Method and apparatus for automatic seizure monitoring
Method, apparatus and computer program product for monitoring seizure activity in brain are disclosed. At least one parameter set sequence is derived from brain wave signal data obtained from a subject, wherein each parameter set sequence comprises sequential parameter sets and each parameter set comprises values for at least two signal parameters, the values being derived from the brain wave signal data. A path formed by each of the at least one parameter set sequence in a parameter space is determined, thereby to obtain at least one path. The parameter space is defined by the at least two signal parameters. At least one evolution indicator is calculated, each evolution indicator quantifying the evolution occurred in respective path formed in a given time period in the parameter space. The at least one evolution indicator is then employed to produce an indication of seizure activity in the brain wave signal data.
1. A method for monitoring seizure activity in brain, the method comprising:
acquiring brain wave signal data from a subject
deriving at least one parameter set sequence from the brain wave signal data obtained from the subject, wherein each parameter set sequence comprises sequential parameter sets and each parameter set comprises values for two signal parameters comprising a first signal parameter and a second signal parameter, wherein the first signal parameter is a single value parameter indicative of the current frequency content of the brain wave signal data and the second signal parameter is a single value parameter indicative of the current amplitude of the brain wave signal data;
determining a path formed by each of the at least one parameter set sequence in a parameter space wherein the parameter space is a two-dimensional coordinate system, and wherein a first axis represents values of the first parameter and a second axis represents values of the second parameter, thereby to obtain at least one path;
calculating at least one evolution indicator, wherein each evolution indicator quantifies evolution occurred in the respective path formed in a given time period in the parameter space; and
employing the at least one evolution indicator to produce an indication of seizure activity in the brain wave signal data.
2. The method according to claim 1 , wherein
the acquiring includes acquiring a plurality of brain wave signal channels from the subject, in which the brain wave signal data belongs to the plurality of brain wave signal channels,
the deriving includes deriving the parameter set sequence for each of the plurality of brain wave signal channels, thereby to obtain a corresponding plurality of parameter set sequences;
the determining includes determining a path for each of the plurality of parameter set sequences, thereby to obtain a corresponding plurality of paths;
the calculating includes calculating an evolution indicator for each of the plurality of paths, thereby to obtain a corresponding plurality of evolution indicators; and
the employing includes employing the plurality of evolution indicators to produce an indication of seizure activity in the brain wave signal data.
3. The method according to claim 2 , wherein
the deriving further comprises deriving at least one additional parameter set sequence, wherein each additional parameter set sequence comprises sequential parameter sets, each parameter set comprising aggregate values for the two signal parameters derived from the corresponding areas of the opposite hemispheres or from the anterior and the posterior parts of the same hemisphere;
the determining further comprises determining a path formed by each of the at least one additional parameter set sequence in the parameter space, thereby to obtain at least one additional path;
the calculating further comprises calculating at least one additional evolution indicator, wherein each additional evolution indicator quantifies evolution occurred in respective additional path in the parameter space; and
the employing further comprises (i) selecting n largest evolution indicators from an evolution indicator group comprising the at least one additional evolution indicator and the corresponding plurality of evolution indicators, where n is an integer greater than one, and (ii) producing a final evolution indicator based on the n largest evolution indicators, wherein the final evolution indicator is indicative of the seizure activity.
4. The method according to claim 1 , wherein the employing includes making a decision on presence of a seizure.
5. The method according to claim 1 , wherein the calculating includes calculating the at least one evolution indicator, in which the at least one evolution indicator is indicative of length of respective path formed in the given time period in the parameter space.
6. The method according to claim 1 , wherein the first signal parameter is an instantaneous frequency derived via Hilbert transform, even signal moment of the order two or higher, nonlinear energy operator, rate of the zero crossings, median frequency, spectral edge frequency, peak power frequency, or mean frequency of the brain wave signal data.
