Anesthesia monitoring system based on electroencephalographic signals

A system which classifies patients according to their level of awareness or consciousness using measures derived solely from electroencephalograph (EEG) signals. The system comprises multiple observes of characteristics of signals, including artifact detectors, especially magnitude artifact detectors, eye blink detectors, stationarity/RMS detectors, slew rate detectors, and burst suppression detectors, and determination of power in certain frequency bands. The system produces a single derived probabilistic measure of conscious awareness called the patient state index (PSI) and displays values of trends in that index and values of an artifact index, an EMG index, and a suppression ratio in order to give the operator current information on the quality of the signal input. The PSI is derived from a statistical analysis using empirically derived population norms and other parameters.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
 THE HEADSET AND THE PATIENT MODULE In a related previous application, Ser. No. 09/113,946, filed Jul. 10, 1998, incorporated herein by reference as though fully set forth, one of the current inventors and others described a head set which can extract from the patient's head EEG signals from five favored locations of the set of international standard locations. The five favored locations are denoted in the international system by Fp1, Fp2, Fpz′, Cz, and Pz. In current embodiments these are electrically referenced to linked ear or linked mastoid contacts. EEG data from four of these five specific locations are analyzed. Alternatively, a more elaborate headset, such as that disclosed by Imran, U.S. Pat. No. 5,479,934, issued Jan. 2, 1996, can be used to obtain information from the four desired locations. As shown in FIG. 3 , the PSA Patient Interface consists of a Patient Module 42 , patient interface cable 43 , and a Patient Electrode Set 44 designed to provide a superior quality, programmable patient interface for EEG monitoring in the OR and ICU. The Patient Module is housed in a custom molded plastic enclosure with an integral universal-mounting bracket that facilitates attachment to an IV pole, bed sheet or rail. A detachable patient interface cable provides a quick connect/disconnect capability to the PSA appliance or Patient Module. EEG signals from the appliance are acquired with an isolated instrumentation grade, 4-channel pre-amplifier assembly and programmable multiplexed high speed A/D converter. The signal inputs are acquired referentially with reformatting provided by the Host application if necessary. Preamplifier optimization for EEG is standard, with EP and ECG optional by design. The combination of optically isolated data pathways, a low leakage/high isolation power converter and amplifiers with precise gain and band-pass matching results in greater than 120 dB CMRR. Calibration, Impedance Test, and Normal Operation are remotely controlled through the DSP Interface using commands generated by the Host Application. A full duplex connection is provided between the Patient Module and the DSP via dual optical-isolators that comply with VDE0884 for safety with extremely low leakage. The power converter is a UL Listed & Medical Grade. This extreme isolation results in negligible leakage currents and assures IEC601/UL2601 compliance with superior common mode performance. The proprietary ISA bus DSP card provides a real time interactive link between the host and patient module and manages the acquisition, calibration and impedance functions of the patient module. Balanced differential drivers are used to minimize EMI associated with serial data transmission while providing the ability to extend the link to approximately 1000 feet. Filtering and decimation of the acquired data takes place in the DSP. 
 THE ANALYSIS UNIT The PSA 4000 analysis unit embodies and operates by means of a complex Algorithm referred to hereafter as the PSA 4000 Algorithm. The system operates in three distinct modes, sometimes referred to as states, with different characteristics. The three states are labeled as follows: 1) Data Accumulation; 2) Awake Patient; and 3) Unconscious Patient. Two very specific and well-defined events cause the transition of the system among these four states. These events are labeled as: 1) Sufficient Data Accumulated ; and 2) Loss of Consciousness. The identification of events and the switching among the states at the occurrence of an identified event is described further below following the description of the operation of the PSA 4000 Algorithm. The following notation is used in describing EEG data: 2 A sample is actually a set of four values, one for each of the Sample electrode, associated with a particular instant of time. j All samples have a sample index, denoted by j that increases with time. j &equals; 0 The index of the most recent sample has index 0. All other samples therefore have a negative index. t j The time associated with a particular sample set is denoted by t j . The time resolution of the algorithm neglects the miniscule differences between time values of electrode values in a sample. S j , S(j) The sample associated with a particular index j. S(t j ) The sample associated with a particular time. The basic operational modules of the PSA 4000 Algorithm are: EEG Data Collection, Filtering and Decimation; Artifact Detection and Signal Morphology Analysis; Eye-blink Observation; Suppression Observation; Calculation of Artifact Index; Calculation of Suppression Ratio Index; FFT Calculation; Spectral Band Decomposition; Calculation of the EMG Index; EMG Beta-5 Observation; Discriminant Observer Calculation of the Probability of Correct Classification; Observer Mediation for the PSI; and Display of the PSI, the Suppression Ratio Index, the EMG Index, and the Artifact Index. 
