• Standard electrode arrays for recording EEG are placed on the surface of the brain.

Detection of High Frequency Oscillations Using Support Vector Machines: A Case Study

Margret Una Kjartansdottir³, Haimonti Dutta¹, Catherine A. Schevon², Ansaf Salleb-Aouissi¹, David Waltz¹ and Ronald Emerson²¹Center for Computational Learning Systems, ²Columbia University Medical School (CUMC), ³Department of Computer Science,

Columbia University, New York, NY.

• Epilepsy is a chronic neurological disorder.• Patients suffer from recurrent, unprovoked seizures.• 65% of patients can be treated with antiepileptic drugs.• 8-10% need resective surgery to eliminate seizures.• During surgery patients are subjected to long-term electroencephalogram (EEG) monitoring.

• Two features - Power and Mean Curve Length (MCL) were extracted from the positive and negative samples and normalized.

• Parameters for Linear, Polynomial and Radial Basis Function (RBF) kernels, C, d and gamma were chosen by grid search. • The performance of the SVM is measured in terms of Area Under the ROC Curve (AUC) . • The Linear kernel with parameter C = 500 provides the best AUC=0.8468.

• V. N. Vapnik The nature of statistical learning theory Springer-Verlag New York, Inc., 1995.• C. A. Schevon, A. J. Trevelyan, et al. (2009b). Spatial characterization of interictal high frequency oscillations in epileptic neocortex. Brain 132(11): 3047-3059.

This work is supported by funding from Epilepsy Research Foundation and National Science Foundation IIS-0916186. The authors would like to thank Hatim Diab and Sam Lee for their help during different phases of the project.

• Support Vector Machines (SVM) were used to distinguish between HFOs and a randomly chosen snippet of EEG. • Positive samples (HFOs) were non-overlapping windows of size 3 seconds including one or more epochs of size 6 ms labeled as an HFO by the detection algorithm. • The negative samples (non-HFOs) were formed by random sampling. • The final data set consisted of 1686 training instances and 722 test instances .

• Signal is down sampled to 2000 Hz.• Filtered with 4th Order Butterworth high pass filter• Artifacts > 100 microvolt and <-100 microvolt removed. • Original signal

• Count of HFO’s by channel layout.

• One electrode is supplemented with a specialized Micro

Electrode Array (MEA).

• The ROC curve obtained from the best performing SVM with Linear Kernel.

• Difference in average MCL of HFO’s and non-HFO’s.

• Difference in average Power of HFO’s and non-HFO’s.

Introduction

Data Collection

Filter and Artifact Removal

High Frequency Oscillation detection algorithm

Acknowledgement

References

Conclusions

Features

Machine Learning: SVM

• MEA measures 4 mm x 4 mm with

96 channels recording signals at 30 kHz.

96 93 92 90 88 85 83 8195 63 94 91 89 87 86 84 80 7932 61 59 57 55 53 49 82 78 7730 64 60 58 56 51 47 45 76 7528 31 62 52 46 44 43 41 74 7326 29 21 54 50 42 40 39 72 7124 27 25 19 15 48 38 37 70 6922 20 23 13 17 5 36 35 68 6718 16 12 11 9 7 34 33 66 65 14 10 8 6 4 3 1 2

• After filtering and artifact removal.

7 7 1 4 15 1 30 149 6 10 3 1 17 34 65 36 177

13 3 4 3 8 19 20 111 6 4631 6 3 3 6 8 1 39 13 824 1 7 7 2 3 9 441 1 1 1 1 2 2 81 13 3 10 1 1 2 5 174 14 3 1 17 2 5 7 194 9 1 2 2 2 7 7 23 33

5 10 16 3 4 3 7 39

-1.2 -1.2 -1.3 -1.0 -0.4 0.1 -0.3 -0.8-0.9 -1.0 -1.0 -0.8 -0.1 -0.1 -0.3 -0.2 -1.4 -1.4-1.1 -1.4 0.0 -0.2 -0.6 -1.0 -0.3 -0.2 -0.9 -0.2-0.6 -0.2 -0.2 -0.4 -0.3 -0.3 -1.1 -0.2 -0.9 -1.0-0.6 -0.1 -0.9 -1.7 -0.6 -0.2 -1.1 -0.3-1.2 -0.3 -0.2 -0.6 -0.6 -1.2 -0.8 -0.9-1.2 -1.2 -0.6 -1.0 -1.2 -0.9 -1.1 -1.0 -0.5-1.0 -1.0 -1.1 -1.2 -1.1 -0.8 -1.0 -0.8 -0.4-0.6 -1.4 -1.2 -0.9 -0.5 -0.8 -1.2 -0.7 -0.3 -0.2

-0.9 -0.6 -0.2 -0.2 -0.3 -1.0 -1.4 -0.3

-0.31 -0.33 -0.32 -0.24 -0.14 0.04 -0.09 -0.19

-0.20 -0.22 -0.23 -0.22 -0.04 -0.02 -0.10 -0.07 -0.35 -0.53

-0.25 -0.34 -0.04 -0.06 -0.19 -0.26 -0.11 -0.07 -0.21 -0.05

-0.16 -0.07 -0.12 -0.18 -0.13 -0.14 -0.26 -0.09 -0.22 -0.22

-0.23 -0.06 -0.26 -0.73 -0.20 -0.06 -0.23 -0.07

-0.29 -0.11 -0.11 -0.14 -0.15 -0.34 -0.22 -0.24

-0.31 -0.28 -0.14 -0.25 -0.28 -0.20 -0.26 -0.22 -0.14

-0.27 -0.26 -0.28 -0.32 -0.25 -0.18 -0.27 -0.21 -0.14

-0.18 -0.32 -0.34 -0.19 -0.14 -0.20 -0.29 -0.16 -0.09 -0.08

-0.25 -0.16 -0.09 -0.03 -0.09 -0.25 -0.34 -0.09

= Micro Electrode Array= Seizure Onset

• HFOs may be potential biomarkers for epileptogenic activity. • HFOs are brief bursts in the high gamma band (80-500 Hz). • HFOs last for 10-100 milliseconds.

• High Frequency Oscillations (HFOs)

Criteria for detections:1. An epoch of 6 ms must have

root mean square (RMS) amplitude of at least 5 standard deviations above the mean for that channel.

2. At least 4 positive or negative peaks whose amplitude is greater

than 3 standard deviations above the mean should be present.

• Automatic HFO detection algorithm

• 90 second sample recording in 96 channels.

• HFO detection algorithm was run on 12 hours of MEA data from one patient.

Parameters AUC

Linear Kernel

C = 1 0.8038

C = 10 0.8376

C = 50 0.8454

C = 100 0.8461

C = 500 0.8468

C = 1000 0.8468

Polynomial Kernel

d = 3 0.6673

RBF Kernel

gamma = 0.3 0.7431

gamma = 0.7 0.7217

• The results obtained from building the HFO classifier.False Positive Rate

True

Pos

itive

Rat

e

• Decision boundary obtained to separate the HFOs vs. Non-HFOs.

Power

MCL