Intraoperative monitoring or EP evaluation system utilizing an automatic adaptive self-optimizing digital comb filter

An electroencephalograph (EEG) system for the detection of a patient's brain waves includes a group of electrodes removably attached to the patient's scalp, low-noise high-gain amplifiers and analog/digital converters. A digital comb filter is used to improve the signal-to-noise ratio and has self-optimizing capability. In the digital comb filter, the band pass frequencies are automatically selected by comparing, at each tooth of the comb, the phase variance parameter, under the brain conditions of the presence and absence of a synchronized stimulus producing an evoked potential brain wave.

BACKGROUND OF THE INVENTION 
The present invention relates to medical devices and more particularly to 
an improvement in EEG (electroencephalograph) devices used for evoked 
potential (EP) analysis. EP's are transient oscillations of the EEG which 
are time locked to the presentation of sensory stimulation. The waveshape 
of this oscillation, imbedded in the EEG, reflects the processing of that 
stimulus information by the brain. Successive components of the EP 
waveshapes represent the passage of incoming neuronal activity along 
particular anatomical pathways. For this reason, precise knowledge about 
EP waveshapes can provide a sensitive index of the functional status of 
neuroanatomical structures. In order to utilize such information most 
advantageously, for example, to monitor the condition of certain brain 
regions during neurosurgery, the EP waveshape must be separated and 
extracted from the other electrical activity in which it is embedded as 
quickly and as cleanly as possible. Because EP waveshapes are complex and 
vary depending upon stimulus parameters, neural condition and 
characteristic of individual patients, it is also advantageous to obtain 
objective criteria for evaluation of changes in EP waveshape during a 
critical period of observation, such as during an operation on the brain. 
At times, it is desirable to obtain such EP's from as many as 19 
electrodes simultaneously, to compare the response in different kinds of 
stimuli sequentially in order to evaluate different neuroanatomical 
pathways. A major problem in such analyses is the poor signal-to-noise 
ratio. 
One method that is used to improve the signal/noise ratio of evoked 
potentials is signal averaging. In evoked potential (EP)analysis, a large 
number of stimuli, such as light flashes or auditory clicks, are presented 
to the patient in a regular pattern, for example, 2048 auditory clicks at 
repetition rates about 7-10/second. The brain response, for example, the 
brain stem auditory evoked potential (BAEP), is in synch with the stimuli, 
but the noise is random. When the responses are averaged, the noise tends 
to cancel itself out, leaving an improved signal/noise ratio. This 
improvement is proportional to the square root of the sample size. Because 
the time required to achieve useful improvement of S/N is so long relative 
to the time frame of intraoperative events, conventional signal averaging 
is poorly suited for surgical monitoring. Further, significant 
fluctuations in the functional status of brain regions may occur during 
the long period required to accumulate a sufficiently large sample and the 
corresponding heterogeneous waveshapes are obscured by combination within 
the average EP finally obtained. 
Even with signal averaging, the signal/noise ratio may not be sufficient 
for reproducible results, under some circumstances, as suggested in an 
article by Drs. E.R. John, H. Baird, J. Friedman and M. Bergelson entitled 
"Normative Values For Brain Stem Auditory Evoked Potentials Obtained By 
Digital Filtering And Automatic Peak Detection", Electroencephalography 
and Clinical Neurophysiology 1982, 54:153-160 (1982, Elsevier Sci. Pub. 
0013-4949). Further improvement of the signal/noise ratio by increasing 
the sample size is prohibitively time-consuming. 