 Operational Modules 1) EEG Data Collection, Filtering and Decimation The patient module (PM) 42 acquires EEG data at a sampling rate of 2500 Hz. The sampling and processing are established to produce a frequency representation resolution of 0.25 Hz or better. Data on four channels from the headpiece are filtered and decimated by a 10-to-1 low pass decimation filter 100 , as shown in FIG. 5 . This is done every 10 samples resulting in 1 sample every {fraction (1/250)} sec., for an effective sampling rate of f S &equals;250 Hz. After the 10-1 decimation and filter, the EEG data passes through a high-pass filter 102 with a cut-off frequency of f h &equals;0.4 Hz. The most recent L 1 samples are used, where L 1 &equals;f S /f h &equals;625. Filtering starts at the sample s(−½L 1 ) i.e., samples more recent than s(−½L 1 ) are not filtered. The average of the most recent L 1 ′−625 samples is subtracted from sample s(−½L 1 ). We denote the filtered 1 s ′ &af; ( - 1 2 &it; L 1 ) = s &af; ( - 1 2 &it; L 1 ) - 1 L 1 &it; &Sum; j = - L 1 + 1 0 &it; &it; s ′ &af; ( j ) 1 sample by s′(−½L 1 ): The filtered sample is stored in the buffer as shown in FIGS. 8 and 9 and refreshed every half epoch. 2) Artifact Detection and Signal Morphology Analysis Artifact detection and analysis are performed once every sample, i.e., 250 times per second in an Artifacter Bank 103 - 108 . This analysis results in an artifact type being associated with every sample that is classified as being affected by an artifact. Artifact types are also collated on an epoch-by-epoch basis. Artifact analysis is performed only onfiltered samples. As shown in FIG. 5 , after the high-pass filter, the artifact engine analyzes data on four channels for 5 kinds of artifacts: a. Magnitude 103 b. Slow Eye Blink 104 c. Fast Eye Blink 105 d. Slew rate 107 e. Stationarity 108 . These are later combined in an Artifact Index module 106 . As is illustrated in FIG. 7 , artifact analysis is done on a series of sample sets (sample buffers) of varying sizes. The magnitude, suppression, slew rate, and eye blink artifacts are checked on a buffer of 150 samples. These 150 samples (0.6 seconds) are older than the latest 312 of the 625 samples that have gone through the high-pass filter. Thus, they are identical with the newest 150 samples in the older half of the high-pass filter buffer. Similarly, the rms deviation artifact is checked on the 2000 samples (8.0 seconds) before (older than) the latest 75 of the 150 samples that were checked for the previous artifact types. The conditions used in checking for the various types of artifacts are: i) Magnitude The magnitude of the newestfiltered sample is checked, on each of the four channels 103 . (Recall that the latest filtered sample is older than the latest sample by L½ samples.) If the magnitude of at least one of the channels exceeds a set threshold, the sample is 2 &LeftBracketingBar; s ′ &af; ( - 1 2 &it; L 1 ) &RightBracketingBar; > M thresh 2 classified as a magnitude artifact. Thus the condition is where M thresh is the magnitude threshold. The magnitude threshold is different for different channels and is determined empirically. ii) Slew Rate The slew rate detector 107 checks for sudden changes in the magnitude of samples. The rate of change cannot be greater than 15 &mgr;V over 20 ms. To check this, the detector obtains the largest sample and the smallest sample over the &dgr; slew samples older than s′(−½L 1 −½L 2 ). If the difference between sample s′(−½L 1 −½L 2 ) and either the largest or the smallest sample is greater than 15 &mgr;V, than a slew rate artifact is declared. Mathematically, the conditions can be expressed as: 3 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) - min &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ slew , - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 3 4 max &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ slew , - 1 2 &it; L 1 - 1 2 &it; L 2 ) - s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 4 where L thresh is threshold for the slew rate artifact, equal to 15 &mgr;V. If either one of these conditions is satisfied, a slew rate artifact is declared. iii) Stationarity The stationarity artifact is checked 108 on the L 3 &equals;2000 samples (8.0 seconds) before (older than) sample s′(−½L 1 −½L 2 ) . First the sum of the squares of the samples (not the squares of the deviations) is calculated. This is compared to the sample in the middle of the 2000 samples s′(−½L 1 −½L 2 −½L 3 ). The following condition is checked: 5 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - 1 2 &it; L 3 ) > bigger &it; &it; ( f scale &it; &Sum; j &it; [ s ′ &af; ( j ) ] 2 , f limit ) 5 If this condition is satisfied for a given scale factor and limit term then a stationarity artifact is declared. 