7. The method according to claim 1 , wherein the deriving includes low-pass filtering the brain wave signal data, thereby to remove gamma activity and at least part of beta activity from the brain wave signal, thereby producing a filtered brain wave signal containing frequencies below beta activity band, and wherein the low-pass filtering is applied prior to the deriving of the at least one parameter set sequence.
8. An apparatus for monitoring seizure activity in brain, the apparatus comprising:
a measurement unit configured to acquire brain wave signal data from a subject;
a parameter determination unit configured to receive brain wave signal data from the measurement unit and derive at least one parameter set sequence from brain wave signal data obtained from the subject, wherein each parameter set sequence comprises values for two signal parameters comprising a first signal parameter and a second signal parameter, wherein the first signal parameter is a single value parameter indicative of the current frequency content of the brain wave signal data and the second signal parameter is a single value parameter indicative of the current amplitude of the brain wave signal data;
a path determination unit configured to determine a path formed by each of the at least one parameter set sequence in a parameter space, wherein the parameter space is a two-dimensional coordinate system, and wherein a first axis represents values of the first parameter and a second axis represents values of the second parameter, thereby to obtain at least one path;
an indicator determination unit configured to calculate at least one evolution indicator, wherein each evolution indicator is indicative of quantitative evolution occurred in respective path formed in a given time period in the parameter space; and
an indication unit configured to employ the at least one evolution indicator, thereby to produce an indication of seizure activity in the brain wave signal data.
9. The apparatus according to claim 8 , wherein
the measurement unit is configured to acquire a plurality of brain wave signal channels from the subject, in which the brain wave signal data belongs to the plurality of brain wave signal channels,
the parameter determination unit is configured to derive the parameter set sequence for each of the plurality of brain wave signal channels, thereby to obtain a corresponding plurality of parameter set sequences;
the path determination unit is configured to determine the path for each of the plurality of parameter set sequences, thereby to obtain a corresponding plurality of paths;
the indicator determination unit is configured to calculate an evolution indicator for each of the plurality of paths, thereby to obtain a corresponding plurality of evolution indicators; and
the indication unit is configured to employ the plurality of evolution indicators, thereby to produce an indication of seizure activity in the brain wave signal data.
10. The apparatus according to claim 9 , wherein
the parameter determination unit is further configured to determine at least one additional parameter set sequence, wherein each additional parameter set sequence comprises sequential parameter sets, each additional parameter set comprising aggregate values for the two signal parameters derived from the corresponding areas of the opposite hemispheres or from the anterior and the posterior parts of the same hemisphere;
the path determination unit is further configured to determine a path formed by each of the at least one additional parameter set sequence in the parameter space, thereby to obtain at least one additional path;
the indicator determination unit is further configured to calculate at least one additional evolution indicator, wherein each additional evolution indicator is indicative of quantitative evolution occurred in respective additional path formed in a given time period in the parameter space; and
the indication unit is further configured to select n largest evolution indicators from an evolution indicator group comprising the at least one additional evolution indicator and the plurality of evolution indicators and to produce a final evolution indicator based on the n largest evolution indicators, wherein the final evolution indicator is indicative of the seizure activity and n is an integer greater than one.
11. The apparatus according to claim 8 , wherein the indication unit is configured to make a decision on presence of a seizure.
12. The apparatus according to claim 8 , wherein the at least one evolution indicator is indicative of length of respective path formed in the given time period in the parameter space.
13. The apparatus according to claim 8 , wherein the parameter determination unit comprises a low-pass pre-filter configured to remove gamma activity and at least part of beta activity from the brain wave signal data, thereby producing filtered brain wave signal data containing frequencies below beta activity band.
14. The apparatus according to claim 8 , wherein the first signal parameter is an instantaneous frequency derived via Hilbert transform, even signal moment of the order two or higher, nonlinear energy operator, rate of the zero crossings, median frequency, spectral edge frequency, peak power frequency, or mean frequency of the brain wave signal data.