An article entitled "Application of Digital Filtering and Automatic Peak 
Detection to Brain Stem Auditory Evoked Potential", Friedman, John, 
Bergelson, Kaiser, Baird; Electroencephalography and Clinical 
Neurophysiology, 1982, describes a way to achieve rapid improvement of S/N 
by using a digital bandpass filter with optimal bandwidth to suppress some 
noise components. This article describes an analysis of averaged brain 
stem auditory evoked responses. The same method can be applied to any type 
of EP. Repeated samples of signal (presence of stimulus) and noise 
(absence of stimulus) were subjected to FFT (Fast Fourier Transform). At 
each frequency, the variance of phase was computed separately for the sets 
of signal samples and noise samples. The optimal frequency band, for 
digital filtering, was obtained by comparing phase variance (as a function 
of frequency) in the absence of stimulus against phase variance in the 
presence of stimulus. Phase variance is low at frequencies which 
contribute to the waveshape of the evoked potential (which is phase-locked 
to the stimulus), while the phase variance of noise components is high 
because of its random composition. The optimal digital filter is defined 
as the frequency band within which the phase variance is lowest for 
samples of signal and highest for samples of noise. Once the filter 
("filter window") has been selected, subsequent samples of signal are 
decomposed by FFT (Fast Fourier Transform) and an Inverse Fast Fourier 
Transform (IFFT) is then performed using only the terms inside the 
selected filter window. The signal which has been decomposed into its 
spectral components by FFT is thus reconstructed with the noise 
selectively removed. In contrast to conventional signal averaging methods, 
in which noise is reduced by summation of random variations, optimal 
digital filtering is selective removal of noise. It should be noted, 
however, that some noise components generated by the stimulator of the EP 
apparatus or reflecting high harmonics of other apparatus may have 
relatively low variance within the selected frequency domain. 
In order to use this technique, the frequency "window" (band pass of 
frequencies) of the optimum filter was selected, using visual inspection 
by the operator, based on his visual reading of the phase variance 
diagrams and his experience. Those frequencies below and above the 
selected band were canceled (band rejection). The operator, if experience 
and careful, was able to select a frequency band that would improve the 
signal/noise ratio by selecting a band pass in which the signal was 
relatively strong compared to the noise. 
That system has two major shortcomings. The first is that it requires the 
operator to visually inspect the phase diagram to select the optimal 
filter bandwidth (frequency window) so that the system relies upon the 
judmgent, skill and attention of a human operator. However, the time of 
such skilled operators is expensive, such a person may not be available 
during every surgical operation, and the person's judgement and attention 
may be less than perfect at times, especially during a prolonged 
operation. 
The second problem is that, even if the band pass is correctly selected, it 
does not eliminate noise which is in phase synchronism with the signal. 
Such phase synchronous noise may lie within the optimal band pass. For 
example, components of stationary noise, such as from the harmonics of 60 
Hz from operating room instruments or the evoked potential apparatus 
itself, may be in relatively stable phase and at the same frequency as 
components of the brain wave signal that it is desired to detect. Such 
synchronous noise is reincorporated into the signal, instead of being 
reduced by the filtering and averaging process. 
OBJECTIVES AND FEATURES OF THE INVENTION 
It is an objective of the present invention to provide an EP system in 
which faint evoked electrical activity embedded in a patient's brain waves 
may be more accurately detected and analyzed by improving th.e ratio of 
signal to noise. 
It is a further objective of the present invention to provide such a system 
in which noise which is synchronous with the patient's evoked potentials 
and within the general range of frequencies of interest may be reduced. 
It is a further objective of the present invention to provide such a system 
in which, automatically and without operator intervention, the "teeth", 
i.e., the band pass sub-frequencies, are selected to construct a 
self-optimizing digital comb filter in which some of the sub-frequencies 
within the range of the filter are band-stopped. 
It is a feature of the present invention to provide an 
electroencephalograph (EEG) system for the detection of a patient's brain 
waves. The system includes a series of electrodes, for example, the 19 
electrodes of the International 10/20 System, adapted to be connected to 
different sectors of the patient's head. Analog signals representing the 
patient's brain waves are detected by the electrodes and amplified by 
amplifier means connected to the electrodes. The amplifier means includes 
low-noise high-gain amplifiers. The system also includes means for 
generating visual, auditory or somatosensory electrical stimuli and 
analog/digital conversion means connected to the amplifiers which produce 
synchronous sets of digital data corresponding to the patient's brain 
waves, time-locked to the stimuli (evoked potentials). 
A digital comb filter means is connected to the aanlog/digital conversion 
means to improve the signal-to-noise ratio of the digital data. Digital 
data analysis means is connected to the comb filter to analyze the data 
and produce statistically analyzed data showing abnormalities and 
normalities in the brain waves relative to population norms or reference 
data obtained from the patient at some earlier time, preferably under 
anesthesia but prior to the surgical intervention. That analyzed data is 
shown in display means, such as a CRT monitor or a printer. 