3) Eyeblink Observation The conditions for eye blinks, 104 and 105 , are checked only if the slew rate artifact is not detected because the slope required in a slew rate artifact is larger than the slope required for eye blinks The eye blink observer is mathematically similar to the slew rate detector, however, it checks for both positive and negative slopes together, i.e., it checks for EEG humps within certain parameters. First, the observer checks for the conditions on the rise (the first half of the eyeblinks). For small eye blinks, the artifactor checks the &dgr; EBSb samples older than sample s′(−½L 1 −½L 2 ) and obtains the largest and smallest samples. The following conditions are checked: 6 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) - min &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ EBSb , - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 6 max &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ EBSb , - 1 2 &it; L 1 - 1 2 &it; L 2 ) - s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 7 If at least one of these conditions is satisfied, then the conditions on the second half of the eye blink are checked. The observer checks the &dgr; EBSa filtered samples newer than sample s′(−½L 1 −½L 2 ) and obtains the largest and smallest samples. One of the following 7 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) - min &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 , - 1 2 &it; L 1 - 1 2 &it; L 2 + δ EBSa ) > L thresh &it; &NewLine; &it; or 8 max &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ EBSb , - 1 2 &it; L 1 - 1 2 &it; L 2 ) - s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh . 9 conditions must be satisfied: If (Equation 8 or Equation 9) AND (Equation 10 or Equation 11) is satisfied, then 8 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) - min &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ EBLb , - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 10 a small eye blink is declared. Similarly, for large eye blinks, the conditions are: 9 max &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 - δ EBLb , - 1 2 &it; L 1 - 1 2 &it; L 2 ) - s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) > &it; L thresh &it; &NewLine; &it; &NewLine; &it; or &it; &NewLine; &it; AND &it; &NewLine; &it; or 11 s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) - min &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 , - 1 2 &it; L 1 - 1 2 &it; L 2 + δ EBLa ) > L thresh 12 10 max &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 , - 1 2 &it; L 1 - 1 2 &it; L 2 + δ EBLa ) - s ′ &af; ( - 1 2 &it; L 1 - 1 2 &it; L 2 ) > L thresh 13 If (Equation 12 or Equation 13) AND (Equation 14 or Equation 15) is satisfied, then a large eyeblink is declared. The threshold parameters for eye blinks are determined empirically. 4) Suppression Observation Every time a newfiltered sample is obtained, the suppression detector 109 looks at the sum of the squared deviations of samples over the latest filtered 600 milliseconds (150 samples, denoted by L 2 ) and over the latest filtered 20 milliseconds (5 samples, denoted by L 5 ). 11 Δ &af; ( j 1 , j 2 ) = ( &Sum; j = j 1 j 2 &it; [ s ′ &af; ( j ) ] 2 ) - 1 N &af; ( j 1 , j 2 ) [ &Sum; j = j 1 j 2 &it; s ′ &af; ( j ) ] 2 14 For reference, the sum of the squared deviations, generically defined, is: where j 1 andj 2 are sample indices, and No(j 1 , j 2 ) is the number of samples between these indices. This quantity is related to variance of the same sample set (the average of the sum of the squared 12 σ j 1 , j 2 2 = Δ &af; ( j 1 , j 2 ) N &af; ( j 1 , j 2 ) 15 deviations) through The difference between these two sums of squared deviations is intended as an approximate measure of the power in the band between 1.67 Hz and 50 Hz. If, on any one of the four channels, this power is less than a set threshold, then a suppression-type artifact is 13 Δ &af; ( - 1 2 &it; L 1 - L 2 , - 1 2 &it; L 1 ) - Δ &af; ( - 1 2 &it; L 1 - L 5 , - 1 2 &it; L 1 ) < S thresh 16 associated with that sample. Thus the suppression condition is where S thresh is the threshold for the