Machine Learning-Based Classification of Patterns of EEG Synchronization for Seizure Prediction

Machine learning based classification of patterns of eeg synchronization for seizure prediction l.jpg
1 / 21
1409 days ago, 504 views
PowerPoint PPT Presentation

Presentation Transcript

Slide 1

Machine Learning-Based Classification of Patterns of EEG Synchronization for Seizure Prediction Piotr Mirowski, Deepak Madhavan MD, Yann LeCun PhD, Ruben Kuzniecky MD Courant Institute of Mathematical Sciences

Slide 2

Observation window Seizure onset Extraction of elements from EEG, design acknowledgment + arrangement intracranial EEG interictal stage preictal stage ictal stage The seizure forecast issue Review of writing: most strategies actualize 1D choice limit machine learning utilized just for highlight determination Trade-off between: affectability (having the capacity to foresee seizures) specificity (keeping away from false positives) Benchmark information: 21-persistent Freiburg EEG dataset; current best results are: 42 % affectability 3 false positives for each day (0.25 fp/hour) [Litt and Echauz, 2002; Schulze-Bonhage et al, 2006]

Slide 3

Hypotheses examples of brainwave synchronization: could separate preictal from interictal stages would be remarkable for every epileptic patient meaning of a " design " of brainwave synchronization: accumulation of bivariate " highlights " got from EEG, on all sets of EEG stations (central and extrafocal) taken at back to back time-focuses catch transient changes a bivariate " include ": catches a relationship : over a brief span window objective: quiet particular programmed figuring out how to separate preictal and interictal examples of brainwave synchronization highlights interictal preictal ictal [Le Van Quyen et al, 2003; Mirowski et al, 2009]

Slide 4

1min of preictal EEG 1min of interictal EEG 1min preictal design 1min interictal design Examples of examples of cross-connection Patterns of bivariate components Varying synchronization of EEG stations Non-frequential highlights: Max cross-relationship [Mormann et al, 2005] Nonlinear association [Arhnold et al, 1999] Dynamical entrainment [Iasemidis et al, 2005] Frequency-particular elements: [Le Van Quyen et al, 2005] Phase locking synchrony Entropy of stage contrast Wavelet intelligence [Le Van Quyen et al, 2003; Mirowski et al, 2009]

Slide 5

c) 60-outline patterns (5min) d) Legend a) 1-outline patterns (5s) b) 12-outline patterns (1min) Separating examples of elements 2D projections (PCA) of wavelet synchrony SPLV highlights, tolerant 1 [Mirowski et al, 2009]

Slide 6

Features registered on 5s windows ( N =1280 tests) 6x5/2= 15 bivariate elements on 6 EEG stations (Freiburg dataset) Wavelet investigation based synchrony values gathered in 7 electrophysiological recurrence groups : δ [0.5Hz-4Hz], θ [4Hz-7Hz], α [7Hz-13Hz], low β [13Hz-15Hz], high β [15Hz-30Hz], low γ [30Hz-45Hz], high γ [55Hz-120Hz] Features are collected into worldly examples y t : 12 outlines (1min) or 60 outlines (5min) # deed C, S, DSTL SPLV, H, Coh 1min 12 15=180 12 157=1260 5min 60 15=900 12 157=6300 Patterns of bivariate elements [Mirowski et al, 2009]

Slide 7

Machine Learning Classifiers Input affectability Input example of elements: p x60 Layer 1 5@ p x48 Layer 2 5@ p x24 Layer 3 5@1x16 Layer 5 3 Layer 4 5@1x8 preictal interictal 1x8 convolution (crosswise over time) 1x2 sub-inspecting p x9 convolution (crosswise over time and space/freq) 1x13 convolution (crosswise over time) 1x2 subsampling L 1 - regularized convolutional systems (LeNet5, above) L 1 - regularized calculated relapse Support vector machines (Gaussian pieces) L1-regularization highlights sets of stations and recurrence groups discriminative for seizure expectation [LeCun et al, 1998; Mirowski et al, AAAI 2007, 2009]

Slide 8

21-understanding Freiburg EEG dataset therapeutically obstinate > 24h interictal 2 to 6 seizures Train + x-val on 66% information (57 prior seizures) PATIENT SPECIFIC Test on 33% information (31 later seizures) Previous best results: 42% affectability, 0.25 fpr/h [Aschenbrenner-Scheibe et al, 2003; Schelter et al, 2006a, 2006b; Maiwald, 2004; Winterhalder et al, 2003]

Slide 9

Results on 21 patients (Freiburg) For every patient , no less than 1 technique predicts 100% of seizures , by and large a hour prior to the onset , with no false alert . Be that as it may, not generally a similar strategy… 16 mixes (highlight, classifier): how to pick a decent one? Classifiers : Features : Wavelet cognizance + conv-net : 15/21 patients ( 0 fp/hour ) Wavelet SPLV + conv-net : 13/21 patients (0 fp/hour) Wavelet intelligibility + SVM: 14/21 patients (<0.25 fp/hour) Nonlinear association + SVM: 13/21 patients (<0.25 fp/hour) [Mirowski et al, 2009]

Slide 10

Example of seizure expectation True positives False negatives False negatives True negatives Wavelet lucidness + convolutional organize, tolerant 8 [Mirowski et al, 2009]

