Method for identification of pathological brain activity from scalp electroencephalogram

1. A computer-implemented method for detecting brain activity patterns from a scalp electroencephalographic signal, the method comprising the steps of:

a) obtaining electroencephalographic signals as a function of multiple channels and time;

b) identifying, for each channel, the zero-crossings of its corresponding electroencephalographic signal over a fixed threshold;

c) generating a zero-crossing representation of the electroencephalographic signals with the identified zero-crossings;

d) obtaining at least one reference family of functions of time and channels from a zero-crossing statistical analysis of a zero-crossing representation of pre-recorded electroencephalographic signals, the functions of the at least one reference family of functions being real functions;

e) for each segment of a plurality of segments of the generated zero-crossing representation, calculating a corresponding matching score by comparing said zero-crossing representation of the segment with at least one reference function from the at least one reference family of functions;

f) monitoring an evolution of the matching score over time based on a set of matching scores for successive segments of the generated zero-crossing representation;

wherein, for each segment, the corresponding matching score relative to a zero-crossing representation of a segment for one reference function T centered in t 0 is defined by the formula:

S T (t 0 )=Σ k εK Σ tεϕ(k,t 0 )T k (t−t 0 ),

wherein S T (t 0 ) is the matching score, the first sum runs over the set of channels k εK, and the second sum runs over a defined subset t εϕ(k, t 0 ) of zero-crossings for each channel k of the segment surrounding t 0 ; and

g) determining a state of the brain based on the evolution of the matching score.

2. The method according to claim 1 , wherein said step of obtaining the at least one reference family of functions comprises the steps:

i. obtaining recordings of at least two electroencephalographic signals, wherein each electroencephalographic signal is a scalp electroencephalographic signal or an intracranial electroencephalographic signal of predefined length, wherein the recordings of at least two electroencephalographic signals are simultaneous to each other;

ii. performing the zero-crossing statistical analysis for each obtained electroencephalographic signal recording with respect to at least one feature of the obtained recordings; and

iii. computing the at least one reference family of functions from the zero-crossing statistical analysis.

3. The method according to claim 1 , further comprising a step of evaluating the matching scores by setting a binary criterion on the matching scores.

4. The method according to claim 3 , wherein the method differentiates a normal electroencephalographic signal from an electroencephalographic signal comprising pathological brain activity patterns.

5. The method according to claim 4 , wherein differentiation between a normal electroencephalographic signal and an electroencephalographic signal comprising pathological brain activity patterns is performed in real time.

6. The method according to claim 1 , wherein the step b) is preceded by a filtering step comprising filtering the electroencephalographic signals with a zero-phase shift bandpass filter.

7. The method according to claim 6 , wherein the filtering step is followed by the step of dividing the electroencephalographic signals into non-overlapping consecutive segments of fixed length.

8. The method according to claim 1 , wherein each matching score is calculated for a set of functions belonging to one reference family of functions, producing a respectively indexed set of matching scores for any given t 0 .

9. The method according to claim 1 , wherein matching scores calculated for a continuously indexed reference family of functions results in matching scores that are each a function of a time t 0 .

10. The method according to claim 1 , wherein the step of calculating each matching score includes comparing said zero-crossing representation of a corresponding segment of the generated zero-crossing representation with two reference functions issued from two different reference families of functions.

11. The method according to claim 1 , wherein the step of obtaining at least one reference family of functions comprises the steps of:

obtaining recordings of at least one scalp electroencephalographic signal of predefined length; and

deriving at least one reference family of functions by optimizing the parameters of a mathematical function according to characteristics of the obtained electroencephalographic signal recordings.

12. A system for detecting brain activity patterns, the system comprising a data processing system configured to:

a) obtain electroencephalographic signals as a function of multiple channels and time;

b) identify, for each channel, the zero-crossings of its corresponding electroencephalographic signal over a fixed threshold;

c) generate a zero-crossing representation of the electroencephalographic signals with the identified zero-crossings;

d) obtain at least one reference family of functions of time and channels from a zero-crossing statistical analysis of a zero-crossing representation of pre-recorded electroencephalographic signals, the functions of the at least one reference family of functions being real functions;

e) for each segment of a plurality of segments of the generated zero-crossing representation, calculate a corresponding matching score by comparing said zero-crossing representation of the segment with at least one reference function from the at least one reference family of functions;

f) monitor an evolution of the matching score over time based on a set of calculated matching scores for successive segments of the generated zero-crossing representation;

wherein, for each segment, the corresponding matching score relative to a zero-crossing representation of a segment for one reference function T centered in t 0 is defined by the formula:

