Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Klösch, G., Stefanic-Kejik, A., Böck, M., Kaniusas, E., & Seidel, S. (2021). 3D Camera and pulse oximeter for respiratory events detection. IEEE Journal of Biomedical and Health Informatics, 25(1), 181–188. https://doi.org/10.1109/jbhi.2020.2984954 ( reposiTUm)

Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Klösch, G., Stefanic-Kejik, A., Böck, M., Kaniusas, E., & Seidel, S. (2020). Detection of respiratory events by respiratory effort and oxygen desaturation. Journal of Medical and Biological Engineering, 40(4), 517–525. https://doi.org/10.1007/s40846-020-00524-9 ( reposiTUm)

Gall, M., Garn, H., Kohn, B., Bajic, K., Coronel, C., Seidel, S., Mandl, M., & Kaniusas, E. (2020). Automated detection of movements during sleep using a 3D Time-of-Flight camera: Design and experimental evaluation. IEEE Access, 8, 109144–109155. https://doi.org/10.1109/access.2020.3001343 ( reposiTUm)

Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Kloesch, G., Stefanic-Kejik, A., Boeck, M., Kaniusas, E., & Seidel, S. (2019). Detecting Respiratory Events By Respiratory Effort Derived From 3D Time-of-Flight Camera And SpO₂. SLEEP, 42(Supplement_1), A185–A185. https://doi.org/10.1093/sleep/zsz067.459 ( reposiTUm)

Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Klösch, G., Stefanic-Kejik, A., Böck, M., Kaniusas, E., & Seidel, S. (2019). Measurement of respiratory effort in sleep by 3D camera and respiratory inductance plethysmography : A comparison of two methods. Somnologie, 23(2), 86–92. https://doi.org/10.1007/s11818-019-0203-y ( reposiTUm)

Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Klösch, G., Stefanic-Kejik, A., Böck, M., Kaniusas, E., & Seidel, S. (2019). Using respiratory effort and SpO₂ to detect respiratory events. Sleep Medicine, 64, S126–S127. https://doi.org/10.1016/j.sleep.2019.11.347 ( reposiTUm)

Coronel, C., Garn, H., Waser, M., Deistler, M., Benke, T., Dal-Bianco, P., Ransmayr, G., Seiler, S., Grossegger, D., & Schmidt, R. (2017). Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer’s Disease Patients. Entropy, 19(3), Article 130. https://doi.org/10.3390/e19030130 ( reposiTUm)

Waser, M., Garn, H., Schmidt, R., Benke, T., Dal-Bianco, P., Ransmayr, G., Schmidt, H., Seiler, S., Sanin, G., Mayer, F., Caravias, G., Grossegger, D., Frühwirth, W., & Deistler, M. (2016). Quantifying synchrony patterns in the EEG of Alzheimer’s patients with linear and non-linear connectivity markers. Journal of Neural Transmission, 123(3), 297–316. https://doi.org/10.1007/s00702-015-1461-x ( reposiTUm)

Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., & Kropatsch, W. (2016). Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals. Pattern Recognition, 320–331. https://doi.org/10.1007/978-3-319-45886-1_26 ( reposiTUm)

Garn, H., Waser, M., Deistler, M., Benke, T., Dal-Bianco, P., Ransmayr, G., Schmidt, H., Sanin, G., Santer, P., Caravias, G., Seiler, S., Grossegger, D., Fruehwirt, W., & Schmidt, R. (2015). Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM). Clinical Neurophysiology, 126(3), 505–513. https://doi.org/10.1016/j.clinph.2014.07.005 ( reposiTUm)

Garn, H., Waser, M., Deistler, M., Schmidt, R., Dal-Bianco, P., Ransmayr, G., Zeitlhofer, J., Schmidt, H., Seiler, S., Sanin, G., Caravias, G., Santer, P., Grossegger, D., Fruehwirt, W., & Benke, T. (2014). Quantitative EEG in Alzheimer’s disease: Cognitive state, resting state and association with disease severity. International Journal of Psychophysiology, 93(3), 390–397. https://doi.org/10.1016/j.ijpsycho.2014.06.003 ( reposiTUm)

Waser, M., Deistler, M., Garn, H., Benke, T., Dal-Bianco, P., Ransmayr, G., Grossegger, D., & Schmidt, R. (2013). EEG in the diagnostics of Alzheimer’s disease. Statistical Papers, 54(4), 1095–1107. https://doi.org/10.1007/s00362-013-0538-6 ( reposiTUm)

Neubauer, G., Preiner, P., Cecil, S., Mitrevski, N., Gonter, J., & Garn, H. (2009). The relation between the specific absorption rate and electromagnetic field intensity for heterogeneous exposure conditions at mobile communications frequencies. Bioelectromagnetics, 30(8), 651–662. https://doi.org/10.1002/bem.20519 ( reposiTUm)

Dietrich, D., & Garn, H. (2007). Embedded Vision System. EURASIP Journal on Embedded Systems, HTTP://WWW.HINDAWI.COM, 2. http://hdl.handle.net/20.500.12708/168486 ( reposiTUm)

Coronel, C., Wiesmeyr, C., Garn, H., Kohn, B., Naghibzadeh-Jalali, A., Schindler, A., Wimmer, M., Mandl, M., Glos, M., Penzel, T., Klösch, G., Stefanic-Kejik, A., Böck, M., Kaniusas, E., & Seidel, S. (2020). Comparison of PSG signals and respiratory movement signal via 3D camera in detecting sleep respiratory events by LSTM models. In Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2020 (APSIPA ASC 2020) (pp. 919–923). http://hdl.handle.net/20.500.12708/77233 ( reposiTUm)

Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., & Kropatsch, W. (2016). A real-time algorithm for train position monitoring using optical time-domain reflectometry. In 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT). 2016 IEEE International Conference on Intelligent Rail Transportation, Birmingham, United Kingdom of Great Britain and Northern Ireland (the). IEEE. https://doi.org/10.1109/icirt.2016.7588715 ( reposiTUm)

Waser, M., Garn, H., Deistler, M., Benke, T., Dal-Bianco, P., Ransmayr, G., Schmidt, H., Sanin, G., Santer, P., Caravias, G., Seiler, S., Grossegger, D., Fruehwirt, W., & Schmidt, R. (2014). Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer’s disease severity. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Austria. IEEE Xplore. https://doi.org/10.1109/embc.2014.6944205 ( reposiTUm)

Garn, H., Waser, M., Deistler, M., Benke, T., Dal-Bianco, P., Ransmayr, G., Schmidt, H., Sanin, G., Santer, P., Caravias, G., Seiler, S., Grossegger, D., Fruehwirt, W., & Schmidt, R. (2014). Electroencephalographic complexity markers explain neuropsychological test scores in Alzheimer’s disease. In IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, Austria. IEEE Xplore. https://doi.org/10.1109/bhi.2014.6864411 ( reposiTUm)