Open Access

Detecting Outliers in Cardiopulmonary Exercise Testing Data of Ski Racers – A Comparison of Methods and their Effect on the Performance of Fatigue Prediction


Cite

Barroso, M. T. C., Hoppe, M. W., Boehme, P., Krahn, T., Kiefer, C., Kramer, F., Mondritzki, T., Pirez, P., & Dinh, W. (2019). Test-retest reliability of non-invasive cardiac output measurement during exercise in healthy volunteers in daily clinical routine. Arquivos Brasileiros de Cardiologia, 113, 231–239. Search in Google Scholar

Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems, 11(2), 137–154. Search in Google Scholar

Baumgartner, N., Kranzinger, C., Kranzinger S., Snyder, C., Stöggl, T., & Resch, B. (2022). A Comparison of Methods for Automatic Outlier Detection in Ergospirometric Data and their Effect on the Performance of Predictive Models. Proceedings of 13th World Congress of Performance Analysis of Sport & the 13th International Symposium on Computer Science in Sport. Manuscript submitted for publication. Search in Google Scholar

Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR), 54(3), 1–33. Search in Google Scholar

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32. Search in Google Scholar

Broich, H., Sperlich, B., Buitrago S., Mathes, S., & Mester, J. (2012). Performance assessment in elite football players: field level test versus spiroergometry. Journal of Human Sport and Exercise, 7(1), 287–295. Search in Google Scholar

Chrominsky, K. & Tkacz, M. (2010). Comparison of outlier detection methods in biomedical data. Journal of Medical Informatics & Technologies, 16, 89–94. Search in Google Scholar

COSMED. (2003). K4 b2 User manual (9th ed.). Rome: COSMED Srl. Search in Google Scholar

Epishev, V., Korableva, J., Alferova, T., Yakhin, D., & Episheva, A. (2019). Functional diagnostics in the comparative assessment of physical performance in ski racers to forecast sports performance development. Proceedings of the 4th International Conference on Innovations in Sports, Tourism and Instructional Science (ICISTIS 2019), 53-55. Search in Google Scholar

Dixon, W. J. (1950). Analysis of Extreme Values. Annals of Mathematical Statistics 21(4), 488-506. Search in Google Scholar

Grubbs, F. E. (1950). Sample Criteria for Testing Outlying Observations. Annals of Mathematical Statistics 21(1), 27-58. Search in Google Scholar

Hyndman, R. J. & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts. Retrieved June 22, 2022, from https://otexts.org/fpp2. Search in Google Scholar

Hyndman, R. J. (2021). Detecting time series outliers. Retrieved June 22, 2022, from https://robjhyndman.com/hyndsight/tsoutliers. Search in Google Scholar

Hyndman, R. J. (2022). MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns. Retrieved June 22, 2022, from https://robjhyndman.com/publications/mstl. Search in Google Scholar

Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2022). forecast: Forecasting functions for time series and linear models. R package version 8.16. https://pkg.robjhyndman.com/forecast. Search in Google Scholar

Kranzinger, S., Kranzinger, C., Snyder, C., & Stöggl, T. (2022). Predicting the rate of fatigue during skiing on a ski treadmill based on ergospirometric data. [Abstract]. Accepted at the 27th annual congress of the European College of Sports Science - ECSS Sevilla 2022. Search in Google Scholar

Luo, A. Z., Whitmire, E., Stout, J. W., Martenson, D., & Patel, S. (2017). Automatic characterization of user errors in spirometry. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4239–4242. Search in Google Scholar

Mapelli, M., Salvioni, E., Paneroni, M., Gugliandolo, P., Bonomi, A., Scalvini, S., Raimondo, R., Sciomer, S., Mattavelli, I., La Rovere, M. T., & Agostoni, P. (2022). Brisk walking can be a maximal effort in heart failure patients: a comparison of cardiopulmonary exercise and 6 min walking test cardiorespiratory data. ESC heart failure, 9(2), 812–821. Search in Google Scholar

Micklewright, D., St Clair Gibson, A., Gladwell, V., & Al Salman, A. (2017). Development and validity of the rating-of-fatigue scale. Sports Medicine, 47(11), 2375–2393. Search in Google Scholar

Robergs, R. A., Dwyer, D., & Astorino, T. (2010). Recommendations for Improved Data Processing from Expired Gas Analysis Indirect Calorimetry. Sports medicine (Auckland, N.Z.), 40(2), 95–111. Search in Google Scholar

Rosner, B. (1975). On the detection of many outliers. Technometrics, 17(2), 221–227. Search in Google Scholar

Rosner, B. (1983). Percentage points for a generalized ESD many-outlier procedure. Technometrics, 25(2), 165–172. Search in Google Scholar

eISSN:
1684-4769
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Computer Sciences, Databases and Data Mining, other, Sports and Recreation, Physical Education