<commit_before>@article{2014:howbert:plosone,
    author = {Howbert, , <NAME> Patterson, , <NAME>, , <NAME> AND Brinkmann, , Ben AND Vasoli, , Vincent AND Crepeau, , Daniel AND Vite, , <NAME>. AND Sturges, , Beverly AND Ruedebusch, , Vanessa AND Mavoori, , Jaideep AND Leyde, , Kent AND Sheffield, , <NAME> AND Litt, , Brian AND Worrell, , <NAME>.},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Forecasting Seizures in Dogs with Naturally Occurring Epilepsy},
    year = {2014},
    month = {01},
    volume = {9},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0081920},
    pages = {e81920},
    abstract = {<p>Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.</p>},
    number = {1},
    doi = {10.1371/journal.pone.0081920}
}        

	
@inproceedings{2011:glorot:aistats,
    Publisher = {Journal of Machine Learning Research - Workshop and Conference Proceedings}, 
    Author = {<NAME> and <NAME> and <NAME>}, 
    Url = {http://www.jmlr.org/proceedings/papers/v15/glorot11a/glorot11a.pdf}, 
    Booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11)}, 
    Title = {Deep Sparse Rectifier Neural Networks}, 
    Volume = {15}, 
    Editor = {<NAME> and <NAME>}, 
    Year = {2011}, 
    Pages = {315-323} 
   }

@Misc{michaelhills,
  OPTkey =       {},
  author =    {<NAME>},
  title =     {Seizure detection using FFT, temporal and spectral correlation coefficients, eigenvalues and Random Forest},
  OPThowpublished = {},
  OPTmonth =     {},
  year =      {2014},
  note =      {\url{https://github.com/MichaelHills/seizure-detection}},
  OPTannote =    {}
}

@INPROCEEDINGS{2011:monteith:ijcnn, 
author={<NAME>.}, 
booktitle={Neural Networks (IJCNN), The 2011 International Joint Conference on}, 
title={Turning Bayesian model averaging into Bayesian model combination}, 
year={2011}, 
month={July}, 
pages={2657-2663}, 
keywords={Bayes methods;belief networks;learning (artificial intelligence);Bayesian methods;Bayesian model averaging;Bayesian model combination;Bayesian strategy;ad hoc methods;Accuracy;Bagging;Bayesian methods;Biological system modeling;Data models;Mathematical model;Uncertainty}, 
doi={10.1109/IJCNN.2011.6033566}, 
ISSN={2161-4393},}

@misc{aprilann,
	Author = {<NAME> and others},
	Note = {{https://github.com/pakozm/april-ann}},
	Title = {{APRIL-ANN toolkit, A Pattern Recognizer In Lua with Artificial Neural Networks}},
	Year = {2014}}

@misc{Rproject,
	Author = {{R Development Core Team}},
	Note = {{http://www.R-project.org}},
	OPTannote = {R Foundation for Statistical Computing, Vienna, Austria},
	Title = {R: A language and evironment for statistical computing},
	Year = {2005}}

@Misc{kaggle,
  OPTkey =       {},
  author =    {Kaggle},
  title =     {American Epilepsy Society Seizure Prediction Challenge},
  OPThowpublished = {},
  OPTmonth =     {},
  year =      {2014},
  note =      {https://www.kaggle.com/c/seizure-prediction},
  OPTannote =    {}
}

<commit_msg>Updated bibliography for APRIL-ANN toolkit<commit_after>@article{2014:howbert:plosone,
    author = {Howbert, , <NAME> Patterson, , <NAME> Stead, , <NAME> AND Brinkmann, , <NAME>, , Vincent AND Crepeau, , Daniel AND Vite, , <NAME>. AND Sturges, , Beverly AND Ruedebusch, , Vanessa AND Mavoori, , Jaideep AND Leyde, , Kent AND Sheffield, , <NAME> AND Litt, , Brian AND Worrell, , <NAME>.},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {Forecasting Seizures in Dogs with Naturally Occurring Epilepsy},
    year = {2014},
    month = {01},
    volume = {9},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0081920},
    pages = {e81920},
    abstract = {<p>Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.</p>},
    number = {1},
    doi = {10.1371/journal.pone.0081920}
}        

	
@inproceedings{2011:glorot:aistats,
    Publisher = {Journal of Machine Learning Research - Workshop and Conference Proceedings}, 
    Author = {<NAME> and <NAME> and <NAME>}, 
    Url = {http://www.jmlr.org/proceedings/papers/v15/glorot11a/glorot11a.pdf}, 
    Booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11)}, 
    Title = {Deep Sparse Rectifier Neural Networks}, 
    Volume = {15}, 
    Editor = {<NAME> and <NAME>}, 
    Year = {2011}, 
    Pages = {315-323} 
   }

@Misc{michaelhills,
  OPTkey =       {},
  author =    {<NAME>},
  title =     {Seizure detection using FFT, temporal and spectral correlation coefficients, eigenvalues and Random Forest},
  OPThowpublished = {},
  OPTmonth =     {},
  year =      {2014},
  note =      {\url{https://github.com/MichaelHills/seizure-detection}},
  OPTannote =    {}
}

@INPROCEEDINGS{2011:monteith:ijcnn, 
author={<NAME> <NAME>.}, 
booktitle={Neural Networks (IJCNN), The 2011 International Joint Conference on}, 
title={Turning Bayesian model averaging into Bayesian model combination}, 
year={2011}, 
month={July}, 
pages={2657-2663}, 
keywords={Bayes methods;belief networks;learning (artificial intelligence);Bayesian methods;Bayesian model averaging;Bayesian model combination;Bayesian strategy;ad hoc methods;Accuracy;Bagging;Bayesian methods;Biological system modeling;Data models;Mathematical model;Uncertainty}, 
doi={10.1109/IJCNN.2011.6033566}, 
ISSN={2161-4393},}

@misc{aprilann,
	Author = {<NAME> and <NAME>{\~n}a-Boquera and
            <NAME> and <NAME> and <NAME>},
	Note = {{https://github.com/pakozm/april-ann}},
	Title = {{APRIL-ANN toolkit, A Pattern Recognizer In Lua with Artificial Neural Networks}},
	Year = {2014}}

@misc{Rproject,
	Author = {{R Development Core Team}},
	Note = {{http://www.R-project.org}},
	OPTannote = {R Foundation for Statistical Computing, Vienna, Austria},
	Title = {R: A language and evironment for statistical computing},
	Year = {2005}}

@Misc{kaggle,
  OPTkey =       {},
  author =    {Kaggle},
  title =     {American Epilepsy Society Seizure Prediction Challenge},
  OPThowpublished = {},
  OPTmonth =     {},
  year =      {2014},
  note =      {https://www.kaggle.com/c/seizure-prediction},
  OPTannote =    {}
}


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