University of Oulu

Ferdinando, Hany; Ye, Liang; Han, Tian; Zhang, Zhu; Sun, Guobing; Huuki, Tuija; Seppänen, Tapio; Alasaarela, Esko (2017) Violence detection from ECG signals : a preliminary study. Journal of Pattern Recognition and Research Vol 12, No 1 (2017);

Violence detection from ECG signals : a preliminary study

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Author: Ferdinando, Hany1,2; Ye, Liang1,3; Han, Tian1,4;
Organizations: 1University of Oulu, Finland
2Petra Christian University, Indonesia
3Harbin Institute of Technology, China
4Harbin University of Science and Technology, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.3 MB)
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Language: English
Published: Journal of Pattern Recognition Research, 2017
Publish Date: 2020-01-10


This research studied violence detection from less than 6-second ECG signals. Features were calculated based on the Bivariate Empirical Mode Decomposition (BEMD) and the Recurrence Quantification Analysis (RQA) applied to ECG signals from violence simulation in a primary school, involving 12 pupils from two grades. The feature sets were fed to a kNN classifier and tested using 10-fold cross validation and leave-one-subject-out (LOSO) validation in subject-dependent and subject-independent training models respectively. Features from BEMD outperformed the ones from RQA in both 10-fold cross validation, i.e. 88% vs. 73% (2nd grade pupils) and 87% vs. 81% (5th grade pupils), and LOSO validation, i.e. 77% vs. 75% (2nd grade pupils) and 80% vs. 76% (5th grade pupils), but have larger variation than the ones from RQA in both validations. Average performances for subject-specific system in 10-fold cross validation were 100% vs. 93% (2nd grade pupils) and 100% vs. 97% (5th grade pupils) for features from the BEMD and the RQA respectively. The results indicate that ECG signals as short as 6 seconds can be used successfully to detect violent events using subject-specific classifiers.

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Series: Journal of pattern recognition research
ISSN: 1558-884X
ISSN-E: 1558-884X
ISSN-L: 1558-884X
Volume: 12
Issue: 1
Pages: 7 - 18
DOI: 10.13176/11.790
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: The research is partly funded by the Directorate General of Higher Education, Indonesia (2142/E4.4/K/2013); the Finnish Cultural Foundation, North Ostrobothnia Regional Fund; the National Natural Science Foundation of China (61602127); Reserve Talents of Universities Overseas Research Program of Heilongjiang (2013); Harbin Science and Technology Bureau, China (2013RFQXJ171).
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