Slide 11

Patient 12, nonlinear reliance 15 extrafocal TLB3 TLC2 TLB2 TLC2 [HR_7] TLC2 [TBB6] TLC2 [TBA4] TLC2 TLB2 TLB3 [HR_7] TLB3 [TBB6] TLB3 [TBA4] TLB3 [HR_7] TLB2 [TBB6] TLB2 [TBA4] TLB2 [TBB6] [HR_7] [TBA4] [HR_7] [TBA4] [TBB6] central extrafocal 10 extrafocal central extrafocal 5 intrafocal 0 30 40 50 60 0 10 20 Time (outlines) Patient 8, wavelet rationality 4 High γ (55-100Hz) Low γ (31-45Hz) 3 High β (14Hz – 30Hz) Low β (13Hz – 15Hz) 2 α (7Hz – 13Hz) 1 θ (4Hz – 7Hz) δ (< 4Hz) 0 20 30 40 50 60 0 10 Time (outlines) Feature affectability (and choice) L 1 regularization → meager weights Analysis of information affectability : Logistic relapse: take a gander at weights Conv nets: inclination on sources of info High γ frequencies could be discriminative for seizure forecast order ? [Mirowski et al, 2009]

Slide 12

Thank You Litt B., Echauz J., Prediction of epileptic seizures , The Lancet Neurology 2002 EEG Database at the Epilepsy Center of the University Hospital of Freiburg, Germany , accessible: extend/eeg-database/Le Van Quyen M., Soss J., Navarro V., et al, Preictal state distinguishing proof by synchronization changes in long haul intracranial recordings , Clinical Neurophysiology 2005 Mormann F., Kreuz T., Rieke C., et al, On the consistency of epileptic seizures, Clinical Neurophysiology 2005 Mormann F., Elger C.E., Lehnertz K., Seizure foresight: from calculations to clinical practice , Current Opinion in Neurology 2006 Iasemidis L.D., Shiau D.S., Pardalos P.M., et al, Long-term planned online constant seizure forecast , Clinical Neurophysiology 2005 B. Schelter, M. Winterhalder, T. Maiwald, et al, Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure-Prediction Methods and Proposed Remedies , Epilepsia , 2006 B. Schelter, M. Winterhalder, T. Maiwald, et al, Testing factual hugeness of multivariate time arrangement examination methods for epileptic seizure forecast", Chaos , 2006 T. Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, et al, Comparison of three nonlinear seizure expectation strategies by method for the seizure forecast trademark , Physica D , 2004 R. Aschenbrenner-Scheibe, T. Maiwald, M. Winterhalder, et al, How well can epileptic seizures be anticipated? An assessment of a nonlinear strategy , Brain , 2003 M. Winterhalder, T. Maiwald, H. U. Voss, et al, The seizure expectation trademark: a general system to survey and think about seizure forecast strategies , Epilepsy Behavior , 2003 J. Arnhold, P. Grassberger, K. Lehnertz, C. E. Elger, A strong strategy for identifying reliance: applications to intracranially recorded EEG , Physica D , 1999 LeCun Y., Bottou L., et al, Gradient-Based Learning Applied to Document Recognition , Proc IEEE , 86(11), 1998 Mirowski P., Madhavan D., et al, TDNN and ICA for EEG-Based Prediction of Epileptic Seizures Propagation , 22nd AAAI Conference 2007 Mirowski P., et al, Classification of Patterns of EEG Synchronization for Seizure Prediction , Clinical Neurophysiology, under update Mirowski P., et al, System and Method for Ictal Classification , US Patent Application, 2009 12

Slide 14


Slide 15

Detailed results

Slide 16

Maximum cross-connection Cross-relationship between's EEG channels x an and x b : Maximum cross-relationship for deferrals | τ |< 0.5s: Cross-relationship between's channels For every channel, decision of postpone giving best cross-relationship [Mormann et al, 2005] 16

Slide 17

Time-delay inserting x a ( t ) and x b ( t ) are time-postpone embeddings of d EEG tests from channels x an and x b around time t . Elec b Elec a 1 second [Iasemidis et al, 2005], [Mormann et al, 2005]

Slide 18

Nonlinear reliance Measure Euclidian separations , in state-space , between directions of x a ( t ) and x b ( t ) . Similitude of direction of x a ( t ) to the direction of x b ( t ) : K closest neighbors of x a ( t ) : Distance of neighbors of x a ( t ) to x a ( t ) : Symmetric measure of comparability of directions: K closest neighbors of x b ( t ) : Distance of neighbors of x b ( t ) to x a ( t ) : [Arnhold et al, 1999] [Mormann et al, 2005]

Slide 19

Difference of Lyapunov examples Estimate of the biggest Lyapunov type of x a ( t ) , i.e. exponential rate of development of an irritation in x a ( t ): STL b STL a Short-term Lyapunov example (registered more than 10sec) diminishes (i.e. solidness of EEG direction increments ) before seizure 1 hour Measure of joining of confused conduct of EEG channels x an and x b : disentrainment entrainment [Iasemidis et al, 2005] 19

Slide 20

Phase locking, synchrony Phase locking = stage synchrony (Wavelet or Hilbert changes) stage [Le Van Quyen et al, 2005], [Mormann et al, 2005] 20

Slide 21

Phase locking insights φ a,f ( t ) and φ b,f ( t ) are periods of Morlett wavelet coefficients from EEG channels x an and x b , at recurrence f , time t Phase-locking esteem at recurrence f : Related measure: wavelet rationality Coh a,b ( f ) Shannon entropy of stage distinction at recurrence f utilizing M b