###### Abstract  
Emotion recognition research has been conducted using various physiological signals. In this paper, we propose an efficient photoplethysmogram-based method that fuses the deep features extracted by two deep convolutional neural networks and the statistical features selected by [PERSON]'s correlation technique. A photoplethysmogram (PPG) signal can be easily obtained through many devices, and the procedure for recording this signal is simpler than that for other physiological signals. The normal-to-normal (NN) interval values of heart rate variability (HRV) were utilized to extract the time domain features, and the normalized PPG signal was used to acquire the frequency domain features. Then, we selected features that correlated highly with an emotion through [PERSON]'s correlation. These statistical features were fused with deep-learning features extracted from a convolutional neural network (CNN). The PPG signal and the NN interval were used as the inputs of the CNN to extract the features, and the total concatenated features were utilized to classify the valence and the arousal, which are the basic parameters of emotion. The Database for Emotion Analysis using Physiological signals (DEAP) was chosen for the experiment, and the results demonstrated that the proposed method achieved a noticeable performance with a short recognition interval. PPG; emotion recognition; statistical feature; feature fusion; convolutional neural network +
Footnote †: journal: Medical  
models to extract a number of features from these signals [17; 18; 19]. Deep belief network (DBN)-based emotion recognition has been conducted using EEG signals, and convolutional neural networks (CNNs) have been introduced to extract the features of various biosignals [20; 21]. The typical biosignals for emotion recognition are the EEG signals, as these signals are associated with the central nervous system and provide strong emotional features [22; 23; 24; 25; 26]. Recently, advanced deep-learning models have been used to extract EEG features. [PERSON] et al. introduced long short-term memory (LSTM) to classify emotions, and [PERSON] et al. proposed a dynamic graph CNN to learn the intrinsic relationship between different EEG channels [27; 28]. Moreover, [PERSON] et al. used a capsule network (CapsNet) to recognize emotions on the basis of a multiband feature matrix (MFM) [29]. However, EEG analysis has the limitation of being a high-dimensionality problem. Furthermore, the procedure for obtaining an EEG signal is tedious and annoying, because multiple EEG electrodes have to be attached to get the EEG signal. To overcome these limitations and recognize true emotion, in this research, we used a PPG signal. It detects the blood volume changes in the tissue by using a pulse oximeter. A PPG signal is typically extracted from the finger, and many electrical devices have PPG sensor functions. Therefore, a PPG signal can be obtained more easily than other signals. However, the PPG signal has a weakness in that is vulnerable to vibrations, which implies that vibration-resistant measurements are required. Another merit of the PPG signal is that it changes quickly depending on the situation. Accordingly, a number of researchers have used PPG signals for emotion recognition [12; 30; 31; 32; 33]. The feature extraction process is essential for emotion recognition. This is because the emotion is not revealed in the physiological signal itself, and therefore, significant characteristics associated with emotions should be extracted from the signal to detect emotion. There are two ways of extracting features from biosignals. The first one is a hand-crafted feature method that uses a statistical theory such as the mean, standard deviation, peak, and area. To extract statistical features, a physiological signal is preprocessed or is changed as a power spectral density (PSD) with a Fourier transform before the application of statistical techniques. [PERSON] et al.