one of area, duration, magnitude, value, slope, spatial pattern, temporal pattern, frequency pattern, and shape.  
   
   
       15 . The method recited in  claim 1 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a nadir measure of the at least one time series.  
   
   
       16 . The method recited in  claim 1 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a clustering measure of the at least one time series.  
   
   
       17 . The method recited in  claim 1 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a perturbation measure of the at least one time series.  
   
   
       18 . The method recited in  claim 1 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a recovery measure of the at least one time series.  
   
   
       19 . A system, comprising: 
 a source of data indicative of at least one time series of data; and    a processor that is adapted to compute at least one of a plurality of sequential instability index values of the data.    
   
   
       20 . The system recited in  claim 19 , wherein the processor is adapted to convert the plurality of sequential instability index values into an instability index time series.  
   
   
       21 . The system recited in  claim 20 , wherein the processor is adapted to analyze the instability index time series to detect at least one of a pattern and a threshold.  
   
   
       22 . The system recited in  claim 19 , comprising an output device that is adapted to produce an output if at least one of the plurality of sequential instability index values exceeds a threshold.  
   
   
       23 . The system recited in  claim 19 , wherein the processor is adapted to express at least one of the plurality of sequential instability index values according to a numerical scale.  
   
   
       24 . The system recited in  claim 23 , wherein the numerical scale comprises a finite range.  
   
   
       25 . The system recited in  claim 19 , wherein the processor is adapted to convert at least one of the plurality of sequential instability index values to correspond to a numerical scale.  
   
   
       26 . The system recited in  claim 25 , wherein the numerical scale comprises a finite range.  
   
   
       27 . The system recited in  claim 19 , wherein the at least one time series includes data indicative of an SPO2 level of a person.  
   
   
       28 . The system recited in  claim 19 , wherein the at least one time series includes data indicative of a CO2 level of a person.  
   
   
       29 . The system recited in  claim 19 , wherein the at least one time series includes data derived from a plethesmographic pulse.  
   
   
       30 . The system recited in  claim 19 , wherein the at least one time series includes data indicative of a respiration level of a person.  
   
   
       31 . The system recited in  claim 19 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a peak measure of the at least one time series.  
   
   
       32 . The system recited in  claim 31 , wherein the peak measure comprises at least one of area, duration, magnitude, value, slope, spatial pattern, temporal pattern, frequency pattern, and shape.  
   
   
       33 . The system recited in  claim 19 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a nadir measure of the at least one time series.  
   
   
       34 . The system recited in  claim 19 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a clustering measure of the at least one time series.  
   
   
       35 . The system recited in  claim 19 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a perturbation measure of the at least one time series.  
   
   
       36 . The system recited in  claim 19 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a recovery measure of the at least one time series.  
   
   
       37 . A pulse oximeter, comprising: 
 a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient; and    a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 time series based on the signal, and to compute a plurality of sequential instability index values of the SPO2 time series.    
   
   
       38 . The pulse oximeter recited in  claim 37 , wherein the processor is adapted to convert the plurality of sequential instability index values into an instability index time series.  
   
   
       39 . The system recited in  claim 38 , wherein the processor is adapted to analyze the instability index time series to detect at least one of a pattern and a threshold.  
   
   
       40 . The pulse oximeter recited in  claim 37 , comprising an output device that is adapted to produce an output indicative of at least one of the plurality of sequential instability index values.  
   
   
       41 . The pulse oximeter recited in  claim 37 , wherein the processor is adapted to express at least one of the plurality of sequential instability index values according to a numerical scale.  
   
   
       42 . The pulse oximeter recited in  claim 41 , wherein the numerical scale comprises a finite range.  
   
   
       43 . The pulse oximeter recited in  claim 37 , wherein the processor is adapted to convert at least one of the plurality of sequential instability index values to correspond to a numerical scale.  
   
   
       44 . The pulse oximeter recited in  claim 43 , wherein the numerical scale comprises a finite range.  
   
   
       45 . The pulse oximeter recited in  claim 40 , wherein the output device is adapted to update the output at a periodic interval.  
   
   
       46 . The pulse oximeter recited in  claim 40 , wherein the output device is adapted to update the output at a threshold change point along the SPO2 time series.  
   
   
       47 . The pulse oximeter recited in  claim 40 , wherein the output device is adapted to update the output at a threshold change region along the SPO2 time series.  
   
   
       48 . The pulse oximeter recited in  claim 37 , comprising an output device that is adapted to produce an output if at least one of the plurality of sequential instability index values exceeds a threshold.  
   
   
       49 . The pulse oximeter recited in  claim 37 , wherein the signal is derived from a plethesmographic pulse.  
   