The digital comb filter has self-optimizing means to automatically select 
the band-pass frequencies constituting the teeth of the optimal comb 
filter. That selection is based on a comparison, at each tooth, of the 
digital data in the presence and absence of a synchronized stimulus 
producing an evoked potential brain wave.

DETAILED DESCRIPTION OF THE INVENTION 
As shown in FIG. 1, the patient (subject) is positioned so that his head 10 
is connected with the desired number of electrodes 11a-11h. The drawing, 
for simplicity, shows only three pairs of electrodes 11a, 11b and 11h. 
Alternatively, and not shown, 19 electrodes may be arranged so that the 
conventional EEG International 10/20 electrode system and nomenclature may 
be employed. Alternatively, when surgical conditions restrict access to 
some regions of the head, one active electrode may be located at the 
vertex or on the forehead and reference electrodes on one or both mastoid 
processes, behind the ears. 
The electrode pairs 11a-11h are connected to respective differential 
amplifiers 12a-12h, each pair of electrode leads being connected to its 
own amplifier. Each amplifier 12a-12h has an input isolation switch, such 
as a photo-diode and LED coupler, to prevent current leakage to the 
patient. The amplifiers 12a-12h are high-gain low-noise amplifiers, for 
example, having a frequency range of 0.5 to 5000 Hz, gain of 100,000 
common mode rejection of 100 dB and noise of less than 2 microvolts 
peak-to-peak. 
The amplifiers 12a-12h are connected to analog-to-digital multiplexer 13 
(A/D multiplexer). The multiplexer 13 samples the amplified analog brain 
waves at a rate compatible with the bandwidth of the amplifiers, for 
example, at 100,000 per second with 12-bit resolution. The multiplexer 13 
provides, at its output, sets of digital data, representing the EEG analog 
signal. The multiplexer 13 is connected to "buffer signal" 25a, which 
stores the signal and "buffer noise" 25b, which stores the noise. The 
buffers 25a, 25b are connected, and A/D multiplexer is directly connected, 
to the dedicated microprocessor 15. The dedicated microprocessor 15 is 
connected through its dedicated 512-point FFT 16a (Fast Fourier Transform) 
to digital comb filter 14, which is described in detail below. 
Alternatively, as shown in FIG. 6, the system microprocessor 17 may be 
used to control the comb filter 14. 
The comb filter is connected to, and controls, the IFFT 16b (Inverse Fast 
Fourier Transform). The output of IFFT 16b is connected to the system 
microprocessor 17 which is connected to the stimulus devices 19 (lights, 
loudspeaker, shock device, etc.), to the system digital storage buffers 
20a-20n (only two being shown), to the mass storage 21, such as a laser 
device storage or hard disk, to the display 22, such as a CRT and a matrix 
print-out recorder and to the control panel 23. Details of these devices 
will be found in the above-referenced patents and articles. 
The conventional type of digital filter provides a broad band response, as 
shown in FIG. 2, in which the frequency response varies successively from 
zero to a uniform maximum to zero, see Bogner, Introduction to Digital 
Filtering, pages 143-144 (Wiley, 1975). 
FIG. 3a shows the phase synchrony of a typical signal sample having peaks 
1, 2 and 3; and FIG. 3b shows the phase synchrony of a typical sample of 
noise. The "optimum" band filter of the prior art would be as shown in 
FIG. 2. 
The comb filter of the present invention, shown in FIGS. 4 and 5, may be 
considered a series of band pass and band stop filters arranged to be 
responsive over a selected range. As shown in FIG. 4, the selected range 
is 0-1400 Hz and there are band pass filters at 100-580 Hz, 600-640 Hz and 
720-800 Hz and 900-1400 Hz and band-stop filters at 0-100 Hz, 580-600 Hz, 
640-720 Hz, 800-900 Hz and above 1400 Hz. The bandpass filters are the 
"teeth" of the comb and they are selected so as to accord with the 
frequencies in which the signal/noise ratio is acceptable. The band-stop 
filters are selected to be at frequencies in which the noise is excessive. 
