Surgery – Diagnostic testing – Detecting brain electric signal
Reexamination Certificate
2002-10-08
2004-06-08
Marmor, Charles (Department: 3736)
Surgery
Diagnostic testing
Detecting brain electric signal
C600S300000, C600S509000, C600S546000
Reexamination Certificate
active
06748263
ABSTRACT:
The present invention relates to a method of monitoring electrical activity in an animal, especially human brain waves, and apparatus for carrying out the method such as an electroencephalograph.
It has been found that when a person is sedated, but not yet anaesthetised, their brain waves contain a frequency component which occurs between 8 and 12 Hz, and is known as the alpha rhythm. As sedation passes to full anaesthesia, the alpha rhythm disappears on termination of anaesthesia as the person returns to a sedated state, it reappears and then tends to disappear again when the person is fully awake.
It has been realised that this effect may be used to detect any undesired transition from anaesthesia to sedation, corresponding to the person beginning to regain consciousness, for example when a surgical operation is taking place. However, the emergence of the alpha rhythm, as anaesthesia passes to sedation, represents a small component in the total brain wave spectrum, and it has not proved possible using known methods to detect the gradual appearance of the alpha rhythm.
In addition, the occurrence of new frequencies lower than the alpha band such as delta, induced by the anaesthetic agent can be used to detect the undesirable presence of true anaesthesia if the intention is to maintain a state of sedation.
Known methods of analysing brain waves via electroencephalographs analyse the brain wave spectra using Fast Fourier Transforms. However, in detecting a weak frequency component, corresponding to the emerging alpha rhythm or low frequency delta rhythm induced by an anaesthetic agent, the use of a Fast Fourier Transform is unsuitable. There are two reasons for this. Firstly, noise in the brain wave signal is analysed by the Fast Fourier Transform as corresponding to many weak frequency components. It is thus not easy to distinguish between weak frequency components due to noise, and weak frequency component due to other reasons, such as the emergence of the new frequencies. Secondly, unless the frequency component being detected corresponds to one of the sampling frequencies of the Fast Fourier Transform, the Fast Fourier Transform will tend to split a frequency signal into a range of spurious frequency components.
The result of these two effects is that the Fast Fourier Transform tends to mask weak components. Hence, it is unsuitable for detecting the emergence of the alpha rhythm. By the time that the alpha rhythm for example is sufficiently significant to be detectable by Fast Fourier Transform, the person will have passed from anaesthesia to sedation, so that it is not possible in this way to carry out early detection of that transition.
Therefore, the present invention seeks to provide an apparatus and a method, of analysing brain waves which permits these rhythms to be detected when they are very weak. This then permits an indication of the anaesthesia or sedation level to be determined. However, as will be explained below, the present invention is not limited to detection of alpha and lower rhythms and could be used to detect other components such as epileptic spikes in the brain wave signal.
According to the present invention, electrical activity is detected and produces a corresponding output signal, the output signal is combined with a random noise signal to produce a modified signal, and the modified signal is analysed using an autocorrelation technique to detect the relative power density values at a plurality of different frequencies.
Preferably, the autocorrelation technique involves use of the Yule-Walker algorithm.
The value of one or more power density values at a frequency or frequencies corresponding to a specific rhythm such as the alpha or delta is then compared with the sum of the power density values over a wider range of frequencies. The result of this comparison gives a measure which may be used to detect the emergence of these rhythms. To express this in another way, the relative power density D
f
at various frequency f are derived using Equation 1 below, for a multiplicity of frequencies f.
D
f
=
1
|
1
+
∑
p
=
1
M
y
p
exp
(
-
i
·
a
·
f
·
p
)
|
2
Equation
1
where y
p
is the pth Yule-Walker coefficient, and a is a constant.
Then, the ratio of the sum of one or more values of D
f
at or about the frequencies of the particular rhythms are compared with the sum of the values of D
f
over a wider range of values, and the changes in that ratio may be used to detect the emergence of these rhythms.
In general, the maximum frequency of the wider range will be at least approximately double that of the maximum frequencies of the rhythms under consideration.
It should be noted that Yule-Walker methods from which the Yule-Walker coefficients referred to in Equation 1 above are obtained, are a known type of frequency analysis method. For a detailed discussion of Yule-Walker methods, reference may be made to the book “Digital Signal Processing” (second edition) by J G Proakis and D G Manolakis published by McMillan publishing company, New York.
The present invention also consists in an electroencephalograph which monitors brain waves using the method discussed above, to indicate the emergence of specific rhythms, and also consists in a method of operation such as an electroencephalograph.
In order to derive the Yule-Walker coefficients referred to above, the present invention further proposes that a series of autocorrelation products be derived from the brain wave signals. These autocorrelation products may then be used directly, to derive the Yule-Walker coefficients, but it is preferable that an averaging technique is applied to them. It would be possible to determine the autocorrelation direct over a relatively long time period, but it is preferable to use a shorter time period and average over those time periods. The advantage of this is that short bursts of noise are then not carried over from one period to the next. Averaging in this way has the disadvantage of slowing detection of trends, and therefore there is the need to compromise between these factors.
In deriving the autocorrelation products, it has been found advantageous to add random linear noise to the brain wave signals. Provided that the amount of random linear noise added is not too great. the reduction in spectral resolution which results is not of practical consequence. However, it has been found that the addition of such random linear noise tends to reduce or prevent the occurrence of occasional rogue results. It is also preferable that any DC components of the brain wave signals be removed, to counteract the effect of drift.
In order to carry out the analysis of the brain waves as discussed above, an electroencephalograph according to the present invention preferably converts the brain wave signals to digital signals, to enable those signals to be analysed by a suitably programmed processor. The analysis of the relative power density values may then be used to generate a suitable display and/or audible signal, and/or a control signal for other equipment. In fact, it is preferable that the value corresponding to the comparison of relative power densities discussed above is converted to an index value which is a non-linear function of the initial value, to emphasise changes at low values of the specific rhythm.
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Griffiths Mark James
Preece Alan W
Mallari Patricia C.
Marmor Charles
Reising Ethington Barnes Kisselle P.C.
The University of Bristol
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