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. 2021 Mar 22:2021:6631616.
doi: 10.1155/2021/6631616. eCollection 2021.

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Jin Zhang  1 , Guangjie Yuan  2 , Huan Lu  3 , Guangyuan Liu  1   2   3
Affiliations

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Jin Zhang et al. Comput Intell Neurosci. .

Abstract

The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Experimental paradigm.
Figure 2
Figure 2
Frame diagram of state recognition of ILFS.
Figure 3
Figure 3
Characteristic p value between the ILFS and non-ILFS.
Figure 4
Figure 4
Results of the accuracy of five classifiers for selecting different numbers of features.
Figure 5
Figure 5
Classification performance of the five classifiers without feature selection.
Figure 6
Figure 6
Classification performance after feature selection.
Figure 7
Figure 7
Comparison of the classification accuracy of different classifiers with no feature selection and feature selection.
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