   
       50 . The pulse oximeter recited in  claim 37 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a peak measure of the SPO2 time series.  
   
   
       51 . The pulse oximeter recited in  claim 37 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a nadir measure of the SPO2 time series.  
   
   
       52 . The pulse oximeter recited in  claim 37 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a clustering measure of the SPO2 time series.  
   
   
       53 . The pulse oximeter recited in  claim 37 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a perturbation measure of the SPO2 time series.  
   
   
       54 . The pulse oximeter recited in  claim 37 , wherein at least one of the plurality of sequential instability index values is characterized at least in part by a recovery measure of the SPO2 time series.  
   
   
       55 . A system for analyzing data, comprising: 
 means for receiving data corresponding to at least one time series; and    means for computing a plurality of sequential instability index values of the data.    
   
   
       56 . A tangible machine-readable medium, comprising: 
 code adapted to access data corresponding to at least one time series; and    code adapted to compute a plurality of sequential instability index values of the data.    
   
   
       57 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series; and    searching the data to identify an incomplete recovery.    
   
   
       58 . The method recited in  claim 57 , comprising searching the data to identify a plurality of sequential incomplete recoveries.  
   
   
       59 . The method recited in  claim 57 , comprising producing an output indicative of the incomplete recovery.  
   
   
       60 . The method recited in  claim 59 , comprising periodically updating the output.  
   
   
       61 . The method recited in  claim 59 , comprising updating the output if a threshold change point occurs along the at least one time series.  
   
   
       62 . The method recited in  claim 59 , comprising updating the output if a threshold change region occurs along the at least one time series.  
   
   
       63 . The method recited in  claim 57 , comprising producing an output if the incomplete recovery exceeds a threshold.  
   
   
       64 . The method recited in  claim 57 , wherein the at least one time series includes data indicative of an SPO2 level of a person.  
   
   
       65 . The method recited in  claim 57 , wherein the at least one time series includes data indicative of a CO2 level of a person.  
   
   
       66 . The method recited in  claim 57 , wherein the at least one time series includes data derived from a plethesmographic pulse.  
   
   
       67 . The method recited in  claim 57 , wherein the at least one time series includes data indicative of a respiration level of a person.  
   
   
       68 . The method recited in  claim 57 , wherein the incomplete recovery is characterized at least in part by a peak measure of the at least one time series.  
   
   
       69 . The method recited in  claim 68 , wherein the peak measure comprises at least one of area, duration, magnitude, value, slope, spatial pattern, temporal pattern, frequency pattern, and shape.  
   
   
       70 . The method recited in  claim 57 , wherein the incomplete recovery is characterized at least in part by a nadir measure of the at least one time series.  
   
   
       71 . The method recited in  claim 57 , wherein the incomplete recovery is characterized at least in part by a clustering measure of the at least one time series.  
   
   
       72 . The method recited in  claim 57 , wherein the incomplete recovery is characterized at least in part by a perturbation measure of the at least one time series.  
   
   
       73 . The method recited in  claim 57 , wherein the incomplete recovery is characterized at least in part by a recovery measure of the at least one time series.  
   
   
       74 . A system, comprising: 
 a source of data indicative of at least one time series of data; and    a processor that is adapted to search for an incomplete recovery represented by the data.    
   
   
       75 . The system recited in  claim 74 , comprising an output device that is adapted to produce an output if the incomplete recovery exceeds a threshold.  
   
   
       76 . The system recited in  claim 74 , wherein the at least one time series includes data indicative of an SPO2 level of a person.  
   
   
       77 . The system recited in  claim 74 , wherein the at least one time series includes data indicative of a CO2 level of a person.  
   
   
       78 . The system recited in  claim 74 , wherein the at least one time series includes data derived from a plethesmographic pulse.  
   
   
       79 . The system recited in  claim 74 , wherein the at least one time series includes data indicative of a respiration level of a person.  
   
   
       80 . The system recited in  claim 74 , wherein the incomplete recovery is characterized at least in part by a peak measure of the at least one time series.  
   
   
       81 . The system recited in  claim 80 , wherein the peak measure comprises at least one of area, duration, magnitude, value, slope, spatial pattern, temporal pattern, frequency pattern, and shape.  
   
   
       82 . The system recited in  claim 74 , wherein the incomplete recovery is characterized at least in part by a nadir measure of the at least one time series.  
   
   
       83 . The system recited in  claim 74 , wherein the incomplete recovery is characterized at least in part by a clustering measure of the at least one time series.  
   
   
       84 . The system recited in  claim 74 , wherein the incomplete recovery is characterized at least in part by a perturbation measure of the at least one time series.  
   