The multiplexer is programmed by programmer 24, which may be obtained from 
a floppy disk, to obtain samples of the signal and of the noise. The noise 
is preferably obtained when there is an absence of evoked potential 
stimuli and the signal is obtained during epochs up to 500 milliseconds 
long, beginning with presentation of the stimuli or after a pre-selected 
delay. 
The noise and signal samples are transformed to their Fourier equivalents 
(from time domain to phase domain) by Fast Fourier Transform FFT 16a to 
produce: a.sub.i +SIN.sub.(frequencyi) +COS (phase.sub.i). 
The "phase variance parameter" is computed separately for the signal 
samples and for the noise samples The "F ratio" is then computed by 
microprocessor 15 for each narrow frequency band ("subfrequency band") 
obtained in the FFT. The "phase variance parameter is defined as follows: 
Phase variance parameter (VAR) (.psi. i) is calculated from the following 
formula (the formula gives the phase variance parameter for the ith 
component of the spectrum). 
##EQU1## 
H.sub.ih =spectral component of the j.sup.th trial (complex value) 
The "F" ratio is defined as the ratio of "phase-variance with stimulus: 
phase-variance without stimulus" and corresponds to the F-ratio used in 
the analysis of variance (ANOVA). 
The formula is as follows: 
EQU F=VAR (.psi. i): VAR (.psi. i') 
where the I frequency component is with stimuli and the i' frequency 
component is without stimuli. 
If the F ratio is high, there is a high synchronization of the spectral 
component with the beginning of the sample in the presence but not in the 
absence of stimulation, and the signal-to-noise ratio is high. Conversely, 
if the F ratio is low, there is no significant difference in phase 
variance whether signal is present or absent; the signal-to-noise ratio is 
poor. For example, in the case of an EEG instrument the "with stimulus" 
may be the brain wave signal evoked response (ER) obtained with the 
presence of a stimulus and "without stimulus" is the brain wave signal 
without such stimulus. The F ratio is evaluated by microprocessor 15 under 
control of program 14 at each narrow frequency band (subfrequency band). A 
threshold is set by the program 14, which has been selected so as to be 
completely automatic in operation. Alternatively, the threshold may be set 
by the operator by means of the control panel 23. 
If the F ratio is below the threshold at a given narrow frequency band, 
that narrow frequency band is excluded, by microprocessor 15, from the 
inverse transform. If the threshold is exceeded, that frequency is 
included in the inverse transform, as shown in FIG. 4. 
As an alternative, as shown in FIG. 5, it may be advantageous to weight the 
contribution of each accepted "tooth" (narrow band pass) proportional to 
the value of the F ratio when the inverse transform is performed. The 
total used signal is a combination of the weighted frequency components 
represented by all of the accepted teeth of the comb filter, as shown in 
FIG. 6. 
It may, or may not, be advantageous to disqualify from acceptance as a 
tooth any frequency component for which the signal contains less than a 
predetermined percentage of the total signal energy or for which the noise 
contains more than a predetermined percentage of the total noise energy. 
Such advantage might be present in various applications of this system to 
the processing of other types of information, such as sonar, radar, radio 
or video signals. 
In practice, a period of filter optimization would usually precede the 
beginning of the surgical procedure, but after anesthesia. The following 
"self-norm" is envisaged: 
(1) Multiple small baseline samples of brain activity would be collected, 
consisting of brief segments beginning at the time of stimulus onset or 
after a preselected delay. The elements in each such sample might be 
combined into a "light average" of size N (SIGNAL SAMPLES). 
(2) An equal number of baseline "light averages" of size N would be 
collected in the absence of stimulation (NOISE SAMPLES). 
(3) FFT would be performed separately on each signal sample, and the phase 
synchrony (1-phase variance) computed as a function of frequency (SIGNAL 
PHASE SYNCHRONY). 
(4) FFT would be performed separately on each noise sample, the phase 
synchrony computed as a function of frequency (NOISE PHASE SYNCHRONY). 
(5) The SIGNAL PHASE SYNCHRONY AND NOISE PHASE SYNCHRONY would be displayed 
one above the other on the CRT display 22. 
(6) The "optimum F-ratio comb filter" (Version 1 see FIG. 4) or "modulated 
F-ratio comb filter (Version 2 see FIG. 5) would be displayed on display 
22 below these, together with the selected threshold. 
