Brute Force ECG Feature Extraction Applied on Discomfort Detection

  • Guillermo Hidalgo GadeaEmail author
  • Annika Kreuder
  • Carsten Stahlschmidt
  • Sebastian Schnieder
  • Jarek Krajewski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 762)


This paper presents the idea of brute force feature extraction for Electrocardiography (ECG) signals applied to discomfort detection. To build an ECG Discomfort Corpus an experimental discomfort induction was conducted. 50 subjects underwent a 2 h (dis-)comfort condition in separate sessions in randomized order. ECG and subjective discomfort was recorded. 5 min ECG segments were labeled with corresponding subjective discomfort ratings, and 6365 brute force features (65 low-level descriptors, first and second order derivatives, and 47 functionals) and 11 traditional heart rate variability (HRV) parameters were extracted. Random Forest machine learning algorithm outperformed SVM and kNN approaches and achieved the best subject-dependent, 10-fold cross-validation results (\(r=.51\)). With this experiment, we are able to show that (a) brute force ECG feature sets achieved better discomfort detection than traditional HRV based ECG feature set; (b) cepstral and spectral flux based features appear to be the most promising to capture HRV phenomena.


Affective computing ECG HRV Brute force feature extraction Machine learning Low-level descriptors Functionals 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Guillermo Hidalgo Gadea
    • 1
    • 2
    Email author
  • Annika Kreuder
    • 1
  • Carsten Stahlschmidt
    • 1
  • Sebastian Schnieder
    • 1
    • 2
    • 3
  • Jarek Krajewski
    • 1
    • 2
    • 4
  1. 1.Institute of Experimental PsychophysiologyDuesseldorfGermany
  2. 2.Institute of Safety TechnologyUniversity of WuppertalWuppertalGermany
  3. 3.Engineering PsychologyHMKW BerlinBerlinGermany
  4. 4.Human-Technology-InteractionRhenish University of Applied Science CologneCologneGermany

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