Violence Detection from ECG Signals: A Preliminary Study

Ferdinando, Hany and Ye, Liang and Han, Tian and Zhang, Zhu and Sun, Guobing and Huuki, Tuija and Seppänen, Tapio and Alasaarela, Esko (2017) Violence Detection from ECG Signals: A Preliminary Study. [UNSPECIFIED]

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Abstract

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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