Surgery – Diagnostic testing – Detecting brain electric signal
Patent
1998-04-17
2000-01-04
Kamm, William E.
Surgery
Diagnostic testing
Detecting brain electric signal
A61B 50476
Patent
active
06011990&
DESCRIPTION:
BRIEF SUMMARY
The invention concerns a method for evaluating an EEG carried out in the context of anaesthesia or intensive care according to the precharacterizing part of claim 1 and a device for evaluating an EEG carried out in the context of anaesthesia or intensive care according to the precharacterizing part of claim 5.
Electroencephalography is a method of representing electrical activity generated by the brain. With the conventional way, registration of the EEG is by a multi-channel recorder onto reel paper, as a rule at a registration speed of 30 mm/s. The German EEG Society recommends the use of devices with at least ten channels. In order to be able to properly assess the signals from the various regions of the head, the electrodes are interconnected in various ways in several derivation programs. Increasingly, recording is also computer-assisted.
The composition of the wave shapes in the electroencephalogram (EEG) depends on the functional state of the brain. The EEG diagrams of patients in surgical and intensive-care areas are varied; they can be influenced by a large number of endogenous and exogenous factors. Apart from the normal waking state EEG, the following for example have to be expected: elements of the sleep EEG, effects of medication and other exogenously admitted chemical substances, influences due to ventilation and metabolism, temperature effects, consequences of traumatic brain lesions as well as inflammable, vascular, degenerative EEG alterations and EEG alterations caused by neoplasms.
The waves occurring in the EEG are allocated to the following frequency domains: alpha (7.5-12.5 Hz), beta (>12.5 Hz), theta (3.5-7.5 Hz) and delta (0.5-3.5 Hz).
Apart from these, the subdelta band (<0.5 Hz) and the gamma band (>30 Hz) can be delimited. During the findings, the waves in the frequency domains are described concerning their amplitude, frequency, regularity, temporal structure, local distribution and change during stimulation. EEG amplitudes are measured in .mu.V. As a rule, waves of higher frequencies have smaller amplitudes whereas slowing down is usually accompanied by an increase in amplitude.
To classify stages of sleep EEG, anaesthesia EEG or coma EEG, Kugler proposes an EEG division in which the waking state is described by A, and EEG images with progressive depression of cerebral function are described by the letters B to F. In order to assess the EEG curves, the frequency and amplitude of the waves in certain frequency domains, as well as typical patterns, are used.
With the majority of adults, the waking EEG, stage A, is characterized by waves in the alpha frequency domain. Stage B is characterized by waves of faster frequency and lower amplitude. In the stages C or D, theta waves and delta waves occur. In stage E, the curve is characterized by high-amplitude delta activity.
Stage F is characterized by a change between flat to isoelectric curve sections and groups of higher waves; by the burst-suppression sample; or by activity which is continuously very flat.
The derivation of a conventional EEG is relatively demanding. Interpretation requires special knowledge and experience. A better judgment of the dynamically progressing EEG changes is possible by recording the original signal, and by applying EEG spectrum analysis. In order to calculate an EEG power spectrum, after analog-digital conversion, the EEG signals are subjected to a Fast Fourier Transformation (FFT) for a defined time period. By means of the Fourier transformation, the wave diagram of the EEG is divided into underlying oscillation components; there is a conversion from the temporal to the frequency domain. The squared amplitudes of the oscillation components constitute the power spectrum. The frequencies occurring in the time signal can be read from the EEG power spectrum. But this information too, requires interpretation in order to gain an insight into the EEG stage and thus into the stage of cerebral function.
In order to obtain automatic pattern recognition, it is known from the journal "Biomedizinische Techn
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Biomedizinische Technik, 37, vol. 6, 1992, pp. 122-130. "Sleep Classification in Infants . . . ".
"Identification of EEG Patterns Occurring in Anesthesia by Means of Autoregressive Parameters", 1991, Biomedizinische Technik, vol. 36, pp. 236-240.
Schultz Arthur
Schultz Barbara
Arthur Schultz
Kamm William E.
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