S T (t 0 )=Σ k εK Σ tεϕ(k,t 0 )T k (t−t 0 ),

wherein S T (t 0 ) is the matching score, the first sum runs over the set of channels k εK, and the second sum runs over a defined subset t εϕ(k, t 0 ) of zero-crossings for each channel k of the segment surrounding t 0 ; and

g) determine a state of the brain based on the evolution of the matching score.

13. The system according to claim 12 , wherein, to obtain the at least one reference family of functions, the data processing system is further configured to:

i. obtain recordings of at least two electroencephalographic signals of predefined length, wherein each electroencephalographic signal is a scalp electroencephalographic signal or an intracranial electroencephalographic signal, and wherein the recordings of at least two electroencephalographic signals are simultaneous to each other;

ii. perform the zero-crossing statistical analysis for each obtained electroencephalographic signal recording with respect to at least one feature of the obtained recordings; and

iii. compute the at least one reference family of functions from the zero-crossing statistical analysis.

14. A non-transitory computer program product for detecting brain activity patterns, the computer program product comprising instructions which, when the instructions are executed by a computer, cause the computer to carry out a method comprising the steps of:

a) obtaining electroencephalographic signals as a function of multiple channels and time;

b) identifying, for each channel, the zero-crossings of its corresponding electroencephalographic signals over a fixed threshold;

c) generating a zero-crossing representation of the electroencephalographic signals with the identified zero-crossings;

d) obtaining at least one reference family of functions of time and channels from a zero-crossing statistical analysis of a zero-crossing representation of pre-recorded electroencephalographic signals, the functions of the at least one reference family of functions being real functions;

e) for each segment of a plurality of segments of the generated zero-crossing representation, calculating a corresponding matching score by comparing said zero-crossing representation of the segment with at least one reference function from the at least one reference family of functions;

f) monitoring an evolution of the matching score over time based on a set of matching scores for successive segments of the generated zero-crossing representation;

wherein, for each segment, the corresponding matching score relative to a zero-crossing representation of a segment for one reference function T centered in t 0 is defined by the formula:

S T (t 0 )=Σ k εK Σ tεϕ(k,t 0 )T k (t−t 0 ),

wherein S T (t 0 ) is the matching score, the first sum runs over the set of channels k εK, and the second sum runs over a defined subset t εϕ(k, t 0 ) of zero-crossings for each channel k of the segment surrounding t 0 ; and

g) determining a state of the brain based on the evolution of the matching score.

15. A non-transitory computer-readable storage medium comprising instructions which, when the instructions are executed by a computer, cause the computer to carry out a method comprising the steps of:

a) obtaining electroencephalographic signals as a function of multiple channels and time;

b) identifying, for each channel, the zero-crossings of its corresponding electroencephalographic signal over a fixed threshold;

c) generating a zero-crossing representation of the electroencephalographic signals with the identified zero-crossings;

d) obtaining at least one reference family of functions of time and channels from a zero-crossing statistical analysis of a zero-crossing representation of pre-recorded electroencephalographic signals, the functions of the at least one reference family of functions being real functions;

e) for each segment of a plurality of segments of the generated zero-crossing representation, calculating a corresponding matching score by comparing said zero-crossing representation of the segment with at least one reference function from the at least one reference family of functions;

f) monitoring an evolution of the matching score over time based on a set of matching scores for successive segments of the generated zero-crossing representation;

wherein, for each segment, the corresponding matching score relative to a zero-crossing representation of a segment for one reference function T centered in t 0 is defined by the formula:

S T (t 0 )=Σ k εK Σ tεϕ(k,t 0 )T k (t−t 0 ),

wherein S T (t 0 ) is the matching score, the first sum runs over the set of channels k εK, and the second sum runs over a defined subset t εϕ(k, t 0 ) of zero-crossings for each channel k of the segment surrounding t 0 ; and

g) determining a state of the brain based on the evolution of the matching score.

16. The computer-readable storage medium according to claim 15 , wherein the computer-readable storage medium further comprises the at least one reference family of functions.