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); https://doi.org/10.13176/11.790
Violence detection from ECG signals : a preliminary study
|Author:||Ferdinando, Hany1,2; Ye, Liang1,3; Han, Tian1,4;|
1University of Oulu, Finland
2Petra Christian University, Indonesia
3Harbin Institute of Technology, China
4Harbin University of Science and Technology, China
|Online Access:||PDF Full Text (PDF, 4.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001101786
Journal of Pattern Recognition Research,
|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.
Journal of pattern recognition research
|Pages:||7 - 18|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
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).
2017 JPRR. All rights reserved. Permissions to make digital or hard copies of all or part of this work for personal or classroom use may be granted by JPRR provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or to republish, requires a fee and/or special permission from JPRR. Available open aceess at: https://doi.org/10.13176/11.790.