Page - 343 - in Intelligent Environments 2019 - Workshop Proceedings of the 15th International Conference on Intelligent Environments

References [1] M. Gjoreski, M. Simjanoska, A. Gradišek, A. Peterlin, M. Gams and G. Poglajen, "Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers," 2017 International Conference on Intelligent Environments (IE), Seoul, 2017, pp. 14-19. [2] S. Choi, Z. Jiang, "Comparison of envelope extraction algorithms for cardiac sound signal segmentation", Expert Systems with Applications, vol. 34, no. 2, pp. 1056-1069, 2008. [3] F. Eyben, F. Weninger, F. Gross, B Schuller: “Recent Developments in openSMILE, the Munich Open- Source Multimedia Feature Extractor”, In Proc. ACM Multimedia (MM), Barcelona, Spain, ACM, ISBN 978-1-4503-2404-5, pp. 835-838, October 2013. doi:10.1145/2502081.2502224 [4] B. Schuller, M. Valstar, F. Eyben, G. McKeown, R. Cowie, M. Pantic, “AVEC 2011–The First International Audio/Visual Emotion Challenge”, Affective Computing and Intelligent Interaction pp 415- 424, 2011. [5] F. Shaffer, JP. Ginsberg. An Overview of Heart Rate Variability Metrics and Norms. Front Public Health. 2017;5:258. Published 2017 Sep 28. doi:10.3389/fpubh.2017.00258 [6] F. Wilcoxon, Frank, "Individual comparisons by ranking methods", 1948, Biometrics Bulletin. 1 (6): 80– 83. doi:10.2307/3001968. [7] M. Sahidullah, G. Saha. Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition. Speech Communication, 54(4), pp.543-565, 2012. [8] P.J. Bentley, G. Nordehn, M. Coimbra, S. Mannor, R. Getz, “The PASCAL Classifying Heart Sounds Challenge 2011,” www.peterjbentley.com/heartchallenge/. [9] D. Gari. Clifford et al. “Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016”, Computing in Cardiology Conference (CinC), 2016. M.Gjoreski etal. /TowardEarlyDetectionandMonitoringofChronicHeartFailure 343