Suppression Artifact. The thresholds are different for the different channels and are determined empirically. (a) Persistent Suppression Ratio (SR) The suppression Observer calculates a quantity called the persistent Suppression Ratio (pSR). It is defined as the percentage, over the past 2.5 minutes, of 2.5-second epochs in which a suppression artifact was detected. The pSR is used later in calculating the PSI from the PCC. The pSR is also calculated based on 2.5-sec suppressed epoch declarations, even though 1.25-sec suppressed epoch declarations are available from the current overlapping-FFT scheme. (Recall that the connecting rule is, if one of the two 1.25-sec epochs in a 2.5-sec epoch is declared suppressed, then the 2.5-sec epoch is declared suppressed.) The pSR is used later in calculating the PSI from the PCC. 5) Calculation of Artifact Index The 2.5-second Artifact Index 106 is one of the four final output parameters communicated to the user of the PSA 4000. The Artifact Index is a time-weighted percentage of 48 overlapped epochs (1.25-second epochs) in the past one minute that were declared as artifacted epochs. The newer half-epochs are weighted more heavily than the older half-epochs. The Artifact Index is calculated every 1.25 seconds. 6) Calculation of Suppression Ratio Index The Suppression Ratio (SR) 143 is also one of the four final output parameters communicated to the user of the PSA 4000. The SR is defined as the percentage, over the past one minute, of 2.5-second epochs in which a suppression artifact was detected. 7) FFT Calculation Whenever 625 continuous good (non-artifacted) samples are calculated, the FFT of the time series (i.e., the set of 625 samples, also called an epoch) is calculated. A Hamming Window is applied to the time series data before the calculation of every FFT (see references). EEG data is sliced into 2.5-second periods, called epochs, containing 625 samples each. The Fourier Transform of an epoch of EEG data is calculated. FFT calculations 126 , 128 are done using a 50% overlap scheme, i.e., FFT calculations are done every 1.25-seconds for data covering the last good 2.5-second period. 8) Spectral Band Decomposition The following band definitions are used in succeeding Algorithmic calculations 146 . The band definitions are given in units of Hertz (Hz). 3 TABLE 1 Band Name Band definition (Hz) &Dgr; 1.5-3.5 &thgr; 3.5-7.5 &agr; 7.5-12.5 &bgr; 12.5-25.0 &bgr; 2 25.0-50.0 &bgr; 5 35.0-50.0 tot 1.5-25.0 The band tot spans only the bands &Dgr; through &bgr;. 9) Calculation of the EMG Index The EMG Index 148 is an indicator is a time-weighted percentage of 1.25-second epochs in the past one minute that had an F 1 &bgr; 2 z-component of greater than 1.96. The newer half-epochs are weighted more heavily than the older half-epochs. 10) EMG BETA-5 observation The F 1 &bgr; 5 raw measure 126 , is the power on the F 1 channel in the &bgr; 5 band. The F 1 &bgr; 5 Z-component is the logarithm of the raw measure, and the F 1 &bgr; 5 Z score is the population-normed Z-component. The F 1 &bgr; 5 index is defined as a running average of the F 1 &bgr; 5 Z-scores over the past twelve overlapped-epochs, in which the newest Z-score is limited to a maximum change of six population standard deviations from the latest running average. 11) Discriminant observer calculations A discriminant 149 is a function of statistical variable that maximizes the separation, in the variable space, of two groups of interest. It is usually a linear combination of the statistical variables. Thus, specification of the discriminant involves both specification of the variables and their weights. i) Raw measures for the Discriminant Observers The Raw Measures used by the discriminant observer 18 are defined by the following table: 4 TABLE 1 Raw Measures as a combination of electrodes and bands tot &Dgr; &thgr; &agr; &bgr; &bgr;2 FP 1 P P FP z' F P P C z P P P z P P P In this matrix, P refers to monopolar power and F refers to mean frequency. The raw measures are averaged over 32 overlapped-epochs. ii) Components for the Discriminant Observer The specific set of raw components calculated in the PSA 4000 Algorithm are as follows: 5 TABLE 2 Raw-components used in calculating the probability of correct classification Raw-Component &num; QUANTITY CHANNEL/BAND(S) 1 monop abs power FP 1 Tot 2 mean frequency FP z' Tot 3 monop abs power P z &agr; 4 power assymetry FP 1 C z &bgr; 2 5 monop power FP z' &agr; 6 