Table 2.
Team | Window (overlap, s) | Features | Machine learning algorithm | Ensemble method | Public leader board | Private leader board | Held-out data | Per cent change | Sensitivity at 75% specificity |
---|---|---|---|---|---|---|---|---|---|
Team A notsorandomanymore (1st place) | 20, 30, 50 (0) | Spectral power, distribution statistics, AR error, fractal dimensions, Hurst exponent, Riemannian autocorrelationa,b, cross-frequency coherence, correlation, other featuresc,d | Extreme gradient boostinge, k-nearest neighbours, generalized linear model, linear SVMf | Ranked average | 0.85276 | 0.80701 | 0.75275 | −6.7234 | 0.58322 |
Team B Arete Associates (2nd) | 60 (30), 600 (0) | Correlation, distribution statistics, zero crossings, complexity, mobility, maximum frequency, total summed energy, entropy, normalized summed spectral energy | Extremely randomized treesg | n/a | 0.78328 | 0.79898 | 0.73364 | −8.1773 | 0.56306 |
Team C GarethJones (3rd) | 80, 160, 240 (0) | Spectral power, distribution statistics, RMS of signal, first and second derivatives, correlation, spectral edge | Polynomial SVM, random under-sampling boosted tree ensemble | Weighted average | 0.81524 | 0.79652 | 0.65523 | −17.7388 | 0.41632 |
Team D QingnanTang (4th) | 75 (0) | Spectral power, correlation, spectral entropy, spectral edge power, square of features | Gradient boostingh, extreme gradient boosting, radial basis function SVM | Weighted average | 0.7965 | 0.79458 | 0.71805 | −9.6319 | 0.52086 |
Team E Nullset (5th) | 30 (0) | Spectral power, correlation (and eigenvalues), spectral entropy, Shannon entropy, spectral edge frequency, Hjorth parameters, fractal dimensions | Adaptive boosting, gradient boosting, random forest, extreme gradient boosting, gridsearch | Voting classifier | 0.81423 | 0.79363 | 0.62929 | −20.7074 | 0.46132 |
Team F tralala boum boum pouet pouet (6th) | 60 (0) | Spectral power, spectral entropy, time/spectral correlation (and eigenvalues), Petrosian fractal dimensioni, Hjorth parametersj, variance, skewness, kurtosis, convolutional neural network outputsk, long–short-term memory network outputsl | SVM, random forest, extreme gradient boosting | Weighted average | 0.80493 | 0.79197 | 0.71822 | −9.3118 | 0.49742 |
Team G Michaln (8th) | 600 (0) | Mean, SD, spectral edge at 50% power, skewness, kurtosis, Hjorth parameters, spectral entropy, maximum cross-correlation, spectral correlation, Brownian, Petrosian and Katz fractal dimensions, wavelet singular valuesm | Decision trees | Simple average | 0.80396 | 0.79074 | 0.73441 | −7.1238 | 0.56327 |
Team H Chipicito+Solver World (Special case: best team for Patient 3) | 15, 60 (0) | Spectral energy, spectral energy ratios, and absolute spectral amplitude | Extreme gradient boosting, random forest, convolutional neural network | Weighted rank average | 0.80334 | 0.73968 | 0.76632 | 3.602 | 0.62635 |
Additional information on algorithms is available in the Supplementary material. Table structure mimics similar table from the 2014 American Epilepsy Society Seizure Prediction Challenge publication (Brinkmann et al., 2016) to facilitate direct comparison. AR = autoregressive; RMS = root mean square; SVM = support vector machine.
aBarachant et al., 2013; bCongedo et al., 2017; cTemko et al., 2011; dTemko and Lightbody, 2016; eChen and Guestrin, 2016; fCortes and Vapnik, 1995; gGeurts et al., 2006; hFriedman, 2001; iPetrosian, 1995; jHjorth, 1970; kLeCun et al., 1998; lHochreiter and Schmidhuber, 1997; mBrinkmann et al., 2016.