Surgery – Diagnostic testing – Detecting brain electric signal
Reexamination Certificate
2002-01-17
2003-01-14
Schaetzle, Kennedy (Department: 3762)
Surgery
Diagnostic testing
Detecting brain electric signal
Reexamination Certificate
active
06507754
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to a method and a device for the medical monitoring, in real time, of a patient from the analysis of electroencephalograms, an application of this method to characterize and to differentiate between physiological and pathological conditions, and a method for anticipating epileptic seizures in real time.
The invention has many application fields and notably the anticipating of epileptic seizures in real time. In the following we will consider, as a specific case, how such a method applied in the epilepsy domain.
A characteristic feature of epilepsy is the spontaneous occurrence of seizures, most often without warning and for no apparent reason. The unpredictability of seizure onset is the most important cause of morbidity for persons with epilepsy.
Epilepsy, one of the most common neurological afflictions of children and adults (1% of the population), is the consequence of a neuronal disorder which is expressed by recurrent paroxysmal discharges of the cerebral cortex. Clinically, this translates into the sudden occurrence of symptoms of a seizure. This sudden emergence is difficult to interpret as a response to an external triggering factor, which is absent in most situations, with the exception of the rare reflex epilepsies. The transition from the condition referred to as the “intercritical condition” to the critical condition (the seizure) is one of the fundamental phenomena of epilepsy and this sporadic occurrence appears as the unforeseeable product of autonomous ordering within the brain.
A reliable anticipation of a seizure several minutes ahead would provide a window of time during which automated warning or a therapeutic intervention could be undertaken to minimize risk of injury and perhaps abort the seizure.
However, it is notoriously difficult to predict the seizure onset more than a few seconds in advance from a visual inspection or a traditional signal analysis of the electroencephalograms or EEG.
Up to now, no traditional method (linear analysis) has enabled one to anticipate this seizure condition to a significant degree. Very recently, an article (reference [1] at the end of the description) has demonstrated that under certain conditions, it is possible to anticipate seizures by several minutes with the help of new strategies arising from the theory of systems dynamics. The methods of non-linear dynamics are derived from mathematics known under the generic name of “Chaos Theory”. They enable one to show how, behind an electroencephalogram signal that is random in appearance, precise laws and determinants can be hidden.
Such a procedure, characterizing in an irrefutable manner the route towards the seizure but requiring long calculation times, was not at all compatible with real time application using devices of the known art.
Other studies have confirmed the usefulness of nonlinear measures for the detection of pre-ictal changes (references [2] and [3] at the end of the description). Although these results are very promising, further studies are necessary to allow an early and accurate anticipation of seizure. In particular, most of the studies have been conceived from the perspective of a “decrease in complexity” in the pre-seizure brain activity. According to this hypothesis, estimations of non-linear invariants, such as the correlation dimension or the largest Lyapunov exponent, are specifically used to characterize transitions from a high- to low-dimensional dynamics. However, these applications to EEG recordings meet some difficulties, first, the link between the changing profile of the EEG and the dimension of an underlying attractor remains problematic. Second, EEG recordings are not stationary over periods of sufficient length to permit a reliable estimation of these nonlinear quantities. Third, the computational effort for the estimation of these parameters restricts the ability to develop a rapid analysis over a long time scale.
In order to overcome these limitations, it is an object of the invention to propose a new nonlinear strategy adapted for carrying out medical monitoring of a patient, in real time, from the analysis of electro-encephalograms.
It is another object of the invention to allow the detection in advance (several minutes in advance) of a developing epileptic seizure with sufficient reliability and to supply a warning signal necessary to permit prevention or therapeutic intervention.
SUMMARY OF THE INVENTION
The invention relates to a method for the medical monitoring in real time of a patient from the analysis of electroencephalograms comprising the steps of:
constructing a reference dynamics of a normal state in choosing a long EEG segment S
ref
recorded during an interval quite distant in time from any problem,
comparing this reference dynamic with the dynamics of distant test segments S
t
,
computing the similarities over the entire EEG recordings by sliding the test segment S
t
periodically, the corresponding time course providing information about long term changes before an onset.
Advantageously constructing a reference dynamics is derived from the sequences of time intervals between positive-going crossings of a fixed threshold, delay vectors defining an m-dimensional embedding of the dynamics being formed from this sequence of intervals. A single value decomposition of the m-dimensional embedding space is applied, identifying the optimal space that contains the trajectory.
Advantageously the recording is split into non-overlapping consecutive test segments of 25 seconds each. A basic “skeleton” of reference dynamics is considered, said skeleton being built by a random selection of a sub-set of points, providing an adapted picture X(S
ref
) of the reconstruction, extracting the most frequent occupations of the phase space flow. The dynamical similarities are estimated between the reference dynamics X(S
ref
) and the projection X(S
t
) of a 16-dimensional reconstruction of the test segment S
t
on the principal axes of the reference dynamics. Dynamic similarities are estimated in using statistical measure based on a cross-correlation integral. A cross-correlation ratio is used, providing a sensitive measure of closeness between two dynamics.
The method of the invention allows one easily to characterize and differentiate between physiological or pathological conditions and is applicable in other application fields such as:
sleep: differentiation between different stages of sleep,
anesthesia: characterization of stages of sleeping under anesthesia with an automatic control of the regulation of the injected substance,
depression: with electro-physiological monitoring, a depressive illness and the characterization of its features and conditions and consequently the adjustment of its treatment.
The invention further relates to a device for the medical monitoring in real time of a patient from the analysis of electroencephalograms or EEG, comprising an amplifier receiving the EEG signals, an analogic/digital multiconverter and a processor delivering an external output and an output warning.
Advantageously said device is a free-standing device, light and portable by the patient. To enable patients to be totally self-sufficient, advantageously said device is miniaturized so that it can be implanted sub-cutaneously, like a stimulator.
Moreover the invention relates to a method for anticipating epileptic seizures in real time comprising the steps of
constructing a reference dynamics of the non-seizure state in choosing a long EEG segment S
ref
recorded during an interval quite distant in time from any seizure,
comparing this reference dynamic with the dynamics of distant test segments S
t
,
computing the similarities over the entire EEG recordings by sliding the test segment S
t
periodically, the corresponding time course providing information about long term changes before a seizure onset.
Advantageously constructing a reference dynamics is derived from the sequences of time intervals between positive-going crossings of a fixed threshold, delay vectors
Baulac Michel
Le Van Quyen Michel
Martinerie Jacques
Varela Francisco
Centre National de la Recherche Scientifique
Droesch Kristn
Oblon & Spivak, McClelland, Maier & Neustadt P.C.
Schaetzle Kennedy
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