relative power P z &Dgr; 7 monop power FP z' &bgr; 8 monop power C z &bgr; 9 monop abs power FP 1 &bgr; 2 The first 8 are used in calculating the PCC. The 9 th is used in calculating the final EMG Index output parameter. Z-components are obtained from the raw components by norming to a set of population means and standard deviations, which are obtained for each component from an experimental study of normative populations. iv) Z-Scores Z scores are either linear combinations of Z components, or identical to the z components. The z-score set used in the PSA 4000 discriminant is: 6 TABLE 3 Z-score set used in calculating the probability of correct classification Zscore &num; Definition in terms of Z-components 1 monop power (FP 1 Tot) 2 mean frequency (FP z' Tot) 3 monop power (FP z' &agr;) - monop power (P z &agr;) 4 power assymetry (FP 1 C z &bgr; 2 ) 5 relative power (P z &Dgr;) 6 monop power (FP z' &bgr;) - monop power (C z &bgr;) The z-scores are linearly combined with weights such that the linear combination will maximize the separation between the two statistical groups: a group of aware people and a group of anesthetized unaware people. v) Calculation of the Probability of Correct Classification The probability that a set of z-scores can be correctly classified as belonging to an aware group, as opposed to an anesthetized unaware group, is obtained from the discriminant by using a sleep term, 14 S = c s + &Sum; i = 1 6 &it; w i ( S ) &it; z i 17 where c s is a constant term and the w's are discriminant weights. Similarly, the wake term is 15 W = c w + &Sum; i = 1 6 &it; w i ( W ) &it; z i 18 The probability of correct classification is 16 PCC = e W e W + e S 19 The PCC, calculated every 1.25 seconds, is a rigorously defined mathematical probability, and as such, varies between zero and one. 12) Observer Mediation Observation mediation logic 25 mediates between the different observers and indices to produce a final set of output parameters, including the PSI. a) The Initial PSI The initial PSI is the starting point for observer mediation logic and is simply a linear range expansion of the PCC by a factor of 100. b) Variability Transformation of the initial PSI The initial PSI 152 , also referred to as the PSI, undergoes a piecewise-linear transformation 153 that re-scales it according to the following formula: rPSI&equals; 1.7( oPSI )−70.0 oPSI&gE; 85.0 rPSI&equals; 0.7( oPSI )&plus;15.0 85.0 >oPSI> 15.0 rPSI&equals; 1.7( oPSI )15.0 >oPSI 20 The result is termed the rPSI. c) Mediation of Eye Blink Information Eye blink information is incorporated into the PSI 154 only prior to LOC, and only if the rPSI is greater than the LOC threshold of 50. An epoch is considered an eye blink epoch only if there are also no other types of artifact detected. If an eye blink epoch is detected during an Indeterminate Probability (i.e., before an rPSI can be calculated, because the raw measure buffer is not yet full) then an rPSI of 95 is reported. This rPSI and all succeeding rPSI values (until the buffer is full enough to calculate a value) are then treated as if they originated from a calculated probability, i.e., it undergoes the transformations and calculations that lead to the PSI. After this first eye blink, whenever an eye blink is detected, the current rPSI is averaged with 99, and the result becomes the current rPSI. d) Mediation of Suppression Information The new PSI that includes the information represented by the pSR is referred to as the nPSI. The nPSI is constructed as a function of the rPSI and the pSR, denoted as nPSI(pSR,rPSI) 144 . The transformation when pSR&equals;0, i.e., nPSI(0, rPSI), is an important special case of the whole transformation. Its result is a compression of the rPSI range (0-100) into a new scale. The new, compressed scale has a minimum value, nPSI bot , so that range of the new scale is (nPSI bot −100). The re-scaling is chosen so that a rPSI of 15 maps to a nPSI of 25. This completely determines the transformation equation, because the maxima of the two scales are the same. The transformation is given by 17 nPSI &af; ( 0 , &it; rPSI ) = 15 17 &it; rPSI + nPSI bot 21 where nPSI bot is given by 18 nPSI bot = 25 - 15 2 17 &TildeFullEqual; 11.7647 22 Another condition is the special case at pSR&equals;15, i.e., nPSI(15, rPSI). We impose the condition nPSI ( pSR&equals; 15 , rPSI&gE; 15)&equals;15   23 A third condition determining the transformation is case when pSR&gE;50. Then