   
       85 . The system recited in  claim 74 , wherein the incomplete recovery is characterized at least in part by a recovery measure of the at least one time series.  
   
   
       86 . A pulse oximeter, comprising: 
 a probe that is adapted to be attached to a body part of a patient to create a signal indicative of an oxygen saturation of blood of the patient; and    a processor that is adapted to receive the signal produced by the probe, to calculate an SPO2 time series based on the signal, and to search for an incomplete recovery represented by the SPO2 time series.    
   
   
       87 . The pulse oximeter recited in  claim 86 , comprising an output device that is adapted to produce an output indicative of the incomplete recovery.  
   
   
       88 . The pulse oximeter recited in  claim 87 , wherein the output device is adapted to update the output at a periodic interval.  
   
   
       89 . The pulse oximeter recited in  claim 87 , wherein the output device is adapted to update the output at a threshold change point along the SPO2 time series.  
   
   
       90 . The pulse oximeter recited in  claim 87 , wherein the output device is adapted to update the output at a threshold change region along the SPO2 time series.  
   
   
       91 . The pulse oximeter recited in  claim 86 , comprising an output device that is adapted to produce an output if the incomplete recovery exceeds a threshold.  
   
   
       92 . The pulse oximeter recited in  claim 86 , wherein the signal is derived from a plethesmographic pulse.  
   
   
       93 . The pulse oximeter recited in  claim 86 , wherein the incomplete recovery is characterized at least in part by a peak measure of the SPO2 time series.  
   
   
       94 . The pulse oximeter recited in  claim 86 , wherein the incomplete recovery is characterized at least in part by a nadir measure of the SPO2 time series.  
   
   
       95 . The pulse oximeter recited in  claim 86 , wherein the incomplete recovery is characterized at least in part by a clustering measure of the SPO2 time series.  
   
   
       96 . The pulse oximeter recited in  claim 86 , wherein the incomplete recovery is characterized at least in part by a perturbation measure of the SPO2 time series.  
   
   
       97 . The pulse oximeter recited in  claim 86 , wherein the incomplete recovery is characterized at least in part by a recovery measure of the SPO2 time series.  
   
   
       98 . A system for analyzing data, comprising: 
 means for receiving data corresponding to at least one time series; and    means for searching the data to identify an incomplete recovery.    
   
   
       99 . A tangible machine-readable medium, comprising: 
 code adapted to access data corresponding to at least one time series; and    code adapted to search the data to identify an incomplete recovery.    
   
   
       100 . A patient monitor, comprising: 
 a source of data corresponding to at least one time series;    a processor that is adapted to detect an incomplete recoveries along the at least one time series, to compute an instability index value based at least in part on the incomplete recovery, and to output an indication of the incomplete recovery and the instability index value.    
   
   
       101 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series;    detecting at least one pattern in the data; and    computing a plurality of sequential instability index values of the data based at least in part on the at least one pattern.    
   
   
       102 . The method recited in  claim 94 , comprising analyzing the pattern.  
   
   
       103 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series; and    detecting a plurality of pattern components of the data;    computing a plurality of sequential instability index values of the data based at least in part on at least one of the plurality of pattern components.    
   
   
       104 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series; and    detecting a plurality of abnormal values in the data;    computing a plurality of sequential instability index values of the data based at least in part on at least one of the plurality of abnormal values.    
   
   
       105 . A method of analyzing data, comprising: 
 receiving data corresponding to at least one time series; and    detecting a plurality of abnormal values in the data;    detecting at least one pattern of at least a subset of the abnormal values,    computing a plurality of sequential instability index values based at least in part on the detecting of the plurality of abnormal values and the at least one pattern.    
   
   
       106 . A method of analyzing data from a patient, comprising: 
 receiving data corresponding to at least one time series having at least one complex pattern;    computing a plurality of sequential instability index values indicative of an instability of the at least one pattern; and    converting the plurality of sequential instability index values into an instability index time series.    
   
   
       107 . A method of analyzing data from a patient, comprising: 
 receiving data corresponding to at least one time series; and    computing a plurality of sequential instability index values indicative of a plurality of sequential indications of a magnitude of instability of the patient.    
   
   
       108 . The method recited in  claim 107 , comprising converting the plurality of sequential instability index values into an instability index value time series.  
   
   
       109 . A method of analyzing data from a patient, comprising: 
 receiving data corresponding to at least one time series; and    computing a plurality of sequential instability index values indicative of at least one pattern of magnitude of instability of the patient.    
   
   
       110 . The method recited in  claim 109 , comprising converting the plurality of sequential instability index values into an instability index value time series.