(7) The unfiltered averaged signal, the signal filtered by 
operator-selected band, optimal F-ratio comb and/or modulated F-ratio comb 
are displayed on display 22, aligned vertically one above the other for 
final approval, if a qualified operator is present. If not, the 
automatically selected filter would be used. 
(8) Once the filter is selected, whether by operator choice or 
automatically, all baseline SIGNAL samples and NOISE samples would be 
separately passed through the filter, i.e., the appropriate IFFT would be 
performed. 
(9) Using an automatic peak detector, all maxima (positive peaks) and 
minima (negative peaks) exceeding the rms mean amplitude of the filtered 
noise by some threshold, preferably two standard deviations of the rms 
noise, would be located. 
(10) The mean and standard deviation of amplitude and latency would be 
computed to derive the confidence interval signifying the limits of peak 
variability expected in subsequent SIGNAL samples. If reliable confidence 
intervals cannot be found with single "light averages" (each of size N), 
multiples of 2, 4, 8, etc. light averages should be averaged together and 
the confidence interval assessed at each successively larger sample size. 
A "reliable confidence interval" for a sample of n light averages is 
defined as one for which a peak within some permitted latency range is 
detected in each of the initial set of SIGNAL samples. This can be done 
automatically or with operator supervision if available. 
(11) Once the optimal filter has been defined and the sample size 
ascertained for which a reliable confidence interval can be defined for 
the peak or peaks critical for the purposes of a particular operation, 
routine monitoring of the EP can begin. Each sample is subjected to FFT, 
followed by the IFFT defined by the optimum filter, and then subjected to 
peak detection. Deviation of peak amplitude (AMP) and latency (LAT) are 
expressed in microvolts and microseconds from the self-norm, or are 
Z-transformed relative to baseline statistics to permit objective 
evaluation. Multivariate Z values (.sqroot.Z.sup.2.sub.AMP 
+Z.sup.2.sub.LAT)1/2 can also be computed. These values are presented in 
appropriate high-lighted form on the CRT 22 or used for an automatic 
reporting means to inform the operating room personnel by appropriate 
visual or auditory signals. Differences between peak latencies or 
amplitudes may also be used for reporting. 
(12) As surgical maneuvers or anesthesia changes cause improvement or 
deterioration in neural condition, the strength and latency of EP 
components and their variance may alter Similarly, as operating room 
apparatus is switched on and off or moved about, environmental noise may 
change. Ideally, the monitoring apparatus should be coupled to blood 
pressure, heart rate, and anesthetic level sensing apparatus and should 
report correlated sources of EP variations. Thus, the true optimum filter 
may vary repeatedly during the course of an operation, no matter how it is 
defined. 
For this reason, it may be desirable to continuously redefine the optimum 
filter. An adaptive optimum filter, automatically redefining itself, can 
be achieved within the present framework by: 
(a) Updating the set of SIGNAL samples used to compute SIGNAL PHASE 
SYNCHRONY (SPS) by dropping out the oldest sample, adding the newest 
sample and recomputing SPS. 
(b) Similarly, at regular intervals (perhaps but not necessarily in 
alternate time periods), the set of NOISE samples can similarly be updated 
and NOISE PHASE SYNCHRONY (NPS) recomputed. 
(c) Using these sliding window estimates of SPS and NPS, the F-ratio as a 
function of frequency can be continuously computed and an adaptive optimum 
comb or F-ratio modulated filter continuously redefined and applied to 
compute new confidence intervals and to evaluate the next SIGNAL sample. 
(d) Means must be provided, should such adaptive filtering be adopted, to 
protect against gradual steady deterioration by periodic checks against 
the initial baseline and notification by appropriate means, when such 
gradual changes have caused significant deviations from the initial 
baseline. Such a check might be provided by t-tests for the difference 
between the baseline statistics and the current values for the means and 
standard deviations of peak latencies and amplitudes. 
The above description has been in connection with an electroencephalograph 
system and method for the detection of a patient's brain waves. However, a 
system and method utilizing the digital comb filter may be applicable to a 
radar (radio detection and ranging) system in which the comb filter may be 
applied in connection with the local oscillator signal.