we have nPSI ( pSR&gE; 50 ,rPSI )&equals;0   24 e) Mediation of Beta5 Information Incorporation of EMG information into the PSI is based on the F 1 &bgr; 5 index 140 . This modification is a refinement of the EMG information already in the PSI by virtue of the F 1 &bgr; 2 term. EMG modifications are possible only when the pSR&equals;0, and only after a time threshold of 15 minutes after the declaration of Loss Of Consciousness (LOC). EMG modifications are not possible when the patient module is disconnected. In addition, there is a timeout period of &frac13; minute after the end of the PM Disconnect during which EMG modifications are not possible. When a BAD IMPEDANCE is detected, the EMG power used is the last one calculated before the BAD IMPEDANCE condition. The PSI that incorporates possible changes due to the F 1 &bgr; 5 index is called the tPSI. (The tPSI is a function of EMG and the nPSI.) When the PSI is modified by the F 1 &bgr; 5 index then the tPSI is considered a good data point and is painted non-white, even though the underlying PSI may have been artifacted and would have otherwise been painted white. The PSI is considered modified by EMG only when the change is greater than 1. The change from nPSI to tPSI is calculated using the following equation: we express the change as the product of three functions: &Dgr; PSI&equals;f ( F 1,&bgr;5) g ( nPSI ) h ( F 1,&bgr;5 , nPSI )   25 The first function governs the rise along the F 1 &bgr; 5 axis: functions: 19 f &af; ( F1β &it; &it; 5 ) = ( 1 1 + e - ( F1β &it; &it; 5 - B m ) / B w ) 26 The second function governs the rise along the nPSI axis: 20 g &af; ( nPSI ) = ( 1 1 + e - ( nPSI - P m ) / P w ) 27 The third function limits the change as nPSI becomes bigger, because there is a smaller remaining range of tPSI into which to change: 21 h &af; ( F1β &it; &it; 5 , nPSI ) = t c - t c n c + 100 - n c 1 + e - ( F1β &it; &it; 5 - b m ) / b w &it; nPSI 28 In each of the three functions there is a functional form F(x)&equals;(1&plus;exp((x&plus;c)/d)) −1 . This function creates a rising transition from 0 to 1, with the midpoint of the transition occurring at x&equals;c, and the width (or sharpness) of the transition determined by d. Thus the first of the three equations above defines the contribution of the FP 1 &bgr; 5 index as rising from zero around an index of approximately 1.25, and the second factor defines that the change can only occur be significant at nPSI values starting around a value of 19. The variable A in the equation above has a value of A&equals;1.25−baseline. The baseline in the beginning of the case is set to zero (the population baseline, since the EMG B5 z-scores used have been normalized to the population baseline). However the EMG B5 term is continuously monitored for conditions that will allow the setting of a new baseline. This allows the adaptation of EMG B5 modifications to individual patient differences. The adaptive baseline algorithm is described later in this section. The third factor in the equation embodies the idea that as the nPSI gets larger, there is a smaller and smaller remaining range into which the tPSI can change. The change is limited to only part of this remaining range. The maximum of this limiting part is defined by the last term which also has the functional form of f(x). The maximum is itself a rising function of the FP 1 &bgr; 5 index with a midpoint at a large value of 10.25 and a relatively large transition width of 3.0. This means that for most typical values of the FP 1 &bgr; 5 index, the change is limited to a maximum value of about 80. This maximum will increase as the FP 1 &bgr; 5 index increases. The tPSI is given by tPSI&equals;nPSI&plus;&Dgr;PSI 29 The EMG B5 baseline is adaptively determined as follows: The EMG B5 index over the past three minutes is stored in two windows, or buffers. The first holds the indices for the oldest two minutes (of the three minutes) and the second holds the most recent 1 minute (of the three minutes). For each of these windows, the averages and standard deviations are calculated at every update (every 1.25 seconds). Every update, the following conditions on the averages and standard deviations are checked: (F 1 &bgr; 5 ) 1 <A 1 30 &verbar;( F 1 &bgr; 5 ) 1 −( F 1 &bgr; 5 ) 2 &verbar;<D 31 &sgr; 2 <S 2 33 &sgr; 1 <S 1 32 If all four conditions are satisfied, then the adaptive baseline, L, is set to the average in the first window, A 1 : L&equals;A 1 34 The variable