Resources

Selected Literature

Crowdsourcing

• Warby, S. C., Wendt, S. L., Welinder, P., Munk, E. G. S., Carrillo, O., Sorensen, H. B. D., … Mignot, E. (2014). Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nature Methods, 11(4), 385–392. http://doi.org/10.1038/nmeth.2855

• Hsueh, P.-Y., Melville, P., & Sindhwani, V. (2009). Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria. In Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing (pp. 27–35). Stroudsburg, PA, USA: Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=1564131.1564137

• Ipeirotis, P. G., Provost, F., & Wang, J. (2010). Quality management on Amazon Mechanical Turk. In Proceedings of the ACM SIGKDD Workshop on Human Computation - HCOMP ’10 (p. 64). New York, New York, USA: ACM Press. http://doi.org/10.1145/1837885.1837906

• Urner, R., Shai, B.-D., & Ohad, S. (2012). Learning from Weak Teachers. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (pp. 1252–1260). La Palma, Canary. Retrieved from http://jmlr.csail.mit.edu/proceedings/papers/v22/urner12.html

Machine Learning in the Domain of Sleep Staging

• Dietterich, T. G. (2002). Machine Learning for Sequential Data: A Review (pp. 15–30). http://doi.org/10.1007/3-540-70659-3_2

• Bajaj, V., & Pachori, R. B. (2013). Automatic classification of sleep stages based on the time-frequency image of EEG signals. Computer Methods and Programs in Biomedicine, 112(3), 320–328. http://doi.org/10.1016/j.cmpb.2013.07.006

• Camilleri, T. A., Camilleri, K. P., & Fabri, S. G. (2014). Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models. Biomedical Signal Processing and Control, 10, 117–127. http://doi.org/10.1016/j.bspc.2014.01.010

• Darkhovsky, B., & Piryatinska, A. (2012). A new complexity-based algorithmic procedures for electroencephalogram (EEG) segmentation. In 2012 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1–5). IEEE. http://doi.org/10.1109/SPMB.2012.6469462

• Herrera, L. J., Fernandes, C. M., Mora, A. M., Migotina, D., Largo, R., Guillen, A., & Rosa, A. C. (2013). Combination of heterogenous EEG feature extraction methods and stacked sequential learning for sleep stage classification. International Journal of Neural Systems, 23(03), 1350012. http://doi.org/10.1142/S0129065713500123

• Kouchaki, S., Sanei, S., Arbon, E. L., & Dijk, D.-J. (2015). Tensor Based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(1), 1–9. http://doi.org/10.1109/TNSRE.2014.2329557

• Lechinger, J., Heib, D. P. J., Gruber, W., Schabus, M., & Klimesch, W. (2015). Heartbeat-related EEG amplitude and phase modulations from wakefulness to deep sleep: Interactions with sleep spindles and slow oscillations. Psychophysiology, 52(11), 1441–1450. http://doi.org/10.1111/psyp.12508

• Motamedi-Fakhr, S., Moshrefi-Torbati, M., Hill, M., Hill, C. M., & White, P. R. (2014). Signal processing techniques applied to human sleep EEG signals—A review. Biomedical Signal Processing and Control, 10, 21–33. http://doi.org/10.1016/j.bspc.2013.12.003

• O’Reilly, C., Godbout, J., Carrier, J., & Lina, J.-M. (2015). Combining time-frequency and spatial information for the detection of sleep spindles. Frontiers in Human Neuroscience, 9. http://doi.org/10.3389/fnhum.2015.00070

Expert Disagreement in the Medical Domain

• Carvalho, A., & Larson, K. (2013). A Consensual Linear Opinion Pool. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (pp. 2518–2524). Beijing, China: AAAI Press. Retrieved from http://dl.acm.org/citation.cfm?id=2540128.2540491

• Foncubierta Rodríguez, A., & Müller, H. (2012). Ground truth generation in medical imaging. In Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia - CrowdMM ’12 (p. 9). New York, New York, USA: ACM Press. http://doi.org/10.1145/2390803.2390808

• Garbayo, L. (2014). Epistemic Considerations on Expert Disagreement, Normative Justification, and Inconsistency Regarding Multi-criteria Decision Making. Constraint Programming and Decision Making, 539, 35–45. Retrieved from http://link.springer.com/10.1007/978-3-319-04280-0_5

• Inel, O., Khamkham, K., Cristea, T., Dumitrache, A., Rutjes, A., van der Ploeg, J., … Sips, R.-J. (2014). CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data. In 13th International Semantic Web Conference (ISCW2014) (pp. 486–504). Cham: Springer Verlag. http://doi.org/10.1007/978-3-319-11915-1_31

• Kors, J. A., Sittig, A. C., & van Bemmel, J. H. (1990). The Delphi Method to Validate Diagnostic Knowledge in Computerized ECG Interpretation. Methods of Information in Medicine, 29(1), 44—50. Retrieved from http://europepmc.org/abstract/MED/2407933