# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
#### Background  
The computer task of emotion recognition has been studied over many years and the most used approaches include using face and body images, video recordings, and audio recordings (speech) as inputs. Most recent studies employ machine learning algorithms to perform the recognition task on an annotated dataset. [PERSON] et al. [8] used a multimodal deep learning approach based on convolutional neural networks (CNN), deep belief networks (DBN), and support vector machines (SVM) on audio and video datasets (AFEW) [9] used in the EmotiW Challenge [10]. [PERSON] et al. [11] fed a convolutional DBN (CDBN) with extracted regions of interest (ROI) from their published emoFBVP dataset. [PERSON] et al. [12] combined long short-term memory (LSTM) and 3D convolutional networks (C3D). [PERSON] et al. [13] proposed a novel method based on cascading a local enhanced motion history image (LEMHI) and a CNN-LSTM, comparing results over three different datasets (AFEW, the extended Cohn-Kanade dataset (CK+) [14], and MMI [15]). Other types of signals, such as physiological signals, have also been explored as inputs for classification. [PERSON] et al. [16] recorded and extracted features from EEG and eye-tracking data to feed a support vector machine (SVM) classifier. [PERSON] et al. [17] used a LSTM Recurrent Neural Network to classify emotions from the EEG signal of DEAP dataset [5], obtaining up to 85% accuracy. [PERSON] et al. [18] proposed a comparative analysis between different machine learning and deep learning techniques, also using the DEAP dataset. [PERSON] [19] used blind signal separation for EEG signal reconstruction, allowing the identification of the source generating a given potential. However, recording audio, videos, or EEG for ambulatory or daily life application of emotion recognition is unfeasible in most cases. Thus, a less-intrusive physiological signal measurement is needed. In this way, some researchers have used less intrusive measurements such as electrocardiogram (ECG) and galvanic skin response (GSR) to classify the signals to an elicited emotion using a machine learning or deep learning classifier [4; 20]. Some studies use ad hoc obtained datasets, usually with a small number of signals and volunteers. To overcome this situation, some researchers have made large, publicly available datasets with more volunteers, a mix of different signal measurements, and emotion elicitation approaches, allowing the validation and comparison of different classification algorithms in different studies. Metrics such as accuracy and F1-score were used in the comparison [21]. Some examples are DREAMER [22], HCI-Tagging [6], and AMIGOS [7]. Some recent publications use at least one of these datasets. For example, a probabilistic Bayesian deep learning algorithm has been employed to classify valence in the DREAMER (accuracy, 0.90; F1-score, 0.88) and AMIGOS (accuracy, 0.86; F1-score, 0.83) datasets [23]. HCI-Tagging data is employed in [24] to extract intrinsic mode function features from the  
Figure 1: In [PERSON]’s circumplex model, emotions are distributed on a two-dimensional plane. The x-axis represents valence and the y-axis represents arousal [1]. ECG and feed a K-Nearest Neighbors (KNN) classifier, obtaining an accuracy of 0.558 for arousal and 0.597 for valence classification. In [25], the authors use deep learning to classify ECG signals from the AMIGOS dataset, obtaining 0.76 and 0.68 F1-score for arousal and valence, respectively. In the original publication of AMIGOS [7], manually extracted time and frequency features are used, obtaining mean F1-scores of 0.545 and 0.551, respectively. In [26], mean F1-scores of 0.851 and 0.837 were obtained with a CNN self-supervised approach. [PERSON] et al. [27] employed entropy domain features and an XGboost model for classification, obtaining mean F1-scores between 0.56 and 0.63. Regarding model validation, one of the most used methods in this field is leave k subjects out (LkSO) cross-validation.