A in the equation above has a value of A&equals;1.25−baseline. The baseline in the beginning of the case is set to zero (the population baseline, since the EMG B5 z-scores used have been normalized to the population baseline). However the EMG B5 term is continuously monitored for conditions that will allow the setting of a new baseline. This allows the adaptation of EMG B5 modifications to individual patient differences. The adaptive baseline algorithm is described later in the section. f) Mediation of Artifact Information i) Repeated PSI The conditions for declaring a Repeated PSI are distinct from the conditions for Repeated Probabilities. A Repeated Probability is generated if artifacts make an FFT unavailable. However, several things can cause the PSI to vary even if the underlying probability is a repeated probability. First, eye blink information can modify the oPSI′. Secondly, the nPSI can be calculated from repeated probabilities. If the pSR happens to change during repeated probabilities, the nPSI will vary even if the underlying probability does not. Third, EMG modifications can also be active during repeated probabilities. The following conditions are used in the declaration of a Repeated PSI: if an epoch is an artifacted epoch, AND the PSI has NOT been modified by either eye blinks or EMG, AND the Artifact Index is greater than 30, then the tPSI is a Repeated PSI. “Repeated PSI” is merely terminology, carried over from “Repeated Probability”. It does not imply that the PSI is actually repeated, though in most cases it will be. ii) Artifacted PSI Artifacted PSI's are distinct from Repeated PSI's. The Artifacted PSI declaration is made on the 2.5-second PSI values. Repeated 1.25-second PSI's or repeated full-epoch PSI's are possible (through the rules in the previous sections). A 2.5-second PSI is declared an Artifacted PSI if the 2.5 second PSI is a repeated PSI AND the Artifact Index is greater than 30. iii) Trend PSI The Trend PSI is the running average of four 2.5 second PSI's, whether it is an Artifacted PSI or not. In the beginning of the case, the initial value of the running average is the population value of 95 for awake patients. The Trend PSI is the one of the four output parameters of the PSI 4000 Algorithm. 
 SYSTEM STATES AND SWITCHING As previously noted, the system operates in four distinct modes, each operating significantly differently. The four specifically defined states are as follows. 1) States a) Data Accumulation During Data Accumulation, the raw measure buffer of raw measure sets does has less than 24 overlapped-epochs of raw measure sets stored. EEG data acquisition is being performed, artifact analysis is being done, and FFT calculations are being made on good data. Each calculated raw measure set is added to the raw measure buffer. The following are NOT calculated: raw components, Z components, the PCC, and the PSI, and the EMG Index. The SR and the Artifact Index are calculated during this period. b) Awake State In Awake State, the raw measure buffer has 24 or more (up to 32) overlapped-epochs of raw measure sets stored. EEG data acquisition is being performed, artifact analysis is being done, and FFT calculations are being made on good data. If the raw measure buffer has 32 raw measure sets, the oldest raw measure set is thrown away before the most recently calculated measure set is added. During this mode, all four output parameters are calculated from non-artifacted data. The most notable feature of this mode is the incorporation of eye blink information from artifact analysis into the PSI. c) Unconscious State In Unconscious State, eye blink information is ignored and is NOT incorporated into the PSI. Otherwise, the operation of the Algorithm is the same as in Awake State. 1) Transition Events The following three transition events initiate a transition between the four states. a) Sufficient Data Accumulated The Algorithm determines that sufficient data has been accumulated after the raw measure buffer has accumulated 24 overlapped-epochs of raw measure sets calculated from FFT data. FFT data can only be calculated from non-artifacted epochs. Thus the time spent in this state is variable, depending on the amount of artifacts present in the data. b) Loss Of Consciousness If the PSI has 18 consecutive non-repeated values below 50, Loss Of Consciousness is declared. This declaration is used to disable the incorporation of eye blink information into the PSI.